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Title

エンタープライズメッセージ管理の指向的開発：電子メールトピック推論の視覚的注意（電子メールのAttLDA）およびECSとERPの統合（SuccERP）に関する研究Author(s)LIN, Yung Yu

CitationIssue Date

2021‑03

Type

Thesis or Dissertation

Text version

ETD

URL

http://hdl.handle.net/10119/17467

RightsDescription

Supervisor:永井

由佳里, 先端科学技術研究科, 博士

Japan Advanced Institute of Science and Technology

Oriented Development of Enterprise Message Management:
Study on Visual Attention of Email Topic Inference (AttLDA for
Email) and Integration of ECS and ERP (SuccERP)

Yung-Yu Lin
Japan Advanced Institute of Science and Technology

Doctoral Dissertation

Oriented Development of Enterprise Message Management:
Study on Visual Attention of Email Topic Inference (AttLDA for
Email) and Integration of ECS and ERP (SuccERP)
エンタープライズメッセージ管理の指向的開発：電子メール
トピック推論の視覚的注意（電子メールの AttLDA）および ECSと ERP の統合（SuccERP）に関する研究

Yung-Yu Lin

Supervisor: Yukari Nagai
Graduate School of Advanced Science and Technology
Japan Advanced Institute of Science and Technology
Knowledge Science
March, 2021

ABSTRACTOur dissertation is mainly focusing on several topics for improving collaborationand communication in an enterprise. Come with considering two features ofcollaboration, unstructured collaboration (information collaboration) and structured
collaboration (process collaboration); we primarily focus on two representative
applications: email and Enterprise Resource Planning (ERP) System.
In terms of an enterprise, most of the current research result struggles to achieve
specific and practical goals by proposed theoretical findings in the ERP domain. To
allow the managers to get a fuller picture of all the messages generated from an ERP
system with the Enterprise Collaboration System (ECS) and improve collaboration andcommunication, we propose a complete method to develop an artifact-SuccERP basedon the Design Science approach to carry out the integration. Based on exploring
multiple ERP systems, we summarize our tasks into three aspects before implementing
the integrations: authentication, data initialization, and specific procedures
implementation; we also explain how the data-processing and integrations between the
ERP and ECS.
In our perspective, we can distinguish our contribution of the proposed SuccERP
into two parts; 1) We present a complete demonstration of how to get the architectureand database schema of an existing ERP system and address the internal and external
hosting issues. 2) According to a series of literature reviews, we implement the
integration based on the critical success factors and existing issues discussed in theprevious studies. In other words, we attempt to fill up the gap in communication andcollaboration capabilities by enhancing the ERP and ECS systems' integrations.
Meanwhile, we fulfill the data-processing and data-sharing from an ERP system to theexternal resources. Given the context of the increasing demands of custom ERP, it isreasonable to provide elaborate research as a guideline to those enterprises that plan to
upgrade and enhance their ERP systems. Furthermore, based on our results, follow-up
research can explore the implementation with other external resources for improving
different issues.
Next, the definition of information collaboration is employees applying IT tools
to communicate and request assistance (answer); email is the most standard
documentation tool for communication. Although existing studies use the topic modelito support users for classifying emails, they disregard that humans are not like amachine that can focus on all the words in an email to determine the distribution ofemail topics. The Latent Dirichlet Allocation (LDA) model forms a basis for inferring
topics; our work aims to discover how each word's visual attention influences the topic
inference and estimates attention to a word according to its location features.
By reviewing the visual-spatial research and the state-of-the-art visual attention
models, we select the Bayesian Models to estimate attention and proposing a novel
model-Attention orientation Latent Dirichlet Allocation model (AttLDA). In AttLDA,
each email can regard as encoded into a two-dimensional space, taking the line length(the characters per line in an email) and window size (the number of lines in an email)into account. After that, draw the optimal display size as a visual space and assigningeach word's location. Besides location, attention estimation also considers the Term
Frequency and Inverse Document Frequency (TFIDF) and inferred topics for each
iteration. Our aim is as follows; the readers can not completely capture all the hidden
topics behind each word in an email, especially the context in the forwarded message.
Moreover, our result shows each email's topic distribution and including the distributionof related words' attention in each topic by considering the visual attention as thesignificance of an email's topic distribution. In our experiment, we consider the public
Enron email corpus as a dataset and apply the Perplexity metrics to measure theperformance of AttLDA. AttLDA is outperforming the previous research on the
perplexity evaluation.
Advanced technology has made the communication distance between people
shorter than ever before and accumulates the number of messages quicker and quicker.
People might quickly out of control for managing their messages owing to theirnegligence. Our research proposes the SuccERP, which builds a platform to manage
ERP and ECS messages through definite guidelines to keep communication efficiency.
On the other hand, we also proposed the AttLDA to effectively extract the email topics
to improve email message management performance. The research findings can be
regarded as a strategy for settling further tasks relating to collaboration in an enterprise.
Keywords: Latent Dirichlet Allocation (LDA), Visual Attention, Email Management,
Enterprise Resource Planning (ERP), Enterprise Collaboration Systems (ECS), Design
Science (DS).
ii

ACKNOWLEDGMENTS
Foremost, I would like to express my sincere gratitude to my advisor Prof. Yukari
Nagai for the continuous support and guide of my Ph.D. study and research, for her
patience, motivation, enthusiasm, and immense knowledge. Her guidance helped me inall the time of research and writing of this thesis. I could not have imagined having abetter advisor and mentor for my Ph.D. study.
Besides my advisor, I would like to thank my mentor and supporter Prof. TzuHang Chiang for his encouragement, insightful comments, and providing explicit
direction at a time when I may have gotten my directions wrong. I am also grateful toProf. Tsutomu Fujinami, who taught us the cognition science for developing a researchmore focus on human demands. To inspire our imagination and innovation, we often
have a group discussions with research students from different labs and areas, it opens
a big door to meet the requirements and questions from different viewpoints. Also, it is
my honor to let Prof. Tsutomu Fujinami to be my advisor for the Minor Research Project,
he provides me timely advice and many valuable suggestions while I do the internshipfor my minor research.
Further, I wish to extend my gratitude to my lab mate Mr. Zhao Ching, who is theexcellent Ph.D. candidate in the Nagai lab. During the COVID-19 pandemic, all of thelab discussion and meeting is considering the remote way, I can’t imagine what would
have happened without his kindly help.
Finally, my sincere thanks also extend to my family—especially to my mother for
her long-lasting encouragement and eternal support in my study and life—and I would
like to thank my husband and son for their understanding and recognition. I thank myother family members for their support throughout my doctoral study and my life ingeneral as well.
Thank you for all your encouragement and support!

Yung-Yu Liniii

TABLE OF CONTENTS
ABSTRACT.................................................................................................................... i
ACKNOWLEDGMENTS ........................................................................................... iii
TABLE OF CONTENTS .............................................................................................. iv
LIST OF FIGURES ...................................................................................................... vi
LIST OF TABLES ..................................................................................................... viii
CHAPTER

1

General Introduction ...................................................................... 1

1.1

Motivation for focusing on the email domain........................................ 2

1.2

Motivation for focusing on ERP integration domain ............................. 4

1.3

Research Aims ....................................................................................... 8

1.4

Research Objectives ............................................................................. 11

1.5

Structure of this Dissertation ............................................................... 12

CHAPTER

2

Introduction and literature reviews of Email Topic Identification
14

2.1

Literature reviews for implementing the AttLDA model ..................... 16
2.1.1 The existing works on spam email detection .............................. 17
2.1.2 The existing works on ham email research ................................. 18

2.2

Literature reviews for enhancing LDA model ..................................... 20
2.2.1 The extension of LDA on term weighting function .................... 20
2.2.2 The fundamental for building visual attention ............................ 22

CHAPTER

3

Methodology for the proposed AttLDA model ............................ 25

3.1

Data collection of Enron Email Corpus ............................................... 27

3.2

Latent Dirichlet Allocation (LDA)....................................................... 31

3.3

Attention integration based Latent Dirichlet Allocation (AttLDA) ..... 33

3.4

Inference by Collapsed Gibbs Sampling on AttLDA .......................... 44

CHAPTER

4

Experimental results of AttLDA .................................................. 48

4.1

Experimental Data set .......................................................................... 48

4.2

Experimental setup............................................................................... 48

4.3

Evaluation metrics and comparison ..................................................... 52

4.4

The discussion of visual attention and keywords ................................ 54

CHAPTERSuccERP

5

Introduction and literature reviews for implementing the proposed
57
iv

5.1

Literature reviews of Enterprise Collaboration Systems (ECS) .......... 59

5.2

Enterprise Resource Planning (ERP) ................................................... 61

5.3

Design Science (DS) ............................................................................ 64

CHAPTER

6

Design and the Development of SuccERP................................... 65

6.1

Problem Identification and Motivation ................................................ 65

6.2

Define the objectives for a solution ..................................................... 67

6.3

Design and Development: The solution for the hosting issues ............ 70

6.4

Design and Development: Collect the outline and schema of ERP

Database
6.5
CHAPTER

71
Design and Development: The integration between ECS and ERP. .... 76
7

Results, Demonstration and Evaluation of SuccERP .................. 84

7.1

The Demonstration of proposed SuccERP .......................................... 84

7.2

The Evaluation of proposed SuccERP ................................................. 88

CHAPTER

8

Conclusion, Implication, Limitation and Future Work ................ 92

8.1

Implication ........................................................................................... 97

8.2

Limitation ............................................................................................. 97

8.3

Future Work ......................................................................................... 98

Bibliography ................................................................................................................ 99v

LIST OF FIGURES
Figure 1.1 Research aims from the state of existing studies. ......................................... 8
Figure 2.1 The conception for the attention allocation when reading an email. .......... 15
Figure 3.1 The collection process of Email data.......................................................... 28
Figure 3.2 The snapshot for an original Enron email (text file). ................................. 29
Figure 3.3 The snapshot for the Enron email inserted into database. .......................... 30
Figure 3.4 The snapshot for the Enron email inserted into database. .......................... 30
Figure 3.5 The snapshot for the Enron email inserted into database. .......................... 31
Figure 3.6 The graphical model of LDA. .................................................................... 31
Figure 3.7 Graphical representation of proposed AttLDA model. ............................... 33
Figure 3.8 Algorithm 1: Preprocessing for assigning location 𝑙𝑑𝑛 to each word 𝑤𝑑𝑛.
...................................................................................................................................... 39
Figure 3.9 Algorithm 2: Assigning target and location to each word 𝑤𝑑𝑛. ................. 40
Figure 3.10 The workflow of AttLDA model. ............................................................. 46
Figure 4.1 The result of the coherence score with the number of topics from 2 to 26.49
Figure 4.2 The result of coherence score with the number of topics from 28 to 52. ... 50
Figure 4.3 The result of coherence score with the number of topics from 54 to 76. ... 50
Figure 4.4 The result of coherence score with the number of topics from 78 to 100. . 51
Figure 4.5 The result of coherence score with the number of topics from 102 to 124.
...................................................................................................................................... 51
Figure 4.6 The result of coherence score with the number of topics from 126 to 148.
...................................................................................................................................... 52
Figure 4.7 the attention value of the top 10 words from all the topics. ....................... 55
Figure 6.1 the architecture of proposed artifact-SuccERP. .......................................... 68
Figure 6.2 the architecture for internal hosting of SuccERP. ....................................... 70
Figure 6.3 the swim lane diagram of Reverse Engineering System (RES). ................ 72
Figure 6.4 the schema of authentication in an ERP system. ........................................ 73
Figure 6.5 The schema of authentication in an ERP system. ....................................... 74
Figure 6.6 Use case diagram and the illustration of SuccERP. .................................... 77
Figure 6.7 Pseudo-Code: synchronize data while accessing a message in ECS. ......... 78
Figure 6.8 Pseudo-Code: sheet creation....................................................................... 80
Figure 7.1 the sequence diagram for the case of sheet creation................................... 84vi

Figure 7.2 snapshot of SuccERP for providing options to decide which type of
connection prefer in the current environment. ............................................................. 85
Figure 7.3 snapshots including company binding, user binding, and specific procedures
selection. ...................................................................................................................... 86
Figure 7.4 snapshot of the user interface for the bill message creation. ...................... 87
Figure 7.5 snapshot of created sheet in an ERP system. .............................................. 87vii

LIST OF TABLES
Table 3-1 the notation of this work. ............................................................................. 26
Table 4-1 the summarized results of perplexity measurement..................................... 54
Table 4-2 the top 10 probability words in four of eight topics. ................................... 54
Table 4-3 the top 10 probability words in four of eight topics. ................................... 55
Table 5-1 the arranged suggestion for the post-implementation stage of ERP. ........... 62
Table 6-1 user requirements definition. ....................................................................... 65
Table 6-2 system requirements specification. .............................................................. 67
Table 6-3 the relative tables and descriptions in initialize data step. ........................... 74
Table 6-4 the relative tables and descriptions in the step of four specific ERP procedures.
...................................................................................................................................... 76
Table 7-1 the notation for the evaluation of SuccERP. ................................................ 88
Table 7-2 the metrics of functions in the back end part of SuccERP. .......................... 89
Table 7-3 the metrics of functions in the front end part of SuccERP. .......................... 90viii

CHAPTER 1 General Introduction
With more and more employees working in complex and knowledge-intensive
environments, the emphasis on demand for collaboration is increasing [1]. To this end,
Prakash et al. [2] indicated that IEEE had proposed an explicit definition ofcollaboration, which implies the capability of two otherwise more systems to share anduse the knowledge, especially emphasizes the information flow aspect.
In this context, collaborative application development is gaining attention.
Enterprise Collaboration System (ECS) is a system designed for the enterprise side to
implement collaboration conception, which has been recognized as a vital enabler ofthe modern digital workplace in the current environment [3]. Prakash et al. [2]emphasized that collaborative systems address administrative work by facilitating thesharing and diffusion of information; however, two fundamental features need to becustomized and paid attention to depending on the organization's goals. These two
features are unstructured collaboration and structured collaboration.
Unstructured collaboration, also known as information collaboration, is used tofind answers to unknown questions by utilizing IT tools, such as emails. Instead,structured collaboration is also known as process collaboration, allowing businessprocesses to be shared by sharing common information, structured, written rules, andset workflows. Specifically, for the collaboration and business process, a portion of theresearchers have identified the integration between Enterprise Resource Planning (ERP)and external resources as one of the most critical fields. They also claim that system
integration is crucial in digitizing business processes and creating business value for
future IT value [4]–[7].
Hence, regarding the two notable features of the collaboration mentioned above,
for the part of the unstructured collaboration (information collaboration), we consider
email as the target for exploring how to improve the integration process in collaborative
systems. On the other hand, for structured collaboration (process collaboration), theissues related to improving an enterprise's business process through system integration
have attracted our attention.

11.1 Motivation for focusing on the email domain
According to the Email Statistics Report [8], they indicate a clear sign about how
the issues of huge amount and complexity from email. The amount of email will exceed
93 billion in 2019 and continue to grow at a rate of over 4 percent per year. While email
usage from ubiquitous to regular and grew into our life, we are not aware of how
important it is in daily life. Hence, managing and maintaining those emails in our daily
life has become a primary concern subject.
Whittaker and Sidner [9] present that people consider email as a tool forcommunication application in the beginning when it comes to email. However, people
apply the email for task management and personal archiving that were not initiallydesigned. Hence, the problem of email overload becomes a well-known issue in thisdomain. Especially in an organization, users often with inboxes containing hundreds ofemail messages, including tasks, partially read documents, and conversational threads.
The story must proceed to the result where important messages get overlooked or lostin archives. Therefore, regarding email as an asynchronous communication application,
the critical requirements for improving the management of communication are:1.
2.

The management of email threading to support context and conversational historyof email.
To track the status of a conversation from the email collection.

3.

Find out the exact subjects (topics) from the messy email threads.

Moreover, some scholars consider task management to the email to apply theconversational threading and semantic clustering techniques to reduce the amount ofinbox. In short, keeping important things (email) “in-view” [9].
A subsequent study considered the utility of various techniques for managing
email and redefined email overload after a decade. Whittaker and Sidner [9] regard theprimary reason to meet the email overload as the reliance on the email client for
personal achievement and tasks beyond communication. However, Dabbish and Kraut[10] define email overload as users' perceptions that their email use has gotten out ofcontrol (p. 431). They further revealed that white-collar workers would attempt tomoderate the stress from high email volume in the inbox via checking email boxes
frequently for reducing email stress. As email is fast becoming the primary means of2

communication for sharing information in an organization, it is necessary to propose amore passive solution and organize email to impact this domain significantly [10].
Afterward, Fisher et al. [11] review Whittaker and Sidner's work [9] to estimate
the change of email overload circumstances in the previous decade. The increasing
familiarity with management techniques would reduce the number of emails keeping in
the inbox. Interestingly, the likelihood of people adopting or keeping an email
management strategy is not increasing over time. Even the archiving behavior is moregeneral than before, and there is no evidence to support the relationship with the
reduction in the inboxes size. Fisher et al. [11] further show that email overload willcontinue growing and need more research to put effort and attention into this domain
to determine the most effective email management method.
Jerejian et al. [12] present the findings that several studies investigate various
email management ways to reduce employees' stress from organizing emails such as
filing, filtering, periodic checking, or constant monitoring. However, the result offindings has been mixed as to the success of email management strategies. Individual
differences may explain it among participants in the experiment. However, in part, weshould also consider the necessity of email management that needs to look for advanced
techniques to improve the efficiency of organizing emails.
Skyrme [13] gets down to practicalities in a set of tools to introduce how you getpeople to share their knowledge and the best start with knowledge management.
Regarding email as a tool, Skyrme further points out that the use of email in businessmust be more focused on effective communication improvement and suggested that it
would be better to restrict an email's content to only one topic. Like the above
suggestion, we have mixed feelings about it. On the one hand, we agree that the
restriction on topics of an email. On the other hand, it is relatively ideal for an email
with only one topic, but there are mixed topics in an email in most cases.
Our intention in the email domain is to show the way how to effectively improve
communication and collaboration activities in an enterprise by identifying email topics.
The intention is first motivated by the issues of huge amount and complexity from email;and then how, under the reliance on the email client for personal achievement and tasks
beyond communication, it has caused another issue-email overload. Researchers havebegun to examine white-collar workers' behavior and analyze management approaches'
effectiveness to overcome email overload. However, the result of findings has been
mixed as to the success of email management strategies. They suggest looking for3

advanced techniques to improve the efficiency of organizing emails. In this way,considering collaborative work and email overload issues would be a worthwhile
research question to help employees effectively identify topics from email and further
improve communication in an organization.

1.2 Motivation for focusing on ERP integration domain
Subsequently, we address another feature of collaboration – structured
collaboration (process collaboration). For the discussion of ERP and beyond,Langenwalter [14] has created a term, Total Enterprise Integration (TEI), which is used
to describe the process of integrating all the information and actions between the
manufacture and supply chain sides. TEI aims to create a strategic advantage forcrossing the entire manufacture and out through the customers and suppliers. In short,
it is in communication to let every member know the whole process of a new customer
order from received to complete, such as the current status of supplier shipment or the
discussion on a machine for the unscheduled maintenance. It makes the workflow
allocate the organization's responsibility to the most appropriate decision-makers while
maintaining proper budgetary. TEI is an intelligent use of an enterprise, while internalcosts continue to rise, and competitive pressures threaten manufacturers' ability tosurvive by decreasing prices. The solution is reducing costs by eliminating ineffective
communication, collaboration, and coordination throughout the manufacturing
company, suppliers, and customers.
Zheng et al. [15] present the integration between Supply Chain Management(SCM) and ERP Systems, aiming to generate a single and seamless system with higher
efficiency and productivity. People conduct the business process related to suppliers
and manufacture within the same system rather than two independent ones. Thus,
integration regards as a complete information-sharing platform for improving theperformance of respond from customers to the supply chain. The result shows thecomparison between SCM and ERP system as an evaluation which comprises
application, functionality, relationship, and explore their potential integration. Samiei
and Ehsan [16] present a review study on the relation between knowledge management(KM) and enterprise resource planning (ERP) after the implementation stage. They also
discuss the possible synergy between KM and ERP and the various integrations that
could strengthen knowledge generation and information sharing in organizations.
4

TEI describes building a strategic advantage by integrating all the organization's
information to eliminate ineffective communication, collaboration, and coordination.
According to both Zheng et al. [15] and Samiei and Ehsan [16] findings, it is morestraightforward to understand the purpose and goal of achieving system integration,
aiming to generate a single and seamless system to allow business processes within thesame system rather than two independent ones. The integration is a further step towards
sharing information within an enterprise and organization. Simultaneously, as we
previously mentioned, structured collaboration allows business processes to be sharedby sharing common information, structured, written rules, and a set of workflows. They
claim that system integration is crucial in digitizing business processes and creating
business value. Referring to these relevant literature studies, we have a better
understanding and confidence that improving business processes through ERP system
integration can effectively enhance collaborative capability within an organization orenterprise.
With the emergency of global value chains and competitive business environment,
we further elaborate on the ERP system. Tarn et al. [17] indicated that ERP systems
have been regarding as the fundamental element of an enterprise. However, today
enterprises are no longer concerned with the issue of whether they need an ERP systembut how to build an effective ERP system. Admittedly, they believed that enterprises
should further extend their ERP systems to integrate inter-company and collaborative
operations across the entire industry processes rather than inter-department, inter-office,and inter-site integration within a single organization. In short, from existing ERP
systems to shift toward Extended ERP (EERP) system. Meanwhile, the market anddemands for ERP post-implementation upgrades and services, or called the second
wave of ERP, are continually growing, and only a relatively small amount of study iscurrently focused on this field [5], [17], [18]. Accordingly, as things are, we summarize
several keywords for the motivation on the ERP domain, including structured
collaborative, business process, integration, and ERP post-implementation.
From the structured collaboration feature mentioned above, we follow the businessprocess, ERP system integration, to which life cycle stage is an appropriate one for ERP
system integration and the noteworthy issues. First, an ERP project's life cycle consistsof three stages: ERP adoption, implementation, and post-implementation [19]. Second,
Kosalge and Motwani [7] argue that the implementation stage's life cycle is not the end
5

in the ERP implementation; timely adding new capabilities, modifications, updates, and
new improvement should be applied in ERP systems and the changes in processes.
Third, some of the studies, however, point out that existing ERP literature tends to focus
on the topics related to the implementation of the Critical Success Factors (CSFs) and
implementation methodologies [21]–[23]. The scholars further indicate that only very
few studies focus on other aspects of ERP applications [24]–[27], based on referring
the current popular ERP research focuses on identifying the CSFs to ERP
implementation stage as the factor research [20].
These literature results and suggestions open the door to studies that achieving the
integration between ERP system and external resource for improving the collaboration
by integrating the business process into a single and seamless way. Our intention starts
with the feature of structured collaboration; subsequently, considering a portion ofworks claims that ERP integration with external systems is a required field and cancreate business process value. Hence, we explore different ERP system integration
studies, including integrating SCM and ERP [15] and integrating KM and ERP [16].
Based on that, we answer two questions first below:
1. How to improve the collaboration within an enterprise: ERP system integration.
2. What is the purpose to implement the ERP system integration with external
resources: Generate a single and seamless system to enable business processes
within the same system rather than two independent ones.
Finally, from the ERP implementation life cycle, we further ensure the postimplementation stage is an appropriate one to implement the ERP system integration.
Besides, we found that merely exploring CSFs in the implementation and postimplementation stage is necessary but not enough.
Hence, recent research convinces us that aiming to build the integration between
ERP and ECS to improve collaboration and communication that such an approach can
produce improvements for the business. Moreover, under unavoidable circumstances,each independent system is hard to share their local information and data with others;
thus, the issues of Information Island [28], [29], poor communication, collaboration,and coordination [14] receiving increasing attention for exploring the possibility ofintegration with two or more systems. According to that, we believe there is a gap inproviding a practical solution and guidelines to let the entrepreneur achieve theirdemands as far as we know. It makes them share the data from the ERP system side to6the external resources to fix the issues, including Information Island, poor
communication, collaboration, and coordination.
We make a short conclusion to sum up this chapter; chapter one describes thisdissertation's general content. Go through a series of literature reviews to establish theresearch background for interpreting aims, including:1.

Why are we concerned about improving email management, and how does thisrelate to enterprise collaboration?

2.

Why do we attempt to create the artifact to achieve the integration between an ERP
and ECS?
The above two questions are the primary research aims we will focus on andimplement in the following section. First, people consider applying email as a tool forcommunication application. We explore how to improve email management
performance based on Natural Language Processing (NLP) techniques. On the other
hand, the ERP system becomes fundamental to an enterprise; besides the theoreticalresult, we expect to provide guidelines and steps to integrate with external resources.
Before outlining the aims in detail, we present the motivation and purpose of this study.
Moreover, we list out the tasks we expect to cover through this research. Finally, we
will briefly review each chapter at the end of this chapter.

7

1.3 Research Aims

Figure 1.1 Research aims from the state of existing studies.
We discuss the state of the current study on both email and ERP, showing ourwork’s relevance to a broader problem. We draw out this illustration to describe our8research in Figure 1.1, which aims to review studies beginning from the collaboration
demands, collaboration definition, and two features of collaboration until the email and
ERP domain. We expect to carry out the top layer (Layer 4 in the email domain andLayer 3 in the ERP domain) in our research. In the below description, we interpret thelayer one by one to show how we construct our research aims and purposes.
About the email domain:
1.

2.

3.

Layer 1: This is the fundamental stage for the exploration study, while many issues
come out by the inconvenience of manual actions. The researchers begin to be
involved in this domain and exploring the key factors that cause theseinconveniences.
Layer 2: The exploration study differs by issues from layer one that including:(1) Review the definition of term or conception to describe the current issues of
email management.
(2) To inspect the influence of current email management issues and present thefindings from questionnaires or interviews.
(3) Highlight advanced solutions as a suggestion to the current issues of email
management based on experimental results.
Layer 3: In addition to the discussion on management, some studies began to focus
on the data of the email, including the subject, content, recipients, and otherinformation to go through the data preprocessing, features extraction, and analysis
to achieve specific research purposes such as spam and ham email classification,email folder classification, and email categorization. Although their studies'
purpose is not exclusively on improving communication, the findings and results
are meaningful for communication improvement.

4.

Layer 4: For building an application that can serve a social purpose, we need totake more factors into accounts, such as attention, cognitive science, and memory
aspects, with NLP to achieve our aims. Our idea is while we try to figure out the
topic distribution of an email, we need to consider the limitation of attention; inour opinion, people will allocate their attention to those important words orsentences they think of in an email document. To a certain extent, the following
study supports our viewpoint, Dehaene et al. [30] indicated that visual attention isa prerequisite for consciously seeing the information. In general, people
consciously shift their attention to perceive those events. However, only a few
studies considering what they need to serve are humans, not machines.
9About to the ERP domain:1.

2.

Layer 1: Each system likes an isolated island, neither unable to share the data andinformation nor to implement the communication and coordination in an
organization. However, these issues are not given much attention at this layer since
many studies are focused on choosing an appropriate ERP system for theorganization and the CSFs of the ERP system.
Layer 2: The researchers noticed the problems arising from Layer one and began
to discuss the possibility of interaction between different systems based on eachsystem's characteristics. To a certain extent, the findings and suggestions of CSFs
could form a research basis for further study. For example, Zheng et al. [15]present the integration between SCM and ERP systems; the findings show that thecomparison between SCM and ERP system as an evaluation comprises anapplication, functionality, relationship, and explore additional potential integration.
However, merely exploring the potential for integration is not enough to satisfy
entrepreneurs' and developers' demands. Meanwhile, some studies explore system
integration for different industries. Lancharoen and Suksawang [31] discuss theinformation system integration in a hospital, and the goal of the integration is toimprove the capabilities of communication in a health care organization. The
findings demonstrated that system integration could have a significant impact onservice efficiency and physician decision-making.

3.

Layer 3: For constructing an application that can integrate with the ERP systemand other external resources and systems, we focus on showing which necessary
steps to be taken for achieving the purpose of interaction. Therefore, the findings
can meet business owners' or developers' actual needs and enable data andinformation sharing with multiple legacy systems. At the same time, a company
adopts a new system in the future. The study in this layer belongs to the postimplementation study of ERP system, considering the suggestion from Amid et al.
[32] that current research has focused on software selection, implementing process,and CSFs rather than the success, actual benefits, and performance improvementsof the ERP post-implementation stage. Hence, proposing a practical and effective
way to improve ERP systems' performance is a primary research direction in thislayer.

10

1.4 Research Objectives
From the perspective of enterprise collaboration, we focus on two collaboration
features: structured collaboration and unstructured collaboration. The overall purposeof this study summarized into four objectives as listed below.
(1) Identify the state-of-the-art model based on the NLP techniques, and discuss
the possibility of inferring topic distribution by applying one of the models.
The inferred topic distribution can also consider as a feature for further tasks
such as email classification, email categorization, and email thread clustering.
(2) Design a topic model based on the NLP techniques and integrate with visual
attention and cognitive science to serve the human demands on email
management in an enterprise.
(3) Go through the complete steps and rigorous methods to build and proposinga system as an artifact to integrate an ERP system with other external
resources and systems.
(4) Exploring multiple ERP systems to summarize the differences and presenting
practical cases to prove the proposed artifact's functions can working well.
Also, considering evaluation metrics to ensure the artifact is easy to design,
implement, and maintain.

11

1.5 Structure of this Dissertation
1.

Chapter 1: Introduce an overview of this dissertation that we concentrate on two
specific domains, comprising email and ERP. Besides, it also includes motivation,
research aims, objectives, and structure. This chapter briefly explains the problems,
goals, and structure of this dissertation.

2.

Chapter 2: Review the studies at the management level of email and explain our
motivation based on the literature. Subsequently, from the ham and spam email
research works, we highlight the LDA model's utilization and application. Further,some research begins to apply the term weighting function to improve theperformance while applying the LDA model to achieve the topic inference. In theend, we consider visual attention as a kind of term weighting function and review
the necessary literature for building and implementing visual attention byproposing the AttLDA model.

3.

Chapter 3: After deciding the LDA model as our basis, we recognize the attention
as a weighting function to form the AttLDA model during topic inference.
According to the literature reviews of visual attention, we have ensured ourattention estimation implementation follows the theoretical approach.

4.

Chapter 4: By considering the open data set, we demonstrate how the AttLDA
works and applies the heat map to show the value of attention on each topic's words.
Before showing the visual attention distribution and topic distribution, we present
how we determine the number of topics from the Coherence Score results. Further,
applying and demonstrating the perplexity measurement as the evaluation metrics
for further comparison.

5.

Chapter 5: We are first introducing the life cycle of ERP implementation and thenexplain why we focus on the post-implementation stage. We also introduce the
Design Science (DS) to create an artifact and provide complete guidelines forconstructing the utility application. Subsequently, we review the highlights andissues of ECS that ECS supports collaborative work. People have examined it as
an enabler of the modern digital workplace for longitudinal work; however,ineffective ECS content is a serious issue. Hence, we also review the suggestionand critical idea of ERP in the post-implementation stage and the DS approach12

from the previous works to consider building research to overcome the issues weencountered.
6.

Chapter 6: We refer to the six activities and seven guidelines from previous
research suggestions of DS, including problem identification and motivation,
define the objectives for a solution, design and development, and thedemonstration and evaluation.

Not only concentrates on the steps of DS, as wellas considering the Software Engineering into part of activities to improve thequality and more explicit for building an information system as an artifact.

Later,

we collect the post-implementation issues of an ERP system and provide thepractical steps in this chapter as a solution. Moreover, we consider multiple ERP
systems to summarize the differences between them and prove our solution canwork well on different ERP systems.
7.

Chapter 7: After a series of DS activities in the previous chapter, we focus on thefindings of ERP integrations and show the metrics to ensure our proposed artifactSuccERP is easy to design and maintain, which is the part of the demonstrationand evaluation.

8.

Chapter 8: Discusses findings from all analyses and highlights originalcontributions to the enterprise on the Email and ERP domain, including theoreticaland practical implications. Limitations and recommendations for future studies,
and we also consider the inspiration for the subsequent studies.

13

CHAPTER 2 Introduction and literature reviews of Email
Topic Identification
Email is the most popular and standard tool for communication over the business.
By employing NLP techniques, Email text mining is becoming a popular research topic[33]–[36]. From the study of trends review presented by Mujtaba et al. [37] that wehave a basic understanding of email research, nearly 65% of the email studies focus on
exploring the spam and phishing email classification, about 20% concentrate on themulti-folder categorization and email clustering. In the rest, only limited studies
consider how to overcome the issues of losing control and management, no matter ofindividual or organization.
Recently, Latent Dirichlet Allocation (LDA) has received increasing attention
from researchers in the email research domain, in which applying the topic model toautomatically clustering email into each group to improve the efficiency of email
management. They applied it to extract the topic distribution as features for achievingsome objectives, such as multi-folder categorization, spam detection, and email thread
identification. For example, McCallum et al. [33] explore the relationship and roles
between authors and recipients in an email; they regard the LDA model with different
roles to identify positively related topics and keywords. Sharaff and Nagwani [36]propose a two-stage clustering approach, including the LDA model and the Nonnegative Matrix Factorization (NMF) to automatically identify the email thread.
However, multiple mechanisms equip our brain that grants us to filter out irrelevant
information to prioritize relevant information, referred to as “attention” generally [38].
Differ from previous works, this paper intends to explore a new problem: We
consider the visual-attention allocation while applying the LDA model to inference thelatent topics from an email document. Our work aims to propose a novel modelAttention orientation Latent Dirichlet Allocation (AttLDA). Each email is a twodimensional space to estimate each word's attention value for inferring the latent topics
according to state-of-the-art visual attention models. Each word's attention estimation
is mainly determined by three factors: location, inferred topics, and the Term Frequency
and Inverse Document Frequency (TFIDF). Based on the relevant literature on visual
attention, our research makes the topic inference closer to reality. We identify the14attention allocation has the nature of a theoretical basis with numerous scientificexaminations that have investigated that visual attention is a prerequisite for conscious
retrieving information. Simultaneously, people in general only consciously perceive
those events, onto which they direct their attention at a given time [30].

Figure 2.1 The conception for the attention allocation when reading an email.
In Figure 2.1, we describe the brief conception of the proposed AttLDA model.
Further, the red box includes the header and actual email content, and the purple box is
the quotation content. Here we apply gradient color to indicate how we assumed thatthe ideal attention allocation works while reading an email for topic inference. Our
motivation comes from an email that brings a large amount of information, but people
read only part of it practically. The preliminary work also supports this viewpoint; they
indicate the header, signature, and quotations (such as a forwarded message or original
message) are typical email noises [39]. As illustrated in Figure 2.1, from the line 1 to 5
are a header, line 10 is a signature, and a quotation lies from line 11 to line 34; only
lines from 7 to 8 are actual email content. As far as we know, existing email studies did
not address these issues while implementing topic inference. Therefore, in this paper,
we assume people will allocate their limited attention to a part of an email's words toidentify the topics that are latent variables behind the words. More precisely, if we
recognize the topic distribution as a feature without considering the effect of attention
allocation, there is a significant deviation from the actual cases.

15

The experimental dataset is the Enron Email Dataset, which is a public email
dataset. Some well-known organizations provide this public dataset in CSV format bygoing through email cleaning and data normalization processes. However, in our case,
we download the original files in UTF-8 encoding, which keeps all the original formats
for drawing a two-dimensional space for each email and calculating each word's
location. Our codes will release at GitHub.
From the general introduction until here, we can summarize our aims and purposes
into a more explicit description below:
1.

As pointed out in the trends review [37], nearly 65% of email studies focus on
spam and phishing email detection. Another 20% of email studies focus on multifolder categorization and email clustering. We have identified our research aim
focuses on the relatively lacking part.

2.

From the previous study’s suggestion [39], most of the email content is noise data.
Based on that, we propose a visual attention allocation approach to simulate thatpeople will put less attention to the noise data practically and make the topic
inference on an email by applying the LDA topic model.

3.

In order to derive the attention distribution, the dataset we applied is as similar as
possible to the actual format people read. Instead of downloading processed CSV
format, we consider the original text files and conduct various processes to extract
the sentences and words for the attention estimation.

2.1 Literature reviews for implementing the AttLDA modelFirstly, we review the current email research from ham and spam email categories.
To our knowledge, the email research can roughly divide into two fields: one is to filter
out those misuse/abuse emails from the dataset; they classify email into a specific class,such as spam, phishing by creating a classifier. The other one focuses on a ham emailthat tries to improve the user experience by considering how to manage and arrange
those useful emails, such as topic extraction, email thread identification, and multifolder categorization. Subsequently, we review the relevant studies that have used LDA
models to implement email analysis and discuss some extension applications. In theend, we review the studies that motivate us to consider visual attention as a mechanism
to combine with the LDA model for topic inference.
162.1.1 The existing works on spam email detection
In the first field, there is plenty of research focused on spam and phishing email
detection.
Youn et al. [40] propose two ontology levels for the spam filter, including Globaland User customized ontology filter. They consider the TFIDF value as an email's
feature and apply it to different classification algorithms to generate the RDF file forcreating ontology via Jena. The C4.5 gets the best result in the experiment than thenaïve Bayesian, neural network, or SVM classifier. The result shows the Global
ontology filter successfully detected 91% of spam emails. They further take into
account the different background of each user to implement the User customized
ontology as well as the second level ontology filter to achieve a more user-customized,
scalable, modularized email spam filter.
Smadi et al. [41] present a framework to explore new phishing behavior from theonline mode's newly received email. The motivation is trying to handle zero-day
phishing attacks and solving the problem of the limited dataset by automatically adding
email data to the offline dataset in the online mode. The proposed framework candynamically select features from fifty features by applying the Feature Evaluation andReduction (FEaR) to rank feature weights and then dynamically change the extractionof the number of features.
Ramanathan and Wechsler [42] explore the field of phishing email detection byproposing a multi-layered approach - PhishGILLNET, which comprises three layers.
The first layer employs the Probabilistic Latent Semantic Analysis (PLSA) to build a
topic model; the second layer considers the feature from PLSA to build a robust
classifier by AdaBoost. The third layer applies Co-training to handle the labeled andunlabeled email data. In this study, the topic model's use attracted our attention, andthere are several processes before they apply the topic model that is worth describing
here. First of all, they consider Porter’s stemming algorithm [43] to remove theinflectional endings from certain words after a series of processes, including parser,
tokenize, and stop words removal. Subsequently, they employ the Part-Of-Speech (POS)
extractor from the WordNet dictionary [44] to look up the words in the dictionary andidentify verbs, nouns, adverbs, and adjectives. Afterward, words in an email are verified
17

by WordNet and utilized Google’s spell check Application Program Interface (API) to
retrieve similar words to the misspelled word. The results obtained from the above
process form a component to build the Term Document Frequency (TDF) matrix. In theend, they conduct the topic model PLSA to identify the synonym, polysemy, and otherlinguistic variations found in a phishing email.
Méndez et al. [45] declare a feature selection method that considers the topic froman email and applies semantic ontology to get the semantic relation and lexicalconceptions. The goal is to improve the topic model by considering the taxonomic
relations between synsets (hypernym and hyponymy) in the semantic ontology. They
further argue that words with similar lexical meanings should be identified as the same
topic, allowing the topic model to provide more consistent results for the synonymous
words in topic inference. In the experiment, besides the proposed method of Semanticbased feature selection, they also apply the Information Gain and Latent Dirichlet
Allocation (LDA) model as feature selection methods to compare and evaluate spam
filtering by eight popular classifiers. As a result, the proposed method, Semantic-based
feature selection, has a better performance than the other two methods.

2.1.2 The existing works on ham email research
Except for the concentration on excluding spam and phishing emails, part of theworks is trying to achieve more effective management of ham emails by clustering or
categorizing technical methods.
Alsmadi et al. [34] define five classes for folder categorization that email users'
nature may consist of Personal, Job, Profession, Friendship, and Others. Subsequently,
they apply K-means clustering to group email into five pre-defined clusters, randomly
selected the centroids, and show magnificent performance in the evaluation stage;
however, the dataset is a private dataset from Gmail. Besides, considering timeconsuming, they only apply the top 100 most frequent words to execute the experimentwith the entire email set. Hence, the principal weaknesses were the following researcher
unable to compare the performance with their result while their dataset is not public.
Several studies [46]–[48] also consider custom or private email datasets for theirresearch that is a serious problem encountered in this research domain.

18

On the contrary, many studies use public email datasets to conduct experiments
and evaluations, which are more rigorous and provide a performance comparison for
subsequent studies.
Dehghani et al. [35] propose a solution with heterogeneity and dynamism
mechanism in email categorization. Their approach constructs two unique structures
for email conversation threads. One is linear; all the emails belong to one conversation
thread and organize in chronological order. The other is a tree structure, whichcalculates the similarity between replies that extracting their relationship to create thetree. As discussed in this work, they assumed that the user might be based on contentaware, time-aware, or participants-aware structural aspects to categorize an email. By
grouping features into several sets, a specific structural aspect can represent by a set offeatures. For simulating user behavior, the study investigates an email from its different
structural aspects to the categorization problem. It tests the similarity score between an
email and a folder by defining the functions that can access the belonging feature spaceas input for each aspect. Each function generates a probability to indicate the folder
assignment. Meanwhile, they agree with the emails' text content is the best
representative of the email's topic since the content-aware made a large proportion ofcontribution.
Sharaff et al. [36] agree that emails are the most popular and effective way tocommunicate over the internet. They further indicate that it is difficult for users to
identify the related topics between the received email and relate newer incoming email.
Hence, the authors consider email threading for email management and communication
improvement, which provides a mechanism for users to sort emails within a certain
time frame. For constructing an email thread that this work goes through a two times
clustering method. In the first part, they apply Latent Dirichlet Allocation (LDA) and
Non-negative Matrix Factorization (NMF) to form an email cluster. Afterward,
calculate the similarity of people and subjects from the previous part's clustering result
to generate the email thread.
McCallum et al. [33] propose the Author- Recipient-Topic (ART) model after
reviewing the Latent Dirichlet Allocation (LDA) and Author-Topic (AT) model. The
core concept is that each email's topic distribution will own a different topic distribution
depending on the sender and recipient. In other words, there is a significant differencein the probability of topic distribution for different senders and recipients. They make19

some improvements and modifications to the original LDA model. From the proposed
ART model, the email is no longer only related to the observed word in an email but
will consider the author and a set of recipients from an email to form a topic distribution.
Moreover, the experiment is conducted on the public email dataset – Enron EmailDataset, and it demonstrates a better performance with lower perplexity value on
previously unseen emails.

2.2 Literature reviews for enhancing LDA model
From the existed research described above, there are interesting trends in the studyof both ham and spam email works that we make a short summarization below. Firstly,in the feature selection of emails, some studies investigate a specific feature in-depth,
such as TFIDF. Simultaneously, some works consider a wide range of features and thenselect a more appropriate one for email analysis by the dynamic feature selection
algorithm. Subsequently, the topic model techniques begin to gain attention in email
analysis research, as well as some scholars focus on the processing of words in email’s
content, including WordNet dictionary check, Stemming algorithm, POS extractor, andGoogle’s spell check API, besides, LDA model is one of the popular ones in the topic
modeling techniques. Moreover, there are various extensions and applications based on
the LDA model. Part of the works applies LDA in combination with other algorithms
to achieve email clustering or email categorization. On the other hand, a portion ofstudies considers the other features of an email to estimate the specific probability ofthe topics distribution depending on certain constraints, such as sender and recipient set.
Based on the LDA model's topics inference, we intend to combine the mechanism ofattention allocation to each word while inferring the topic distribution of each email.
There is a degree of similarity between this approach and the term weighting function.
Hence, we will focus on reviewing the relevant studies that use term weighting to
improve the performance of the LDA model below.

2.2.1 The extension of LDA on term weighting function
To our knowledge, discovering and collecting the features from email by the topic
model has become more popular and common. Blei et al. [49] proposed the Latent
Dirichlet Allocation (LDA), a topic model for automatically summarizing corpus. In
20

LDA, each document is represented as a mixture over an underlying set of topics, andeach topic is selected from a probability distribution over the terms in the vocabulary.
It assumes that the author has a bag full of topics and will randomly draw a topic fromthe bag when writing the document. Afterward, each topic has a bag full of words; theauthor will draw a word randomly from this bag to form a specific topic in the document.
They selected each word from the word bag related to the topic after determining the
topic. The way to represent each document is to consider the bag-of-words; however, awell-known problem with bag-of-words is that it does not acknowledge the order or
location of the words in a document. While an email is not an ordinary document;
instead, it has unique characteristics, involving multiple topics, lengthy replies, highvariance [50]. Each email consists of multiple parts, a header line, a signature part, aquotation line, and actual text content. However, except for text content, most of them
are typical noise [39].
Li et al. [51] argue that some of the inferred topics from the LDA model are hardto interpret, even unexplainable, because the words are given equal weight during the
topic inference. They propose the Entropy Weighting (EW) scheme to measure theword co-occurrences by conditional entropy to address this issue. The primary concept
is to prevent the high-frequency words from dominating the top topic word lists andgiving less weight to those meaningless words, such as stop words. The experimentsection applies the proposed scheme to the LDA and evaluates the topic quality,
document clustering, and classification tasks based on real-world data. This study foundthat the more informative word, the more likely it to bring together a smaller set ofwords. The proposed EW scheme aims to identify the more informative words among
the words with a similar frequency of occurrence for the LDA model's topic inference.
Pion-Tonachini et al. [52] propose the Crowd Labeling Latent Dirichlet Allocation(CL-LDA) for gathering crowd labels from the unlabeled datasets based on the LDA
model. This study also recognized the necessity of giving higher weight value to certain
words when making label (topic) inferences to achieve better performance. They apply
the workers and weighting votes to equalize each work's influence, afterward,
according to the change of weights and the prior distribution to estimate the instanceclass probabilities for allowing to perform comparable to or better than other Crowd
Label (CL) algorithms.

21Wilson and Chew [53] suggest that the term weighting function is necessary wheninferring the topic because the higher frequency words tend to be scattered across too
many latent topics without explainable reason. They believe that the purpose of
applying an appropriate term weighting function is to deal with those high-frequency
words (which might be eliminated as stop words) in a more elegant way. According to
the findings, they argue that removing stop words is not a necessary pre-processing stepafter considering the term-weighting function. However, the removal of stop words is
usually unstated but widespread. To our knowledge, this approach is more realistic,
while humans do not remove stop words when reading a document but instead assign alower weight to these stop words.

2.2.2 The fundamental for building visual attention
As discussed above, most scholars have tried to apply the term weighting function
to assign a lower weight to the high-frequency words as a kind of punishment. Thus,
making the topic inference of the LDA model more meaningful. In our research, wehighlight that the topic model has a weak point in processing email data. It ignores theoriginal structure of an email, and the characteristic words will have different meanings
according to their specific location. Therefore, we consider the visual attention
allocation to assign a specific weight value according to its feature and location
information.
Lovelace et al. [54] propose that we can regard the information from the text page
as a two-dimensional encoded space and assign each piece of information like anaddress in the form of (x, y) coordinates. In other words, the 'WHERE' may serve torecall the 'WHAT'. More precisely, our purpose is to interpret and implement how toallocate attention to the words based on their location and features to infer hidden topics
among these words between the various email structures.
Before applying visual attention, the first thing to do is confirm the definition andambiguous terms. While we discuss attention that it is generally separated into two
mechanisms Top-down and Bottom-up. The expectations and task requirements are theprimary resources for deriving Top-down attention, and salient stimuli from the scene
are the resource for deriving Bottom-up attention [55]. Besides, there are ambiguous
concepts and meanings regarding the terms of attention and saliency.
22

Borji and Itti [56] conclude that the term "attention" is a general concept; it
includes all the factors that affect the selective process of attention, whether Bottom-up
or Top-down mechanisms. Furthermore, the Bottom-up computation applies the term"saliency" often, which considers the scene's part as a factor intuitively. The above
mechanisms and definitions will consider understanding the intention of eachcalculation of probability in the formula as we calculate the attention probability oneach word in the following section. Second, besides the definition, we review theprevious works about how attention influences selection.
White et al. [38] describe that attention is a selective process; it will preferentiallyselect information from specific locations in the scene since they are relatively
important to the task. Based on previous research results, Rayner [57] shows that itneeds to shift or allocate attention to select a single word on the page while reading.
Furthermore, many studies also support that attention will allocate to those mostinformative, most surprising scenarios or locations that can return the maximum reward
for the current task [58]–[60]. From the above review works, we can conclude that
attention can effectively select the content that brings more information. However, as
discussed in the first paragraph of this subsection, an email is not an ordinary documentthat includes multiple structures. Hence it is necessary to prove how people allocate or
shift their attention to those structures and locations concurrently.
The previous research has likened the attention to a spotlight [61] or a zoom lens
type [62]. The spotlight imitates the attention as a beam; it is allowed to move but
process nothing outside of it. However, the zoom lens's way can define as a target
stimulus that is more likely to be detected if near another stimulus [63]. Most of the
discussions focus on whether attention can be split or not based on these two types.
Castiello and Umiltà [64] suggest that humans can split the attention across locations
that allocate it to across two to four locations in the visual field. Moreover, Cave et al.
[65] propose a theoretical framework to explain human can adjust the attention in bothways which include multiple locations can be selected simultaneously and a single
location only.
In our work, we propose the AttLDA model, which models email as a mixture oflatent topics and considers each word's attention value as a weighting function. For
implementing the AttLDA model that we summarize three critical factors from theabove review works.
23

(1) Location: with building a two-dimensional space, each word will assign to a
specific coordinate, and we can determine and estimate the attention
allocation of shift based on it.
(2) Attention types: as we introduce two kinds of mechanisms: Top-down and
Bottom-up, both mechanisms will consider in our experiment. Further, weagree with the idea that it describes attention as a zoom lens; another stimulus
will be more easily detected while it is near the target stimulus.
(3) Split attention: an email is not like an ordinary document that includes
multiple structures. Hence, no matter if we likened attention to a spotlight orzoom lens types, we ensure the attention can split across multiple locations
simultaneously from the above review.
The point is, we consider the allocation of visual attention as a weight function in
the process of topic inference on each word from an email to try to filter out thosemeaningless words. We will present the method in the following section.

24

CHAPTER 3 Methodology for the proposed AttLDA model
The Latent Dirichlet Allocation (LDA) is a well-known model applied to a wide
variety of fields. Throughout this work, there are some terms mentioned frequently.
Thus, before deep into our work, here we conclude the notation and terminology in thefollowing to provide a clear intuition:
1.

A word is the basic unit of a document from a vocabulary of size 𝑉 (includes 𝑉
distinct words). We represent words using unit-basis vectors that only a single
component will equal to one and other components equal to zero. The 𝑣th word
is represented by a V-vector 𝑤 , w
𝑤

0 for u0 ⋯ 010 ⋯ 0such that 𝑤

1 andv, where both 𝑢 and 𝑣 are auxiliary index over vocabulary and

we also define the auxiliary index as word_id, which is considered as a unique
identifier for each word.
2.

A document is a sequence of 𝑁 words denoted by 𝐰
𝑤

𝑤 ,𝑤 ,⋯,𝑤

is the 𝑛 th word in the sequence of document 𝑑 , and 𝑁, whereindicates thenumber of words in document 𝑑, where 𝑛 ∈ 1,2, ⋯ , 𝑁 , and d ∈ 1,2, ⋯ , 𝐷 . In
our case, we recognize an email as a document. For estimating visual attention ofword 𝑤, we define that within each email consists of 𝐾 lines, and there

are 𝑀

cumulative characters in 𝑘th line, where 𝑘 ∈ 1,2, ⋯ , 𝐾 . Based on that,the cumulative characters in 𝑘 th line of document 𝑑 are denoted by 𝐦
𝑚 ,𝑚 ,⋯,𝑚, where𝑚indicates the cumulative amount of characters

until the xth word of the kth line in document d and 𝑥 ∈ 1,2, ⋯ , 𝑋
3.

A corpus is a collection of D documents denoted by Y.

𝒘 , 𝒘 , ⋯ , 𝒘 ; in ourcase, the corpus is a collection of all the email documents.
4.

The visual attention of each word is denoted by 𝐡ℎ ,ℎ ,⋯,ℎ, where ℎ

represents the attention value given by the nth word in document d; ℎ represents
the value of attention given by the xth word of kth line in document d. For example,
if there are only two sentences in a document:“Check your inbox to complete the verification.”"And we will activate your account."
In the first sentence, the word "your" ℎ2, and ℎsentence, the word "your" ℎ5.

12, and ℎ25

2. While in the second

5.

A topic is a latent variable; during the sampling and inferring processes, each topic
distribution of a document is according to the word probabilities, where 𝑡 ∈1,2, ⋯ , 𝑇 .
Table 3-1 the notation of this work.

SymbolMeaning𝑣

Auxiliary index over vocabulary of the corpus Y.

𝑉

Number of words in vocabulary of the corpus Y.

d

Auxiliary index over documents (emails).

D

Number of documents (emails) in the corpus Y.

t

Auxiliary index over topics.

T

Specified number of topics.

k

Auxiliary index over lines in a document.

𝐾

Number of lines in document d.

𝑀

Value of cumulative characters of the kth line in document d.

nAuxiliary index over the words in document d.

𝑁

Number of words in document d (document length).

x

Auxiliary index over the words of kth line in document d.

𝑋

Number of words of kth line in document d.

𝑤

It represents the nth word in a document.

𝑤

It represents the nth word in document d.

𝑚Value of cumulative amount of characters until the xth word in kth line.

α

A prior T-vector represented the topic distribution of documents.

𝛽

A prior V-vector represented the word distribution of topics.

𝛽

A prior V-vector represented the word distribution conditioned on the
topic 𝑧 .

𝜃A topic proportions for document d, which subject to the 𝐷𝑖𝑟 𝛼 .

𝐷𝑖𝑟 𝛼

A T-dimensional Dirichlet distribution with a symmetric parameter α forproviding the topic proportions.

𝜙A topic-word proportions for topic 𝑧 , which subject to the 𝐷𝑖𝑟 𝛽 .

𝐷𝑖𝑟 𝛽

A V-dimensional Dirichlet distribution with a symmetric parameter β forproviding the vocabulary proportions.
26𝑧Topic index over topics for nth word in document d; 𝑧𝑡 means thatthe nth word in the dth document is assigned to topic t, which subject
to Multinomial 𝜃 .
ℎIt represents an attention value of the nth word in document d.

𝑓Value of visual feature of nth word w in document d.

𝑓Value of visual feature of vocabulary word v.

𝑙Value of location of the nth word in document d.

3.1 Data collection of Enron Email Corpus
The Enron Email Corpus is alluring and of particular interest with much academic
value for applying as the experimental dataset. It is a rich temporal record of internal
communication within an organized, real-world corporation. This corpus contains alarge amount of raw data on communication, knowledge sharing, relationships,
perceptions, resources, and events in a company. In addition to the above considerations,
the Enron Email Corpus has been widely used in academic works, including Klimt andYang [66] exploring the distribution of emails and each user's time series from theEnron Email Corpus extract the characteristics to form an email discussion thread.
Bekkerman et al. [67] consider that workers in an organization have to deal with many
spam emails and large quantities of legitimate emails. Hence, he presents a classifier toautomatically classify emails into pre-defined folders by applying two corpora,including Enron Email Corpus and SRI Corpora. Duan et al. [68] analyze and identify
the importance of the Enron employees by extracting the Enron Email Corpus features,
including the number of emails sent and received.
Before delving further into the proposed AttLDA model, we describe how weacquire the mail dataset and process it into a type that can be used for visual attention
estimation on each word, as illustrated in Figure 3.1. Some of the agencies provide theEnron email data through a series of data pre-processing to facilitate the research work,
such as Kaggle [69]. A large percentage of scholars download this dataset directly fromthose organizations or agencies. However, in our case, we need to preserve the original
structure and format for estimating the visual attention on each word, according to the27word’s location, feature, and inferred topics. Hence, we download the original resourcein zip file format from https://www.cs.cmu.edu/./enron/enron_mail_20150507.tar.gz.

Figure 3.1 The collection process of Email data.
In Figure 3.1, there are two main aspects to this illustration, which we divided into
the upper and bottom parts. In the upper part, the process focuses on scanning email
corpus architecture, and we describe it from left to right. In either part, we will automate
the work by building a python tool.
First and foremost, we decompress the downloaded file. After decompressing,several folders will be created in the pre-created folder. In our case, we store all thedecompressed folders within the folder with the name "EnronEmail". Eachdecompressed folder is regarded as a personal mailbox of an Enron employee, and thefolder name represents their user name. Once completed, we overview how many Enron
employees' mailboxes were collected in this corpus.
Secondly, going through each Enron employee's folder, there are several
customized folders beyond the inbox folder. Also, there is no fixed number ofcustomized folders over each folder. Therefore, the python tool we built must support
a dynamic approach to scanning the customized folders' structure.
28

Last but not least, each customized folder may hold several emails and might bean empty folder as well. The emails in the folders are named numerically. Thus, before
accessing the content of an email, the only information we identified is that the email
is stored in a specific employee’s mailbox and to which defined folder it is classified.

Figure 3.2 The snapshot for an original Enron email (text file).
Next, proceeding to the bottom part, each email is an unstructured text file as wellas the presented snapshot in Figure 3.2. The definition of unstructured text is
information that either does not have a pre-defined data model or is not organized in a
pre-defined manner. Hence, using the Regular Expression (RegEx), we identify eachcolumn of an email and extract the content, including Message-ID, Date, From, To,
Subject, Mime-Version, Content-Type Content-Transfer-Encoding, X-From, X-To, Xcc, X-bcc, X-Folder, X-Origin, X-FileName, and Content.
Afterward, the email content is encoded in UTF-8 format. The purpose is to
identify the line breaks' location, which is display as "\\n". According to that, we canperform the sentence segmentation in the subsequent process that we split the encoded
content string into an array according to the line breaks.
29

Lastly, we insert the email data column by column into the MySQL database forthe permanent application and export it as a CSV file as shown in Figure 3.3 to Figure
3.5, which will be used as input data for subsequent actions. To sum up, we have listed
the actions we carried out below:
1.

Decompress the Enron Email Corpus.

2.

Identify and access the structure of Enron Email Corpus.

3.

Identify and access the customized folders from each Enron’s employee.

4.

Identify the email text file over each customized folder and access its content byusing the RegEx.

5.

Identify the line breaks' location by encoding email content in UTF-8 format forsentence segmentation process.

6.

Insert the email data into the MySQL database for the permanent application.

7.

Export the data into CSV from the MySQL database for the subsequent analysis
process.

Figure 3.3 The snapshot for the Enron email inserted into database.

Figure 3.4 The snapshot for the Enron email inserted into database.

30

Figure 3.5 The snapshot for the Enron email inserted into database.

3.2 Latent Dirichlet Allocation (LDA)

Figure 3.6 The graphical model of LDA.
LDA is a generative hierarchical Bayesian model that represents the documents as
random mixtures over latent variables. Each document conducts as a random mixtureof corpus-wide topics, and each word in a document is drawn from one of the specified
topics. It assumes topics exist outside of the document collection, and each topic is
distributed over fixed vocabulary. In Figure 3.6, we illustrate the graphical LDA modelthat the bottom-outer box represents documents in the corpus, and the inner one
represents the selection of topics and words within a document. The upper one
represents the selection of topics. One of the advantages of LDA is that it provides welldefined inference procedures for previously unseen documents.
As shown in Figure 3.6, a corpus Y consists of D documents, and each documentd has 𝑁 words. Except for words 𝒘

in document d, other variables are not directly

observable. Here we describe the different meanings of representatives over the31

graphical model. The plates' boxes represent the replicated structure, nodes are randomvariables, edges represent possible dependence, and the gray shaded one is the observed
variable. The documents are unsupervised data, and applying this model to explore thelatent variables to identify a document's topics. LDA represents topic by word
probabilities, and all the probability generation starts from Dirichlet prior. We conclude
the steps for the generative process of LDA:
1.

Choose 𝑁~𝑃𝑜𝑖𝑠𝑠𝑜𝑛 𝜉 , where ξ is the average number of words in a document
over the corpus. This step can be omitted while the number of words is a known
parameter.

2.

Choose 𝜃 ~𝐷𝑖𝑟𝑖𝑐ℎ𝑙𝑒𝑡 𝛼 , 𝜃 and α are T-dimension and α is a prior as regard
as a pseudo counts of a topic in a document.

3.

Afterward, for each word 𝑤 , choose a topic 𝑧 ~𝑀𝑢𝑙𝑡𝑖𝑛𝑜𝑚𝑖𝑎𝑙 𝜃

where 𝑧 is

a topic index over a T-dimension. A multinomial distribution with parameter 𝜃over topics, drawn from a Dirichlet prior with hyper-parameter α.
4.

Choose 𝜙~𝐷𝑖𝑟𝑖𝑐ℎ𝑙𝑒𝑡 𝛽 , ϕ and β are V-dimension and β is a prior as

regard as a pseudo counts of word-topic pair. A multinomial
5.

Choose a word 𝑤 ~𝑀𝑢𝑙𝑡𝑖𝑛𝑜𝑚𝑖𝑎𝑙 𝜙, which is a multinomial probability

conditioned on the topic 𝑧 . A multinomial distribution with parameter ϕ overwords, drawn from a Dirichlet prior with hyper-parameter β.
In short, given the prior parameters α and β, we choose the random variables θand ϕ. For each word w in document d, we are sampling a topic t as a word-topic pairfrom a multinomial distribution with parameter 𝜃 , represented as 𝑧
indicating the word 𝑤, clearly

belongs to which topic. To generate the word 𝑤

document d, we are sampling a topic t as 𝑧

infrom the multinomial distribution withparameter 𝜃

firstly. After that, according to the specific topic 𝑧

parameter 𝜙, we select the corresponding multinomial distribution to sample theword 𝑤and theinto document d.

Lastly, we summarize the above description into the equation below:
𝜃 ~Dirichlet 𝛼 ∀𝑑 ∈ 𝐷(1)

𝜙 ~Dirichlet 𝛽 ∀𝑡 ∈ 1,2, ⋯ , 𝑇

(2)

32

𝑧

~Multinomial 𝜃𝑤~Multinomial 𝜙∀𝑑 ∈ 𝐷, ∀𝑛 ∈ 1,2, ⋯ , 𝑁

(3)
(4)

∀𝑑 ∈ 𝐷, ∀𝑛 ∈ 1,2, ⋯ , 𝑁

3.3 Attention integration based Latent Dirichlet Allocation(AttLDA)
Describing the LDA model in linear-algebraic terms that the product of θ (the 𝐷
𝑇 column-stochastic topic-by-document matrix) and ϕ (the 𝑇
topic-by-vocabulary matrix) is the 𝐷

𝑉 column-stochastic

𝑉 term-by-document matrix. The standard

LDA model presented above assumes each word is equally important (equal weighting)in calculating the conditional probabilities. However, this is not the case from both aninformation-theoretic and linguistic point of view. For example, in English, the word"the" is meaningless compare with other low-frequency words. Besides, in the case ofan email, contents located in a forward message are often considered less meaningful.

Figure 3.7 Graphical representation of proposed AttLDA model.

33

Our proposed model – AttLDA infers the email topic distribution by considering
the visual attention, which is calculated by the variance among the topic distribution,
word location, and visual feature per word. For each vocabulary v, we estimate thevisual feature from the Term Frequency (TF) and Inverse Document Frequency (IDF)
as a visual feature. Jones [70] proposed the IDF idea firstly in 1972 and conceived astatistical interpretation of term-specificity (IDF) which became a cornerstone of term
weighting. Salton and Yu [71] present the TFIDF algorithm in their work by combing
IDF's idea and demonstrating the effectiveness of this algorithm in information retrieval.
We consider the TFIDF for estimating the visual feature, as in Eq. (5).
𝑡𝑓𝑖𝑑𝑓 𝑣, 𝑑, 𝐷
𝑡𝑓 𝑣, 𝑑ℱ,

𝑖𝑑𝑓 𝑣, 𝐷log

𝑡𝑓 𝑣, 𝑑 ∙ 𝑖𝑑𝑓 𝑣, 𝐷
ℱ

(5)
(6)

,

𝐷
| 𝑑∈𝐷∶𝑣∈𝑑 |(7)

In Eq. (6), a term frequency is to use the raw count of a term 𝑣 in document 𝑑,e.g., the raw frequency of term 𝑣 divided by all the occurring terms in the document.
In Eq. (7), the inverse document frequency is a measure of how much information froma word, i.e., the information provided by a word, is rare or common across the corpus.
The logarithmically scaled inverse fraction is obtained by dividing the total number of
documents by the number of documents containing the term.
A location parameter 𝑙

is constructed by the parameter 𝑘

𝑛th word over lines in document 𝑑) and the parameter 𝑚(value of line until(value of cumulative

amount of characters until the xth word in kth line). In the part of word location
estimation, we have consider to several literatures to determine other necessary
parameters, which will be described with more details in a later section.
Given a Dirichlet parameters α and β and a sequence of 𝑁 words in a document
which denoted by 𝐰, the generative process works as follows:1.

The occurrence rate of each topic 𝑡 in document 𝑑,𝐷𝑟𝑎𝑤 𝜃 ~𝐷𝑖𝑟𝑖𝑐ℎ𝑙𝑒𝑡 𝛼

(8)

34

2.

For each document 𝑑, number of times the 𝑛th word in document 𝑑 occurred ineach topic,𝑧 ~𝑀𝑢𝑙𝑡𝑖𝑛𝑜𝑚𝑖𝑎𝑙 1, 𝜃 , 𝑧

3.

~𝐷𝑖𝑟𝑖𝑐ℎ𝑙𝑒𝑡 𝛽(10)The visual feature for each vocabulary word 𝑣,
𝑓

5.

(9)

The occurrence rate of the words over the topics,
𝜙4.

∈ 1,2, ⋯ , 𝐾

𝑉𝑖𝑠𝑢𝑎𝑙𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑖𝑜𝑛 𝑡𝑓𝑖𝑑𝑓 𝑣, 𝑑, 𝐷

(11)For each document 𝑑,(a) The word location 𝑙
𝐷𝑟𝑎𝑤 𝑙for each word 𝑤 ,𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑖𝑜𝑛 𝑘 , 𝑚(b) The visual attention ℎ𝐷𝑟𝑎𝑤 ℎ(12)for each word 𝑤 ,𝐴𝑡𝑡𝑒𝑛𝑡𝑖𝑜𝑛𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑖𝑜𝑛 𝑙 , 𝑓 , 𝜙(13)(c) For each word 𝑤 ,𝐷𝑟𝑎𝑤 𝑤 ~𝑀𝑢𝑙𝑡𝑖𝑛𝑜𝑚𝑖𝑎𝑙 𝜙∙ℎ,𝑤∈ 1,2, ⋯ , 𝑉(14)

After defining the structure of the AttLDA model, we can derive the probability ofobserving a word 𝑤

in the Eq. (15) below:

𝑝 𝑤 |𝑧 , 𝜙 , ℎWhere 𝑤ℎ(15)

is the observed word, 𝑧

is an index from 1 to 𝑇, 𝜙 are the topics, andis the visual attention.
Next, we can derive the mechanism of data generation, i.e., the joint probability

distribution of all the hidden and observed variables according to the Eq. (16) below:
𝑝 𝜃, 𝜙, ℎ, 𝑧, 𝒘, 𝑓|𝛼, 𝛽, 𝑙𝑝 𝜙 |𝛽∙𝑝 𝑓 |𝜙(16)𝑝 𝜃 |𝛼Where 𝑝 𝜙 |𝛽𝑝 𝑧 |𝜃

𝑝 ℎ |𝑙 , 𝜙 , 𝑓 𝑝 𝒘 |𝑧 , 𝜙 , ℎis topics distribution over words which comes from Dirichlet

distribution with parameter 𝛽. 𝑝 𝜃 |𝛼 is the topic proportion over documents from
Dirichlet distribution with parameter α . 𝑝 𝑓 |𝜙35

is used to estimate the visualfeatures by TFIDF value of the words given 𝜙 . 𝑝 𝑧 |𝜃is used to indicate withineach document, we have the words drawn from the topic assignment from 𝜃 .
𝑝 ℎ |𝑙 , 𝜙 , 𝑓

is used to indicate that the visual attention of each word determinedby location 𝑙 , topic-word proportion 𝜙 , and visual feature 𝑓 . 𝑝 𝑤 |𝑧 , 𝜙 , ℎis the probability of observing this word, conditioned on 𝑧 , 𝜙 and ℎ .
Subsequently, in our discussion of joint probability, we describe how wedetermined the visual attention, the word location, and the visual features of each word.
Afterward, we describe how AttLDA model learning topics and attention via Collapsed
Gibbs Sampling in the next section. Although LDA has been widely used in latent topics
exploration, there is rare research to consider visual attention among words.
The review works of state-of-the-art visual attention modeling [56], introducing
attention models including Cognitive model, Bayesian model, Decision Theoretic
model, Information Theoretic model, Graphical model, Spectral Analysis model,
Pattern Classification model, and other model. They further explain their mechanism toobtain saliency and attention. Also, all of the introduced models can implement insoftware. In this study, we select the Bayesian model, which combines the priorknowledge (e.g., scene content or gist) and sensory information (e.g., target features) todetect an object of interest probabilistically. According to that, we consider it as aweighting function into the LDA.
As mentioned above, LDA is a generative probabilistic model that identifies the
documents as a mixture of latent topics and a distribution over the words determinedby each topic. The basic idea is that if a word with higher probabilities to a topic, it getsa higher probability of indicating a specific topic than other words. Nevertheless, we
assume people will allocate variance attention to a word according to the location,
feature, and target of each word (topic) in our work. We depict the graphical model of
AttLDA in Figure 3.7. Specifically, given a corpus Y consist of documents D, where
document d is an email that composed of the 𝒘

words, and there are five steps for

explaining the parameters of our proposed AttLDA in detail:1.

The occurrence rate (pseudo count) of each topic 𝑡 in document 𝑑 is drawn froma Dirichlet distribution with parameter 𝛼. For each document d, randomly choose
a topic 𝑧 from the multinomial distribution 𝑀𝑢𝑙𝑡𝑖𝑛𝑜𝑚𝑖𝑎𝑙 𝜃 . According to thesuggestions from previous research [33], we define the α
36

50/𝑇.

2.

For each word in the vocabulary, we first determine the occurrence rate (pseudocount) of the words over the topics which draw from the Dirichlet distribution withparameter 𝛽. According to the previous research [33], we define the parameter 𝛽equal to 0.1. In the proposed AttLDA model, the proportions of the words
generation not only consider the multinomial distribution 𝑀𝑢𝑙𝑡𝑖𝑛𝑜𝑚𝑖𝑎𝑙 𝜙 , butalso the visual attention ℎ.

For estimating the word location 𝑙, we consider two parameters to represent a
document into two dimensional space. We apply 𝑘
and 𝑚
3.

to indicate the word in kth line,is the cumulative characters until 𝑛th word from kth line.

Regarding the word location determination, we first deliberate on determine thecoordinate of a document for defining the word location later. Thus, we arranged
the detail in the following and summarize the algorithm for assigning word
location as described in Figure 3.8 and Figure 3.9.
(1) As discussed in the chapter of literature reviews above, Castiello and Umiltà[64] suggest that we can split the attention across locations that include two
to four locations. They expressed that people can put their attention todifferent locations at the same time while reading a document. Further, Dillon
et al. [72] present that people will extract information from text using the
optimal display size according to the target even though some differences
might exist between individuals. For achieving the task and take into accountthe display size, there are two factors that we need to define: line length (thecharacter per line) and window size (the number of lines or window height).
Previous research has several suggestions about the line length, including 132
characters, between 25 characters to 100 characters, less than 70 characters,
between 40 characters to 60 characters, 55 characters, and 100 characters[73]–[78]. Some of the views consider reading rate or ease of reading
purposes to present the suggestions on line length; however, the line lengthwith 55 characters is suggested by Chaparro et al. [77] which is based onreading comprehension. Therefore, we used 55 characters as the value for theline length because we believe that the purpose of reading comprehension ismore closely related to our research objectives. We define the parameter
CharsLimit in Algorithm1, as shown in Figure 3.8.
37

(2) The next factor that needs to take into account is the window size. From theprevious research, Richardson et al. [79] show that people prefer the larger
window size for reading or writing on a text page. Dyson and Kipping [74]present a study of the minimum window size, and the number of lines thatare easiest to read must be at least 15 lines. However, it only provides thelower bound for determining the window size. Hence, we also refer to thework proposed by Duchnicky and Kilers [80] that they apply different
variables for the evaluation, including five different values (1 line, 2 lines, 3lines, 4 lines, and 20 lines). We select 20 lines for the window size from theabove five values and considering the recommendations of a relevant study
on the lower bound value that the minimum window size must be at least 15
lines. Therefore, we define the parameter WindowLimit in Algorithm 1 withthe value 20.
(3) Some extra factors might also affect location segmentation, such as marginsize, interlinear spacing and font size, Etc. However, the result proposed byDuchnicky and Kilers [80] states that there is no difference inincomprehension with the various sets of font sizes. Gould and Grischkowsky[81] found the interlinear spacing and visual angles that have no significant
impact on reading accuracy. After considering the recommendation andsuggestions from previous research, we apply two parameters (windows sizeand line length) to create a visual space representing the best display size forpeople to extract text information from a document (an email). Subsequently,
the sentence segmentation will automatically process according to the rules
below to form a visual space. If the number of characters in a sentence
exceeds the threshold of line length and there is no line break symbol, we will
create a new line as a new sentence in the current document (email)automatically; and simultaneously update the WindowLimit in Algorithm 1.
The intention of doing so is because we only consider two factors to form ourvisual space of an email, including line length and window size; other factors
will not include in this work for any further consideration.

38

Figure 3.8 Algorithm 1: Preprocessing for assigning location 𝑙to each word 𝑤 .

(4) By determining the line length and window size, we define the visual space
as above mentioned. Castiello and Umiltà [64] indicate that people

could

split attention across two to four positions. Depending on that, we divide thewords into five locations; and we further define the additional fifth location
to indicate those words do not display within the pre-defined visual space
where the line length of the word is larger than 55 characters, and window
size is larger than 20 lines. Besides, we also consider whether the location ofa word is under some prefixes (e.g., "forward" or "original message" or39

"forwarded message," Etc.), when such a scenario exists, we will identify thefollowing content (words) as located in the additional fifth location.

Figure 3.9 Algorithm 2: Assigning target and location to each word 𝑤 .
4.

In the third step, we completed the definition of the word's location and explainedthat literature reviews which we refer to achieve our purpose. Next, after we
reviewed 65 state-of-the-art attention models from the previous work [56], and wechoose the Bayesian model for estimating the visual attention by Eq. (17) below.
The reason we select the Bayesian model is because this model considers overall
40

factors, including the Bottom-up salient, prior knowledge, and Top-downknowledge (location prior). As we discussed the types of attention in the literature
reviews chapter that the Bayesian model consider both types (attention andsaliency), which is the primary reasons we select it as our model basis. Althoughmost studies apply this model for image-related research, we allocate the location
to words in the document through the previous steps, which allowed us to integrate
this model with the LDA model into our research. We make an explanation below.
log 𝑠

log 𝑃 𝐹𝑓log 𝑠log 𝑃 𝐹𝑓ℎℎlog 𝑃 𝐹log 𝑃 𝐹𝑓 |𝐶

1

log 𝑃 𝐶

1|𝐿

𝑓 |𝐶

1

log 𝑃 𝐶

1|𝐿

𝑙
(17)

𝑙

𝑒𝑥𝑝 log 𝑠(1) First, let us briefly review the original equation and definition of the Bayesianmodel as shown in Eq. (17). They define 𝑠 as the overall saliency of a pixel
point 𝑧 in an image, where 𝐹 is the visual features of a point, 𝐶 is used todetermine whether a point belongs to a predefined class or not, and 𝐿 is thelocation of a point (pixel coordinates), where 𝑓 is the visual features on
point 𝑧 and 𝑙 is the location on point 𝑧.
(2) Next, we define the notations and the parameters for applying the Bayesianmodel into our proposed AttLDA model. We define point 𝑧 as a word 𝑤 inan email in our experiment since it is the basic unit in a document similar to
a

pixel

point

of

an

image.

Therefore,

we

further

explainthe 𝑉𝑖𝑠𝑢𝑎𝑙𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑖𝑜𝑛 method here as we describe in the above Eq. (11).
About the 𝐹 of thelog 𝑃 𝐹𝑓 , we calculate the TFIDF value first by

applying 𝑡𝑓𝑖𝑑𝑓 𝑤 , 𝑑, 𝑌 and define ten groups of TFIDF values by thepercentile function. Each word 𝑤
ten groups as their feature 𝑓will estimate the nearest index from the, and we calculate the probability of eachfeatures based on it later. The target class 𝐶 of the log 𝑃 𝐹

𝑓 |𝐶

1 is

defined as a binary value like Boolean logic in the original formulation toindicate whether find the target or not. In our case, our target is discovering
the hidden topic behind a word; therefore, we define the target class C as a
topic.
41

(3) Lastly, about the 𝑙Y-axis and 𝑚
the 𝑙in log 𝑃 𝐶

1|𝐿𝑙, we employ the 𝐾 lines ascharacter as X-axis to form a coordinate to measureof each word as the location in a document.

In Eq. (17), the first term on the right side is the self-information (bottom-up
saliency), and it depends on estimate the probability of each index of a word 𝑤

fromthe whole index by discovering the nearest index of TFIDF value groups; the word 𝑤
is same as the point 𝑧. The self-information will take a higher value for a rare TFIDF
index of a word, that is, the more familiar TFIDF index, the less interest (attention).
The second term is the log-likelihood, which considers the TFIDF index of a wordconsistent with the prior knowledge of the target (topic) 𝐶. The purpose here is to figureout if the feature (TFIDF index) of a word is rarely co-occurred with related priorknowledge of the target 𝐶; in that case, it will get a smaller value. Finally, as we
previously presented, we assign a particular location to a word, and the third term of
the equation is the top-down knowledge of the location at a word point 𝑧. We use it tocalculate the probability that a word co-occurred with a particular 𝐶 in the case of thegiven location 𝑙 .
The attention value of each word is primarily based on Eq. (17), in which wordpoint, saliency 𝑠 , feature 𝑓 , target C, and location 𝑙 are concluding as follows:
⎧
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪𝑙
⎪
⎪
⎩

5.

𝑠

𝑧 ∈ 1,2, ⋯ , 𝑤

𝑓

𝑓 ,𝑓 ,⋯,𝑓𝐶𝑡 ,𝑡 ,⋯,𝑇

0, 55 𝑚27.5 𝑎𝑛𝑑 𝑘
10⎧1, 𝑚
27.5 𝑎𝑛𝑑 𝑘
10
⎪
2, 𝑚
27.5 𝑎𝑛𝑑 20 𝑘
10⎨3, 55 𝑚
27.5
𝑎𝑛𝑑
20
𝑘
10
⎪
⎩4, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 𝑜𝑟 𝑢𝑛𝑑𝑒𝑟 𝑝𝑟𝑒𝑑𝑖𝑥𝑒𝑠 𝑐𝑜𝑛𝑡𝑒𝑛𝑡(18)

We then talk about how to integrate with the LDA to form our AttLDA. For eachdocument d, only both of a word w and the location l can observe in the generative
process. For each document d, form a location 𝑙by considering 𝑘

and 𝑚

,

and a topic 𝑧 of document d is chosen from the multinomial distribution with42

parameter 𝜃 . To generate a word 𝑤
corresponding distribution ∅topic 𝑧, where the ℎin document d, it then selects theand the value of attention ℎdepending on theis a term weighting schema to represent how muchattention on a word. In the AttLDA model, given the Dirichlet prior α and β, thelocation 𝑙, the topic distribution Multinomial 𝜃

distribution Multinomial 𝜙, the mixture

for the topic z, a visual attention ℎ, a set of topic T,

and a set of words in the corpus Y to learn the topic distribution of a document 𝑑.
Although the AttLDA model with several latent variables that need to estimate byposterior distribution, in practice, once the topic z assignment and visual attention
allocation of each word are derived, the distribution θ and 𝜙 can be estimatedaccordingly. As shown in Eq. (19), it is a further illustration based on Eq. (17).
𝑝 𝜃 , 𝜙 : , ℎ , 𝑧 , 𝒘 , 𝑓 |𝛼, 𝛽, 𝑙⎧⎪⎪𝑝 𝜙 : |𝛽 ∙ 𝑝 𝜃 |𝛼 ∙𝑝 𝑤 |𝜙

⎨
⎪
⎪
⎩

,ℎ

𝑝 𝑧 |𝜃

∙𝑝 ℎ𝑙, 𝜙 : , 𝑓

𝑝 𝑓 |𝜙 :

∙

∙

⎫
⎪
⎪

(19)

⎬
⎪
⎪
⎭

However, the above joint probability distribution is not our ultimate goal, ourobjective is to learn from a large number of emails in an email corpus 𝑌 to obtain the
distribution of topics of each email, the distribution of words of each topic and thevisual attention allocation of each word. In other words, try to get the posterior
distribution of θ, 𝜙, 𝑧, and ℎ.
𝑝 𝜃, 𝜙, 𝑧, ℎ|𝛼, 𝛽, 𝑙, 𝑌, 𝑓𝑝 𝜃, 𝜙, 𝑧, ℎ, 𝑌, 𝑓|𝛼, 𝛽, 𝑙𝑝 𝑌, 𝑓|𝛼, 𝛽, 𝑙

(20)The subsequent step is called inference in Bayesian statistics. However, thechallenge in the inference step is in writing an email 𝑤 ∈ 𝐷, while both the topic 𝑧
and the associated words proportion 𝜙of the topic 𝑧 are predefined. We are notaware of the generation mechanism of an email without given 𝑧 and 𝜙 . Therefore,
some works apply the common and well-known Bayesian estimation method –43

Variation Inference. Our research employs a more efficient method, Collapsed Gibbs
Sampling for the inference process, and describes the process in the section below.

3.4 Inference by Collapsed Gibbs Sampling on AttLDA
Before presenting how we apply the Collapsed Gibbs Sampling in our works, we
also review another well-known estimation algorithm - Expectation Maximization
algorithm. Blei et al. [49] present the variational Expectation-Maximization algorithm
by executing the E-step and M-step iteratively. This algorithm considers the E-step toestimate each document's topic distribution by current model parameters in the training
stage and applying the M-step to update the model parameters. However, asXiao and

Stibor [82] indicate, that Expectation-Maximization algorithm approach is prone to
local optima due to the wiggly likelihood function, and Minka and Lafferty [83] further
argue that variational Expectation-Maximization algorithm can lead to inaccurateinferences and biased learning. Subsequently, Griffiths and Steyvers [84] proposed theCollapsed Gibbs Sampling, a Markov-chain Monte Carlo method and as present by
Xiao and Stibor [82] that the Collapsed Gibbs Sampling is an efficient and straightforward approach to rapidly converges in a known ground-truth. Besides, it has been
widely used in many LDA research works. The description above is the primary reason
why we select Collapsed Gibbs Sampling in this work.
For obtaining a practical result from the proposed model, we use collapsed Gibbs
sampling [84] to perform the inference process. After defining the AttLDA model'sarchitecture, we also obtained a data generation mechanism, that is, the joint probability
distribution between parameters and email data as shown in Eq. (19). As a consequenceof the joint probability distribution, our ultimate target is to learn how to identify the
topic distribution of words in an email by considering attention's influence. Gibbs
sampling through the Markov Chain Monte Carlo process, repeat sampling toapproximate the unknown joint probability distribution. Meanwhile, Gibbs sampling is
a Markov chain with stable distribution; strictly speaking, while simulated times
enough, the sample generation’s distribution will be equal to the unknown joint
probability distribution. Before implementing the inference, we also refer to theprevious studies [51]–[53], which apply term weighting schemes into LDA to filter out
meaningless words. They consider multiple schemes such as Log weighting to achieve
44the purpose; Log weighting is used to concern a word frequently appears in a document
or not. In the AttLDA model, we used attention to emphasize a word. While a word getsa higher value of attention, it will provide the rich word for the topic inference and givea clear explanation. Hence, we assume it will more effectively capture the topic from adocument.
Accounting for attention and weighing the probability of a word to simulate theinfluence of attention when selecting a word for a particular topic, the probabilities in
the AttLDA are calculated based on Eq. (20). In practice, we only need to estimate thevisual attention of each word and the topic distribution of each word in an email, due
to that, the distribution of θ and 𝜙 can be estimated accordingly. Hence, when we
conduct Gibbs Sampling and only sample for 𝑧 without considering other latentvariables, we call Collapsed Gibbs Sampling.
𝑝 𝑧 |𝛼, 𝛽, 𝑙, 𝑌, 𝑓, ℎ
𝑝 𝑧 , 𝑧 , 𝑌|𝛼, 𝛽, 𝑙, 𝑌, 𝑓, ℎ
𝑝 𝑧 , 𝑌|𝛼, 𝛽, 𝑙, 𝑌, 𝑓, ℎ
∝ 𝑝 𝑧 ,𝑧(21)

, 𝑌|𝛼, 𝛽, 𝑙, 𝑌, 𝑓, ℎ𝑝 𝑧, 𝑌|𝛼, 𝛽, 𝑙, 𝑌, 𝑓, ℎ

As shown in Eq. (21), we begin with the known distribution and given the topic
distribution from the inferred emails 𝑧

to approach the topic distribution of theemail 𝑑. In Eq. (22) below presents the probability that topic 𝑧 of 𝑛th word in email 𝑑is assigned to topic 𝑡 by applying the Collapsed Gibbs Sampling derived from the joint
probability distribution.
𝑝 𝑧𝑡|𝛼, 𝛽, 𝑙, 𝑌, 𝑧∝

Here, 𝑁 ,

,ℎ

ℎ 𝑁,
∑

ℎ

𝛽
𝑁,

∙

𝛽 ∑

𝑁,
𝑁,

𝛼
𝛼

(22)

indicates the summation of word count are assigned to another topic 𝑡

expect topic 𝑧 , and 𝑧

is the topic that the model tries to sample in the current

document 𝑑 Also, 𝑁 ,denotes the summation of word count except for the word

itself. The below Eq. (23) and Eq. (24) show the derivation of document and topic anddocument pair distribution θ and the topic word pair distribution 𝜙 from 𝑧.
𝜙,

ℎ 𝑁
∑

ℎ

𝛽
𝑁

(23)

𝛽
45

𝜃 ,

𝑁
∑

(24)

𝛼
𝑁

𝛼

To review all the processes we perform, we use the flow chart to describe, as we
illustrated in Figure 3.10. We divide the current workflow into five stages:(1) Loading and initializing email data;(2) Removing noise data and assigning word positions;(3) Estimating the value of attention for each word;(4) For training and testing process, split the data set into training and test data,
and define the number of topics based on topic consistency metric;(5) Infer the topic of each word and used perplexity to evaluate the performanceof the proposed AttLDA.

Figure 3.10 The workflow of AttLDA model.
As described in Figure 3.10, the mission in the first stage is loading the Emaildataset. About the Email dataset, there are two critical things. We consider the Enron
Email Dataset to our experiment to make our result and performance more reliablebecause it is a public Email dataset. The other one is that for calculating each word's
46

location, we need to keep all the format of an email; therefore, we download the originalZIP files, UTF-8 encoded process, segmentation, insert into a database, and export toCSV format. After that, the first stage will be loading the CSV format and deliver thedataset to the second stage-Word process.
In the second stage, we remove the noise data but not including the Stopwords.
After we complete the Tokenize process, remove the line break from each document
encoded in UTF-8 format. We keep these line breaks until the second stage because we
can keep the word location even through the Tokenize process and then deliver the
Tokenize word-location pairs to the next stage.
Before deep into the third stage, we first download two packages from Natural
Language Toolkit (NLTK), including the words package and the stopwords package.
We employ the words package to verify that the words in an email are verified andwithout the mis-spelling situation. On the other hand, we apply the stopwords package
to verify a word is a meaningless word or not, such as to, would, with, by, and may, Etc.
In the third stage, we divide the pair data delivered from the previous stage and extract
each word from the Email content data to determine whether it exists in the Stopwords
list or not. If so, the process will ignore and loading the next word. If a word does notbelong to the Stopwords, we will insert it into the Word count vector for the model
evaluation.
Moreover, if a word is never seen before, it will insert into the Vocabulary vector.
By downloading the NLTK words package, besides the Stopwords list, we also form alegal word list to verify the words without misspelling issues; if a word does not exist
in the legal word list, we will treat it as a stop word. After we run a loop of all the words,
calculate each word's attention value based on its location, features, and the inferred
topics.
In the fourth stage, in addition to dividing the data set into training and test data,
it is worth mentioning that we use the Coherence model here to determine thedescription of the number of topics for the AttLDA model. Namely, we select the
number of topics with the highest Coherence Score from the Coherence model results.
Finally, training and testing our topic model, and according to the result, define each
topic's name and show the evaluation to demonstrate the performance of AttLDA.

47

CHAPTER 4 Experimental results of AttLDA
In this section, we present the experiments to demonstrate the performance of the
proposed AttLDA model. First, we discuss how to carry out the data splitting, i.e., weapportion the collected data into the training set and test set. Next, we describe how we
implement the proposed AttLDA model, including the programming language andexperiment environment. Third, we consider the Coherence model to determine thenumber of topics 𝑇 . Fourth, we present how we evaluate the performance of the
proposed AttLDA model. Lastly, based on the results of the proposed topic model, we
conduct a further discussion.

4.1 Experimental Data set
As described in the previous chapter, we consider the Enron Email Corpus(https://www.cs.cmu.edu/./enron/enron_mail_20150507.tar.gz) to accomplish ourexperiments. This data set is consists of 490,745 emails from 150 users. After download
it, the raw data set goes through the process shown in Figure 3.1 from the database to
generate the CSV file for the subsequent experiments. We choose this data set for theexperiment is because it is the most used data set by researchers in the email
classification domain. For demonstrating the AttLDA model's performance, we divide
the dataset into two parts, that is, randomly divided the data set into a training datasetwith 90% and a test dataset with 10%. Besides, in the LDA experiment, the number of
topics should be defined first. Hence, we determine the number of topics based on the
Coherence model results and give a simple topic name to identify it, e.g., Topic 1, Topic
2 and Topic T.

4.2 Experimental setup
We build and deploy Docker containers to the server with python files and Enronemail corpus for running the experiments. Our experiments were performed to evaluate
the performance on a VPC cluster server [85], each node is equipped with an Intel Xeon
E5

2680v2@2.80GHz

20, 64 GB of RAM, running Red Hat Enterprise Linux 6.4.

The proposed AttLDA model was implemented with Python.
The definition of the number of topics is what research generally disapproves of;that is, it is vital to define the number of topics in an objective way. Therefore, before48

beginning to implement the experiments and the evaluation, we apply the Coherence
model [86] to determine this parameter-the number of topics among all the corpus.
Coherence is used to measure a set of statements or facts that could able to support eachother. They will consider being coherence; thus, a coherence fact set can regard as aninterpreted context that covers all or most of the facts. In other words, the words under
the topic are consistent in interpretation. We apply Java-based Mallet to create theCoherence model. According to the coherence measurement to determine the numberof topics, we describe results generated from the Coherence model in Figure 4.1 toFigure 4.6. As we can see, while we set the number of topics as 20, the coherence scoreis the highest. Based on that, we employ 21 as the number of topics. The extra one topic
is a pre-define topic for the stop words since we did not remove stop words from a
corpus for calculating attention value; hence all the Stopwords will conclude into this
topic in our experiment. Estimating the number of topics is the main task we performin the fourth stage of the workflow, as described above in Figure 3.6.

Figure 4.1 The result of the coherence score with the number of topics from 2 to 26.

49

Figure 4.2 The result of coherence score with the number of topics from 28 to 52.

Figure 4.3 The result of coherence score with the number of topics from 54 to 76.

50

Figure 4.4 The result of coherence score with the number of topics from 78 to 100.

Figure 4.5 The result of coherence score with the number of topics from 102 to 124.

51

Figure 4.6 The result of coherence score with the number of topics from 126 to 148.

4.3 Evaluation metrics and comparison
Most natural language studies evaluate their proposed model by perplexity todemonstrate the performance. The most common way to evaluate a probabilistic model
is to measure a held-out test dataset's log-likelihood. In our experiment, the test dataset
is a collection of unseen emails. The perplexity is used to measure a model's uncertainty,
based on that to evaluate the goodness of model fitting. If the value of perplexity islower, in our case, it indicates the proposed model can able to make a correct predictionof a topic for the words effectively, and those words have never seen before. In thefollowing perplexity formulation as Eq. (25), it is necessary to take into account thelocation of each word. The definition of the location of each word is described as theabove Eq. (18), the location 𝑙
the kth line 𝑘of word 𝑤

is calculated based on the nth word ofand the cumulative characters 𝑚until the xth word 𝑤

of kth line

appears in document d.
In the case of training stage that AttLDA model generates a new document 𝐰 by
firstly drawing a document-specific topic distribution. Next, drawing a topic
assignment for each word. Lastly, observed the words as describe in below Eq. (25).

52𝜃~𝐷𝑖𝑟𝑖𝑐ℎ𝑙𝑒𝑡 𝛼
𝑧~𝑝 𝑧|𝜃(25)

𝐰~𝑝 𝐰|𝑧, 𝜙, ℎHowever, a test dataset is a collection of unseen emails, the parameter 𝜃 is not taken
into account as it represents the topic distribution for the document of the training
dataset. Therefore, we will ignore the 𝜃 to estimate the log-likelihood as below Eq. (26).

ℒ 𝒘

log 𝑝 𝒘 |𝜙, 𝛼, ℎlog 𝑝 𝒘|𝜙, 𝛼, ℎ(26)

We first evaluate the log-likelihood of a set of unseen emails given the topics 𝜙and the hyper parameter 𝛼 for topic distribution. The likelihood of unseen emails canbe used to compare models; higher likelihood implying a better model. The
measurement of perplexity of held-out emails 𝒘

𝑝𝑒𝑟𝑝𝑙𝑒𝑥𝑖𝑡𝑦 𝑡𝑒𝑠𝑡 𝑠𝑒𝑡 𝒘

𝑒𝑥𝑝

defined as below Eq. (27).
ℒ 𝒘
𝑐𝑜𝑢𝑛𝑡 𝑜𝑓 𝑡𝑜𝑘𝑒𝑛𝑠(27)

Which is a decreasing function of the log-likelihood ℒ 𝒘 of the unseen emails 𝒘; thelower the perplexity, the better the model. We also define the information rate to makea comparison with previous works as below Eq. (28).
𝐼𝑛𝑓𝑜𝑚𝑎𝑡𝑖𝑜𝑛 𝑅𝑎𝑡𝑒 𝑡𝑒𝑠𝑡 𝑠𝑒𝑡 𝒘

𝑙𝑜𝑔 𝑃𝑒𝑟𝑝𝑙𝑒𝑥𝑖𝑡𝑦 𝑡𝑒𝑠𝑡 𝑠𝑒𝑡 𝒘(28)

In Table 4.1, we summarize the number of words, perplexity performance, and theinformation rate (logarithm of perplexity) in our experiment. Here we compare with theprevious study, McCallum et al. [33] proposed the ART model that distribution overtopics conditioned on both the sender and recipient. Although their results show thatthe information rate is roughly between 8 and 9, there is no basis for defining the ART
model's number of topics. If we consider the number of topics less than 30, the ART
model’s information rate is falling between 9.4 and 9.6. In addition to the ART model,
the result of the information rate from other previous studies is larger than 10 [87], [88].
53Table 4-1 the summarized results of perplexity measurement.
Perplexity
log 𝑝 𝑤

𝜃 ,𝜙,ℎ13706727.076438876

13845.903286

Word

Information

Count

Rate

1437405

9.5357446763

4.4 The discussion of visual attention and keywords
After comparing perplexity evaluation, we selected eight topics with their highest
conditional probability words from 21 topics to run for the Enron email corpus,
respectively, as shown in Table 4.2 and Table 4.3.
Table 4-2 the top 10 probability words in four of eight topics.
Topic 2

Topic 3Topic 5Topic 8

“Accountant”“Evaluation”“Transaction”

“Tax”

blond

0.002003

srrs

0.007141

auction0.011979

deserved

0.010099

xls

0.000239

unreadable

0.002585

shout

0.004261

turbotax

0.005857

accountants

0.000185

finley0.002339

counterparty

0.004146

interrogatories

0.002221

offsetting

0.000130

policies

0.001846

userid

0.002188

expense0.002019

worksheet

0.000119

captured

0.001846

wreck

0.001843

invoiced

0.001514

word

0.000119using

0.001477

pamela0.001727

coordinated0.001413

heather

0.000098

egrb

0.001109

shoemaker

0.000576

nicolevancroft

0.001211

mpeg

0.000087

shout

0.000985

tactic0.000576

update0.000808

invoiced

0.000054

fledgling

0.000985

ditches

0.000576

ledford0.000808

corman0.000043

fischer

0.000861

warman0.000460

shireman

0.000808

The quoted title in the table is we inference and summarize from the keywords.
For example, the words "xls", "worksheet", "word" in Topic 2 are very relevant to word
processing. The words "blond", "offsetting", "invoiced" give us the confidence that
Topic 2 has a high degree of similarity with the Accountant. In Topic 3, the word "srrs"
is the abbreviation of the Social Readjustment Rating Scale, which is a questionnaire
used to evaluate, and we estimate the word "unreadable", "captured" is an action thatpeople use the "srrs" for the evaluation. Finally, the word "policies" might be the results
derived from the result of "srrs", and "fledgling" might be the target to do "srrs"
investigation. Also, in Topic 8, although the word "invoiced" appeared in Topic 2 as54

previous mentioned, however, since the word "deserved", "interrogatories","coordinated" also appeared in Topic 8, we thought that this topic should be a more
serious topic than Topic 2. Furthermore, according to the word "turbotax" and"expense", this serious topic might be related to tax.
Table 4-3 the top 10 probability words in four of eight topics.
Topic 12

Topic 15

Topic 18

Topic 19

“Downtime”“Sport”“Recruitment”“Marketing”

twister

0.009180

game

0.005460

pics

0.007868

emarket0.003823

drinks

0.004590

avi

0.001260

contract

0.006141

beep

0.001982

adventurevillage

0.004590

team

0.001260

fribble0.004989

ask

0.001416

furches0.004590

erica

0.001260

headhunter

0.004797

duplicate0.001132

mead

0.004339

ballpark

0.001120comments

0.004350

customers

0.000991

burn

0.002336

assistants

0.000980

hreich

0.004222

nemec

0.000849

clint

0.002003

queue

0.000840

hello

0.003838

hpln

0.000849

babysitter

0.001418

madhup0.000700

schoene0.003710

ads

0.000849

jean

0.001251

furches

0.000700

letter

0.002175

laporte0.000849

interacting

0.001251

crowd

0.000560

variance

0.001983

concur0.000708

Figure 4.7 the attention value of the top 10 words from all the topics.

55

We identify divergent findings from those words with a high probability of a topic,
and those with high attention value can discover a topic from another perspective. Someof the words with higher attention value than others but did not hold a high probability
in a specific topic as we present in the above table, although their attention value higher
than other words significantly. In that case, from the conclusions of the previous
attention research [89], that attention influences the selection of stimuli of interest.
Hence, we apply the heat maps to demonstrate the value of several words' attention
value in each topic in Figure 4.7; the vertical axis is the word, where the number is theword_id defined by the vector 𝑤. There are several interesting patterns we found fromthis Figure. As we can see in Table 4-3, Topic 12 and 15 are not entirely related to thework; we assume it might relate to the company trip or the club activities. Therefore,
only the word "calfornia" and "message" get a higher value of attention. On the other
hand, the word "calfornia" also gets greater attention while talking about Topic 19"Marketing".
Additionally, there is another interesting pattern while we focus on Topic 2"Accountant" and Topic 8 "Tax". To our knowledge, both topics are severe issues in an
organization. At the same time, we notice that the words "energy", "company", "corp",
and "information" get higher attention value on these topics. Here we think theaccountant covers broad business fields that focus not only on a single product orservice. However, the Topic 8 "Tax", it might get a higher probability to focus on a
specific target, that is why the word "energy", "power" and "gas" get greater attention
in this topic.
Further, it is also reasonable that the word "calfornia" gets lower attention since itgets a higher value of attention while talking about Topic Downtime and Sport.
Moreover, the word time get the average value of attention among topics but do notprovide significant assistance. In the subsequent works, we can consider removing it to
get better performance.

56

CHAPTER 5 Introduction and literature reviews for
implementing the proposed SuccERPAs stated above, Prakash et al. [2] emphasized that collaborative systems addressadministrative work by facilitating the sharing and diffusion of information; however,two fundamental features need to be customized and paid attention to depending on theorganization's goals. Thus, besides the email message, managing the messages andinformation from an ERP system is a noteworthy study. Also, the necessary extensionof the business and an ERP system need to integrate with external systems and share
information keeping a comprehensive manner has become an essential issue in the ERP
post-implementation stage. An ERP system comprises multiple software modules tofacilitate the flow of information in organizations. According to the American
Production and Inventory Control Society (APICS) [90], ERP is a financial accountingoriented information system that improves manufacturing firms' overall performanceby effectively integrating and arranging all the resources, including resources forpurchasing, production, distribution, and logistics operations. Mraz [90] further
indicates that the strategic IT investment of ERP system can help sustain operational
efficiencies in the long run.
However, some scholars have highlighted the considerable disadvantages of ERP
systems. Carton et al. (p. 159) [91] commented that "ERPs are good for storing,accessing and executing data used in daily transactions, but it is not good at providing
information" and "Many [organizations] experience frustration when they attempt touse their ERP system to access information and knowledge". While we try to improve
the shortcomings by considering the integration approach, the implementation does notseem easy. There are quite different characteristics from legacy systems to ERP systems,
including complex, large-scale, integrated, and interdependent [92], [93].
When it comes to ERP, most of the researchers will readily agree to divide the ERP
project life cycle into multiple implementation stages and focus on each stage's different
issues to propose their research. On the one hand, Esteves and Bohórquez [94] claimthat we can classify the lifecycle into three implementation stages, including preimplementation, implementation, and post-implementation. On the other hand,
Swanson and Ramiller [19] propose that the lifecycle consists of three stages: ERP
57

adoption, implementation, and post-implementation. Others even defined various
stages rather than three stages: scope and commitment, analysis and design, acquisitionand development, implementation, and operation [95]. However, several studies havesuggested that existing studies are primarily focusing on the adoption andimplementation stages. The topic of maintenance and upgrading in the postimplementation has received less attention compared to others [96]–[100]. Esteves andBohórquez [94] further present the relevant statistics from the existing research, with
about 47% of ERP implementation studies, compared with only 15% of ERP postimplementation studies. Willis and Willis-Brown [5] point out that once an ERP system
has been successfully built, it has a 'go-live' date; however, implementation is not theend of the ERP journey; the post-implementation or exploitation stage is where the real
challenges begin.
It is clear that research in the post-implementation stage is necessary but lacking.
To that end, scholars have given different opinions on the issues of the inadequacy ofthe post-implementation study. Some studies suggest that the post-implementation
study should toward exploring the Critical Success Factors (CSFs) [97], [100]–[103].
Thus, a portion of the study identified several key factors critical to the postimplementation stage, including support from top management, user training, internal
ERP

team'scompetency,

continuous

process

improvement,

collaboration,communication, continuous systems integration/extension. Whereas, on the other hand,
other scholars hold different views on exploring CSFs. They claim that the relevant
literature remains limited to exploring the success factors and learning issues on the
post-implementation stage [103], [104]. More precisely, Ram et al. [105] suggest thatthe researcher should focus more on improving performance to achieve real benefits as
the success of ERP post-implementation rather than the current literature, which focuseson software selection and implementation processes CSFs.
Our objective corresponds to the suggestions about post-implementation fromprevious research; that is, Hsu et al. [96] assert that for effectively monitoring employee
usage of the adopted ERP system in post-implementation, the managers should selectmeaningful information stores in the ERP system. However, companies usuallyencounter great difficulties maintaining or enhancing (to meet organizational
requirements) ERP systems in post-implementation. To our best knowledge, only a few
studies have put their effort into collecting meaningful data and information from an
58

ERP system and integrating with an advanced information system to let the manager
precisely control the performance and situation of the adopted ERP system. Although
ERP replaces some legacy systems (e.g., accounting, billing, order entry, etc.), it ischallenging to integrate ERP systems seamlessly with other resources [28].
As our expected result, we focus on the post-implementation stage to allow the
managers to realize the effective way to monitor and improve employees' efficiency.
This paper presents the artifact - SuccERP, which can extract completely and convert
data from an ERP system and achieve the interaction between the ERP system and theEnterprise Collaboration Systems (ECS). More precisely, instead of operating in theoriginal ERP system, the user can carry out the ERP procedures in the ECS directlybased on the support of the SuccERP, such as the inventory inquiries, order processing,and bill processing. Meanwhile, we will synchronize all the operations and data of theexisting ERP system with the ECS. Each completed procedure has a corresponding
message in the ECS to allow the managers and employees to do more in-depth
communication. Furthermore, as Kwon and Lee [106] demand, the development of
SuccERP will be along with the algorithms to present the details of our proposed system.
In short, our demonstration also includes the pseudo-code to provide detailed steps andfunctions.
Afterward, we review the ECS since the SuccERP implements the integration
between ERP and ECS. Later, a brief overview of the ERP system mostly focuses on
the post-implementation stage. Finally, we review Design Science (DS) to interpret how
we apply it to achieve the design, implementation, and evaluation of the proposed
artifact-SuccERP.

5.1 Literature reviews of Enterprise Collaboration Systems(ECS)
From the 8C Model for Enterprise Information Management description, we can
explicitly get the ECS outline. Employees can achieve collaborative works such as
information and content sharing, communication, cooperation, and coordination
through all areas of collaboration covered by ECS. On the whole, ECS is a softwaresystem that supports employees' collaborative work [107]. More specifically, the59composition of ECS consists of Enterprise Social Software (ESS) components (e.g.,social profiles, tags, and blogs) with traditional groupware components (e.g., email,calendars, and document libraries). We can apply the ECS to support internal business
communication, collaboration, and content and knowledge sharing activities [108].
Previous studies think highly of ECS, as Schubert and Glitsch [3] suggest that ECS
has been seen as an essential enabler of the modern digital workplace. Unfortunately,
research using ECS for further exploration is relatively fragmented and rarely provides
in-depth empirical studies. Besides, ECS challenges are multifaceted and, therefore,
often require to be addressed in different ways [108].
Schubert and Glitsch [3] depict ECS implementation projects' scope and possible
conditions considering 26 case studies on ECS introduction projects. Although thesmall scope of cases limits the study's findings, they ensure that ECS is a suitable tool
for longitudinal work according to identifying cases and scenarios. In other words, byusing ECS, from the manager to the employees that the members of an organization canwork effectively.
Greeven and Williams [109] identify the challenges and issues while organizations
encounter during the introduction and use of an ECS. They propose five adoption
challenge areas, including culture, business/operation, technology in use, benefits, andattitude/behavior according to academics literature and interviews. Significantly, they
indicate the lack of activity in the period of ECS adoption that the ineffective content
causes it. While ineffective content contributes to this problem primarily, low quality
and insufficient usable content are also important factors. As a result, it leads theemployees to move to other alternatives (e.g., email, previous generations of groupware
software) that demonstrate the weak points from adopting the new system.
Diehl et al. [110] suggest that the laissez-faire approach cannot get the fullpotential to project success. Besides avoiding the laissez-faire approach, ECS is social
software that social software presents cultural rather than technical challenges. These
cultural challenges predictable, and we should manage and control them beforehand,
not ad hoc. Most importantly, the adoption of trading systems such as ERP is almost
mandatory, and the use of ECS is usually voluntary.
Schwade and Schubert [111] first refer to the findings from Herzog et al. [112]work, which introduces four methods with metrics to measure the success of ESS fromthe usage analysis. Later, based on that, Schwade and Schubert suggest considering the60database queries and log file analysis to measure the usage of ECS, for database queries
and log file analysis, the number of posts, visits, creates pages per day or the averagetime per user per visit from Web Analytics are suggested.
From the above literature reviews of ECS, we highlight the views below:
1.

Although ECS effectively supports collaborative work, the content of the
ECS determines the user's attitude, either animated in using ECS or moving
to alternatives. Hence, how to make the ECS content more significant is an
important issue.

2.

In the attitude’s discussion of the users between ERP and ECS, ERP is almost
mandatory, and ECS is usually voluntary. These outcomes open the door tostudies that the possibility of combination or integration with ERP and ECS.

5.2 Enterprise Resource Planning (ERP)
Hasan et al. [103] propose an ERP success measurement model with seveninterrelated, interdependent success factors that investigate the critical factors andevaluate the ERP's post-implementation stage performance. The research is motivatedby the research trend that an enormous amount of research has been devoted to finding
various factors for successfully implementing or adopting ERP.
Equally, from Ha and Ahn proposed model [113], they have identified six factorsthat influence the performance of ERP in the post-implementation stage, including top
management support, competency of an internal ERP team, user training, interdepartment collaboration and communication, continuous process improvement andcontinuous systems integration/extension. They also emphasize that continuous
improvement in the post-implementation stage has a positive impact on ERP
performance.
As we mentioned earlier, many studies have noticed that post-implementation
research is lacking, and the enterprises have been struggling under this stage. The
following studies explicitly point out the issues; Ali and Miller [100] indicate criticalgaps in current research that only a few works concentrate on the post-implementation
stage. There is no standard methodology that has been devised. Besides, the cost is also
a significant limitation. Willis and Willis-Brown [5] argue that once the ERP system is
set up, it has a 'go-live' date. However, this is not the end; instead, the post61

implementation stage's development process is a big challenge. Such proposals areanalogous to other studies. Peng and Nunes [114] figure out that organizations often
encounter various risks such as technical pitfalls, emergent business needs, inadequate
user behavior, and poor system design when developing, maintaining, and enhancing
the ERP system.
In discussing the literature review on post-implementation, one controversial issue
has been what kind of studies should emphasize. On the one hand, Osnes et al. [102]argue a need for more research on Critical Success Factors (CSFs) in the ERP postimplementation stage. On the other hand, Amid et al. [32] contend current research has
focused on software selection, implementing process, and CSFs rather than the success,actual benefits, and performance improvements of the ERP post-implementation stage.
Others even directly claim the relevant literature is still limited to explore CSFs andlearning issues in the post-implementation stage [103]. Our view aims to improve ERP
performance by data integration and the sharing of information between theorganization's systems and business processes. We propose a complete development
process to let those enterprises lack experience and guidelines for continuous
improvement and enhancement in the post-implementation stage, preventing them fromencountering various risks appropriately, such as technical aspects. We have arranged
the descriptions of how to develop, enhance, and upgrade the ERP system after
implementation in the following Table 5-1.
Table 5-1 the arranged suggestion for the post-implementation stage of ERP.
Keywords / Source
Internal

hosting.

[115]

Short conclusion/ From this report, the ratio of hosting options between theexternal and internal is 22.6% and 77.4%. The reasons whyorganizations do not select cloud ERP include:(i)

Risk of security branch (27.27%);(ii)

Risk of data loss (31.82%);(iii)

Lack of information/knowledge about offerings(40.91%). Another point is that the more employees
use cloud ERP, the more licenses must purchase.

Functional upgrade,

There are two functional roles of an ERP in organizations:

Enhancement. / [116]automating and informating. Automating is considered to62Automating,be an essential function and has been thoroughly utilized toInformating. / [117]– date. Informating role is defined as all the information[120]

generated during the work process using an ERP system andtranslating the description of activities, events, and objects
into data to support work integration and decision support.
The potential of an ERP depends on the informating roleand data integration throughout the enterprise.

Information sharing,

In order to solve unexpected projects, we need to develop

Communication,

an ERP system continuously. Meanwhile, both theMonitoring.

/[96], continuous system development and ongoing business[101], [121], [122]process monitoring were also identified as the ultimate
success factor. Part of the studies involves the achievementofinter-department

information

sharing,aspoor

communication is considered one of the reasons for failurein the post-implementation stage. Besides, ERP systems arecomplex in nature, and the utilization of such complex
systems often necessary to rely on external IS services in
the post-implementation stage. Therefore, it is reasonable toassume that for higher extended use, the managers should
be mindful of selecting metrics and information to monitor
the employee's use of an ERP system. Moreover, keep these
metrics and information accurate, updated, consistent,
relevant, complete, and format is easy to understand.

63

5.3 Design Science (DS)As stated above, most ERP researchers have focused on the subject of study inbehavioral science research. We consider the theory-oriented cumulative knowledge
base as a set of statements that summarized the critical factors and lessons learned from
ERP research. The theoretical basis is the logical concern and product of both design
and behavioral science scholars. Aboulafia [123] specifies the relationship between
theory and artifact that the truth (justified theory) and utility (practical artifacts) are twosides of the one coin, and the scientific research should consider its practical
implications for evaluation. Lee [124] elaborates that technology and behavior are notdichotomous in an information system; they are inseparable. To some degree, it also
supports the perspective that the theory and artifacts are equally important.
Simon [125] advocates the natural sciences and social sciences' attempt tounderstand reality. Design science attempts to create things that serve a human purpose.
The DS paradigm has its roots in engineering and the sciences of the artificial.
Hevner et al. [126] describe the DS research in information systems by proposed
a conceptual framework that consists of seven guidelines: Design as an Artifact,
Problem Relevance, Design Evaluation, Research Contribution, Research Rigor,
Design as a Search Process and Communication of Research. Besides, they also
summarized two fundamental questions for DS research: 'What utility does the new
artifact provide?' and 'What demonstrates that utility?'.
Peffers et al. [127] propose and develop a design science research methodology(DSRM) for implementing the Information System. There are six activities in the
DSRM includes: Problem identification and motivation, Define the objectives for asolution, Design and development, Demonstration, Evaluation, and Communication.
Besides, there are multiple possible entry points for DS research; they show several
cases start from different activities to carry out.

64CHAPTER 6 Design and the Development of SuccERPThis section will refer to the six activities of the DSRM proposed by Peffers et al.
[127] and seven guidelines addressed by Hevner et al. [126]. Davenport [6] further
explains that an ERP system's heart is a central database used to draw the data and feedthem into a series of applications to support the organization. The flow of information
could throughout a business in an organization based on a single database. In order toprovide more substantive guidelines of how we built our artifact based on the activities
and guidelines to integrate with an ERP database, this section begins with the problem
identification and motivation to the evaluation and demonstration. Also, some activities
and guidelines that will moderately incorporate software engineering.

6.1 Problem Identification and Motivation
Software engineering is a widely used discipline, especially in the improvementand development of information systems. The purpose of the software specification
function and the problem identification and motivation of DSRM is similar; therefore,
we apply the user requirements definition and system requirements specification toachieve this work more systematically, which present in Table 6-1 and Table 6-2.
Table 6-1 user requirements definition.
Item

Descriptions

1.

How to enable an ERP system to communicate with external resources
regardless of internal hosting or external hosting.

2.
3.

How to provide an efficient way for a manager to monitor the operating
performance of ERP.
How to make ECS content more compelling (improving ineffective issues).

4.

For SMEs, how to strike a balance the enhancements and costs.
The items of Table 6-1 mainly refer to the literature review in the previous chapter,

where we outline the items one-by-one below.
For item 1, we first consider the ratio of hosting options between the external andinternal, 22.6% and 77.4% from the ERP report [115]. Hence, the post-implementation
stage studies are impractical if without considering the issues of the ratio of internal65hosting still high. Further, although Liu et al. [128] identify the necessity for integrating
an ERP system with other external resources, we have no choice but to face a growing
challenge for developing and enhancing an ERP system. When implementing
integrating an ERP system with other external resources, applying to both internal andexternal hosting is a more practical and complete solution.
Next, we consider both item 2 and item 3 at the same time. To some degree, the
ECS and ERP can support the weak points of each other. Most organizations are
struggling to share information effectively from an ERP for communication. Oseni etal. [129] show that for attaining ERP-based operational effectiveness, it needs to relyon the present advances in information and communication capabilities. Al-Mashari[121] presents that considering ongoing business process monitoring of ERP as asignificant stage for completing a full process of ERP implementation.
Meanwhile, Hsu et al. [96] also suggest that managers consider meaningful
metrics from the ERP system to monitor and test the performance from employees’ useof an ERP system. As a result, selecting practical information for the managers tocommunicate and monitor is crucial in the post-implementation stage. Although the
ECS can achieve collaborative works such as communication and suit for thelongitudinal work, the ineffective content is the primary reason for the lack of activity
in ECS adoption [3], [108], [109].
For item 4, Weston [130] and Elragal and Hassanien [131] present that the costmatters in determining whether the SMEs should carry out upgrade/enhancement or not.
Peng and Nunes [114] further figure out that organizations often encounter a variety ofrisks of techniques when developing or enhancing an ERP system in the postimplementation stage. Hence, for small and medium enterprises (SMEs), bothenhancements and functional upgrades require development costs and extra license
costs with the increase in the number of users. Therefore, an explicit guideline of
upgrades and enhancements can prevent encountering the risks of techniques; also, thecost of self-exploring and consultant charges can significantly declaim.
After proposing the user requirements definition, we summarize the system
requirements specification to indicate how we define the proposed artifact's functionsin Table 6-2. In the latter section, by going through the practical snapshots to show how
we implement the system requirements.

66

Table 6-2 system requirements specification.
Item

Descriptions

1.

Regardless of internal or external hosting, the artifact needs able to access an
ERP database.

2.

To collect the database schema and information of tables from thecorresponding procedure of an ERP system.

3.

Provide the user interface in the artifact to allow the users to carry out the
ERP procedures and deliver the data to external resources that allow the
managers to monitor and evaluate the performance of employees.

4.

For SMEs, how to strike a balance the enhancements and costs.

5.

Synchronize the data between the external service and an ERP system to let
all the members get information timely, whether the members access external
service or an ERP system.

6.2 Define the objectives for a solution
Our work focuses on enhancement and functional upgrades in the postimplementation stage of an ERP system, especially communication and monitoring.
The target is the SMEs because compared with a large enterprise, they have fewer
resources. This research expects to create integration with the external resource tostrengthen the communication and monitoring of an ERP, which led us to consider
internal and external hosting issues. Also, synchronize the data between ERP and an
external resource via artifact, and provide the user interface to enable users to carry out
ERP procedures in an external resource.
We select ECS as the external resource and interpret the entire development
process for creating the integration between ECS and ERP system by the proposed
artifact. The reason to select ECS is describing below.
(1)

ECS is a suitable tool for longitudinal work [3]; such a characteristic is
ideal when an organization manager needs to monitor employees'
performance.

(2)

The adoption of an ERP system is almost mandatory, and the use of ECS
is usually voluntary [110]. To our knowledge, with voluntary that theresulting content is relatively casual. It might be the primary reason for an
67

ECS with lower quality and not enough usable content [109]. However,
ERP data sources are mandatory and valid that can improve theseshortcomings of an ECS.

Figure 6.1 the architecture of proposed artifact-SuccERP.
We illustrate the architecture diagram in Figure 6.1, which provides a more
applicable consensus of how the integration between the proposed artifact-SuccERP, an
ERP system, and an ECS interact substantially. There are four kinds of roles in thearchitecture diagram, including the ECS external user, ERP external user, ERP internaluser, and developer. It is worth mentioning that there are two primary actions in thisarchitecture, and we used the arrow with different colors for recognizing.
The blue lines are used to show an action that grasps the database schema andcorresponding tables info, then inserts into the SuccERP database for achieving theactions represents with green lines. We describe it step by step below.
Step 1 and Step 2: The developer initializes the Reverse Engineering Service(RES) by invoking the SuccERP Application Programmers Interface (API).



Step 3: The RES is ready to grasp and get the database schema andcorresponding table info with the Database Profiler after initialization. We
applied the Database Profiler to grasp the Database and Application server's
68information and rules after the ERP user carried out the ERP procedures in
Step 4.


Step 4: In this step, the ERP system's specific procedure is carried out by theclient in both external hosting with a physical address and internal hostingwith a dynamic address.

Step 5 and Step 6: After receiving the ERP users' requests, there is a seriesof data processing between the ERP database and server.



Step 7: Then, RES grasps and receives those necessary schema and table info.

Step 8: The RES insert the schema and tables info of the ERP database into
the SuccERP database.

Step 9 and Step 10: The RES returns the confirmation message to the
SuccERP API and the developer.

We turn to the green part of Figure 6.1, where we describe how the ECS users withthe proposed artifact-SuccERP to carry out the ERP procedures with the ECS directly.


Step 1 and Step 2: The ECS users will deliver the request for executing thespecific ERP procedures to the SuccERP API via the ECS API.



Step 3 and Step 4: While the SuccERP API receives the request from the
ECS API, then the SuccERP API will request the database schema and tables
info from the SuccERP database to execute the corresponding procedures inthe ERP database.

Step 5: After completing the data processing from the SuccERP to an ERP
database, sending a confirmation message to the ECS API.



Step 6: Create a corresponding message in the ECS system for further
communication and the practical content show in Figure 7.6.



Step 7: Upload the relevant report files to the stored files server.



Step 8: Return the confirmation message to the ECS users.

The blue and green parts are two independent actions. Typically, the blue part
executed at the beginning only, and we describe the subsequent operations from the
ECS users as the green part, such as order creation, inventory inquires, and bill ofpurchase.

696.3 Design and Development: The solution for the hosting
issuesRegarding the keywords of Internal hosting from Table 5-1, as well as item 1 ofTable 6-2 for defining the system requirements, we apply Figure 6.2 to interpret the
productive solution. First of all, the limitation on the ERP for internal hosting is withouta physical address for identification. In other words, SuccERP API must be able tomove to the ERP local environment and hold the ability to connect with the ECS API.

Figure 6.2 the architecture for internal hosting of SuccERP.
The key point in Figure 6.2 is the SuccERP API that deploys in the internal hosting
environment with the ERP system. In internal hosting, users are only allowed to accessthe ERP with a dynamic address. As mentioned earlier, in Figure 6.1, the action
constructed by blue lines is only executed at the beginning to recognize the database
schema and tables info of the ERP database. Hence, in Figure 6.2 that we only describe
the action constructed by green lines. Besides, the servers and services relevant to theblue lines turn to the semi-transparent color in the diagram that they have no connection
with this solution.
70

The users will access ECS and SuccERP API via the web browser as a local client.
Here we describe the steps that differ from Figure 6.1.


Step 1: We divide this step into two parts, mainly; Step 1.B: which is used torequest the initial data from a client user of ECS for keeping those data in thelocal environment.



Step 2 to Step 4 is used to describe the ECS user's requests to carry out the
ERP specific procedure in ECS.

Step 5 to Step 7 is used to create the ECS's corresponding message after thedata-processing in the ERP database is completed.



Step 8 will send a confirmation message to the ECS user after completing the
ECS message creation.

In summary, the SuccERP API can change the role according to the environment;
the role of SuccERP API in Figure 6.1 is as a bridge that ECS API delivers the data to
the ERP database by the SuccERP API. On the other hand, under the local
environment's limitation, it can be treated as the only pathway for communicating withexternal services, as we describe in Figure 6.2.

6.4 Design and Development: Collect the outline andschema of ERP Database
The Reverse Engineering System (RES) is an initial point under the SuccERP
architecture; also, it is the heart of the action constructed by blue lines in Figure 6.1. By
invoking the Database Profiler, the RES used to request and grasp the statements
executed in the ERP database from the ERP users login into the ERP system to carryout the specific procedures.
We illustrate in Figure 6.3, which consist of two kinds of swim lane for thecomparison. The bottom part of Figure 6.3 is the general ERP process without SuccERP.
On the contrary, the upper part is in the case with the SuccERP. The essential part isverifying the collected schema and table info able to select the relative data from the
ERP database according to the two decision processes in the swim lane.

71Figure 6.3 the swim lane diagram of Reverse Engineering System (RES).
We consider multiple ERP systems to verify whether the RES is well working or
not and mention those substantial parts below.
Part 1: First, before the user login into an ERP system, the developer
initializes the Database Profiler to waiting for getting the statements andcorresponding authentication schema by invoking SuccERP API. After that,collect the authentication schema and relevant table info into the SuccERP
database during user login into an ERP system. The schema and tables info
is the basis for subsequent integration between SuccERP API and ERP system.
There are two common scenarios of authentication schema.


We describe the first kind of scenario in Figure 6.4. The authentication
data is collected in an independent database Auth*, and the login data ofeach user is preserved in a table of the database Auth*. The point is, theysummarize all the authentication data in a database and determine which
companies that a user allows accessing according to the specific column(i.e., MA003) of the table dbo.Table N. Also, each company has anindependent database.
72

In Figure 6.5, the second scenario is distinct; they separate theauthentication data into two parts. First, they collect all the gatheredcompany list in table dbo.Table M in the independent database System.
Subsequently, it determines whether a user is allowed to access by table
dbo.comPerson from a database of each company.

Figure 6.4 the schema of authentication in an ERP system.
Part 2: Then, we make the short conclusion of the initialize data schema in
Table 6-3. We can categorize the initial data into ten categories: user
department, company info, warehouse, items type, items collection, sheet rule,invoice, currency, customer & supplier, and tax. To our knowledge, most ERP
systems are following this category to initialize the necessary data. Therefore,
we summarize it to let enterprises have these rules in hand for upgrading or
enhancing the post-implementation stage.

Part 3: Finally, regarding the execution of the specific procedures, weconsidered the four most frequently used procedures, including order creation,
bill of purchase creation, bill of sale creation, and inventory inquiries. Equally,
we arrange the comparative tables associated with these four procedures inTable 6-4.
73

Based on RES, researchers and developers can get the scope of an ERP database.
In our perspective, this is an essential and first step for the post-implementation stage.
Especially in the authentication part, there is a significant difference between different
ERP systems. Next, we describe how ECS integrates with ERP while carried out ERP
procedures.

Figure 6.5 The schema of authentication in an ERP system.
Table 6-3 the relative tables and descriptions in initialize data step.
Category

Short descriptions

User department

An ERP system goes through the department of a user to govern
the permission.

Company info

The company info typically includes the company name, unique
identifier code of the company, telephone, fax, address, and theunique identifier code of administrator columns.

Warehouse

There are two dependent tables relevant to the warehouse. One isthe collection of warehouse includes a unique identifier code of74

warehouse and name. The other is used to collect the inventory ofeach item in a warehouse. While we try to carry out the procedureof inventory inquiries, we must identify how many warehouses in
a company first confirm the inventory of each warehouse.
Items typeThe items typically comprise finished goods inventory, semifinished products, and defective products, Etc. Besides, a single
warehouse might comprise multiple types of items.

Items collection

The items collection table typically includes a unique identifier
code, name, specification, items unit, item type, and unit pricecolumns. In some cases, they summarize all the inventory fromeach warehouse as an on-hand inventory in this table.

Sheet ruleThis table is used to indicate how to generate the unique identifier
code of each sheet.

InvoiceThis table is used to preserve invoice records of purchases andsales, including invoice type, tax rate structure, and tax namecolumns.

Currency

Each company might have multiple options on the currency. This
table typically includes the currency name, rate, and a unique
identifier code column.

Customer,

These two data are similar; the customer and supplier data are

Supplier

preserved in a single table in many cases. It typically comprises aunique identifier code, name, liaison, tax ID number, telephone
number, fax number, and address columns.

Tax

This table collects a list of tax types. It comprises the tax rate
value, name of the tax, and a unique identifier code column.

75

Table 6-4 the relative tables and descriptions in the step of four specific ERP
procedures.
Category

Short descriptions

Header of an order, Each sheet with a single header-only and the important
Header of a bill.

columns includes order serial number, date, and identifier
code of customer or supplier, currency, an address fordelivery, tax type, tax fee, sum total, and sum quantity.

Content of an order, At least one record per sheet in the content and the criticalContent of a bill.

columns include order serial number, index number, relevant
data of items, and relevant data of warehouse, quantity, unit,
unit price, and subtotal.

Log data of an order, While the user inserts or updates a sheet, there is a record
Log data of a billinsert into this table. Typically, the crucial columns are
similar to the tables of the header category, as describedabove.

Inventory

There are two kinds of the scenario for recording theinventory of each item. One only records the inventory of
items in each warehouse. The other is beside the inventory ofeach warehouse's inventory, and record all the inventory ofeach item on-hand in another table.

6.5 Design and Development: The integration between
ECS and ERP.
Let us briefly look back on why we want to integrate with ECS and ERP from theconclusion and suggestion of previous works. First, the ERP system contains muchinformation sufficient for management. However, collaborating and communicatingwith the ERP system is partly ineffective, much less testing employees' performance.
While ECS has the advantages of facilitating communication and collaboration, it
usually contains ineffective and useless content. We believe that ECS and ERP offset
each other's lack and settle ERP's severe issues in the post-implementation stage.

76

Figure 6.6 Use case diagram and the illustration of SuccERP.
In Figure 6.6, we present the detailed integration between ECS and ERP system
via the proposed artifact that is the more interpretive version of Figure 6.1, especiallyin terms of processes and data flow. From our previous work, Lin et al. [132] presented
SuccMail (https://succmail.com) as an ECS to achieve the research purpose of theintegration between ECS and ERP system as an initial exploration. Regarding the useof ECS, we can discover some interesting features. The application of an ECS is hardto prescribe, and it is not easy to develop manuals or guidelines for its utilization. From
the theory on the social construction of technology (SCOT), we can explain that theevidence of the ECS is open to interpretative flexibility [133]. The practical cases definepart of the features. For example, the software IBM Connections provides users withmultiple options for creating templates of activities with a list of tasks. Creating such atemplate is simple, but the areas of use are endless.
Equally, during our research, we found evidence for such a task by integration with
the ERP system and the SuccMail. We identify these areas for the use case to implement
the specific purposes in an enterprise and require the employees' individual creativity.
As illustrated in the right-bottom corner of Figure 6.6, the ECS software SuccMail
provides managers and employees with the possibility of communicating/collaboratingwith each in the lists of enterprises they took part in.
77With the SuccMail, the managers will pre-define several discussion groups thatgive an initial name to reveal the group's expected topics. People assign each discussion
group with different task orientations that compose members according to what they
are in charge of in an enterprise or organization. Each member can assign the receiver
while they create a message within a discussion. Each message comprises a subject(title and content) and multiple replies similar to the structure of an email.
By supporting from the SuccERP, the relevant data of the specific ERP procedures
will be fully synchronized to a message of a discussion in the SuccMail, such as orderdata. For communication, it allows the managers and employees can create replies to
do further discussion and collaboration. Considering the suggestions from Kwon and
Lee [106], we present the pseudo-code below and the functions of each table of the ERP
database.

Figure 6.7 Pseudo-Code: synchronize data while accessing a message in ECS.

78

The Pseudo-Code scenario: synchronize data while accessing a message in ECS
in Figure 6.7. While a user accesses a created message in the SuccMail, the ECS API
will send multiple requests to the SuccERP API to determine whether it needs to update
the message. Although we conduct the SuccMail as the example of the ECS system inour research, the Pseudo-Code: synchronize data while accessing a message in ECS we
present in Figure 6.7 is not limited to specific systems and programming languages.
It begins at the user accesses a created message; the ECS API will apply thefunction CONN_SERVER () to confirm whether it can create a connection with the ERP
database and ensure the ECS user has permission to access the company of an ERP. It
is

worth

mentioningthat

consideringinternal

hosting.

We

apply

the

function GENERATE_HOST_URL () to determine the way of connection with the ERP
database.
Next, the ECS API delivers an ECS user's user data as a parameter to thefunction USER_BINDING () for requesting the user binding, typically depends on theemail address. Each created message in the ECS will record the unique, identical codeof ERP company, which is the parameter for applying the function COMP_BINDING() for identifying which ERP company is the target and accessing it for subsequent
requests.
Then, according to the result of the request from the function GET_SHEET_DATA() to update ECS's corresponding message and the binding info. Finally, each sheet (billororder)

reportwill

removeandgenerate

a

new

one

by

the

function REFRESH_REPORT_DATA (), if the sheet has changed. The format of thereport is in PDF file.
Compared with the Pseudo-Code: synchronize data while accessing a message in
ECS in Figure 6.7, the Pseudo-Code: sheet creation in Figure 6.8 is more complicatedthan the previous one. Hence, we will specifically explain the function withcorresponding table categories, summarized in Table 6-3 and Table 6-4. Besides, ourdescription will begin from function SELECT_DISC () because we skip the same partsin Figure 6.7. The Pseudo-Code: sheet creation in Figure 6.8 mainly describes sheet
creation, including order creation, bill of purchase, and bill of sales creation.

79

Figure 6.8 Pseudo-Code: sheet creation.
Function SELECT_DISC() is used to provide a list of discussion groups for
the ECS user, and the user assigns a discussion group to the creating message
associated with the ERP sheet in the ECS.

Function INITIALIZE()is used to read all the necessary initial data from the
ERP database via the API of SuccERP. These data are summarized into
categories, as shown in Table 6-3, and we discuss the critical differences fromvarious ERP systems while initial the data.
(1) Company_info: It is used to show the information on the current ERP
Company connected. In our case, reading the information of ERP
Company is not only at a particular point in this time (initializing) and80

beginning to generate the report data after the sheet creation. Other users
might

modifythe

ERPsystembetweeninvoking

the INITIALIZE() function until submitting the sheet creation request. In
other words, our artifact will send the request to get the company info
again to ensure the data consistency between the ERP system and ECS.
(2) Warehouse: Regardless of order, bill of purchase, or bill of sales
creation, the content items must be associate with a warehouse. Hence,a sheet might relate to multiple warehouses. While we do the inventory,
inquiries, or adjustments need to refer to the warehouse data. As
describe in Table 6-4, there are two kinds of scenarios. While an ERP
system is with recording all inventory of each item on-hand in anothertable, that the ECS API will sum the inventory from each warehouse andupdate the corresponding record in that table.
(3) Items type: The items type contain finished goods inventory, semifinished product, defective product, Etc. A single warehouse might
comprise multiple types of items.
(4) Sheet rule: The rules of identical code of an order or bill. Generally, thecode consists of 4-digits for the year, 2-digits for the month, 2-digits forthe date, and 4-digits indicating how many orders or bills were createdin a day. In most ERP systems, they record their rule in a table. Somedefined the rules in the ERP system instead of the table; in that case, thedeveloper needs to check the rule manually.
(5) Customer, Supplier: The object of an order is a customer, and theobject of a bill is a supplier. In some ERP systems, they used different
tables to collect the customer and supplier data separately. However,others apply an additional column as a flag to identify the collected dataaccording to the customer and the supplier based on a single table. Hence,
we need to deliver one more parameter while an ERP system collects
both customer and supplier into a single table.
Function GENERATE_SHEET_CODE() is used to generate the latest unique
identifier code according to the sheet rule and the number of sheets createdon that day. According to the sheet type (order or bill), consider thecorresponding table to estimate the number of the created sheet on that day.
81Function SELECT_OBJECT() is according to the sheet type (order or bill) to
provide the customer or supplier list options. While the user selects an object
that the artifact will load the relevant data, including address, phone number,and liaison.

Function SELECT_TAX() most ERP systems provide several options such as
taxable, exemption, and untaxed. These tax options need to take into account
while calculating the total of a sheet.

Function INIT_EMPTY_SHEET_ITEM() is used to generate a new item inthe sheet's content after initializing the data. Our artifact's user interface
allows the user to add a new item in the sheet's content by themselves.



Both

functions GENERATE_TITLE() and GENERATE_CONTENT() areused to generate the message of ECS, and the generated message looks likethe example we illustrate in the right-bottom corner of Figure 6.6. We
organize the sheet from the ERP database into completed message
information via these two functions. These two functions provide more
possibilities to the managers and developers in the post-implementation stage,
such as transferring the ERP data into other languages, building evaluation
up with more metrics/indicators, or keeping the prompting messages forfurther communication ECS.
Function CALCULATE_TAX() is according to the user's selected tax option
to calculate the total and the Value-added Tax (VAT). The tax rate value is arecord in a table as the category tax we describe in Table 6-3.

Function CALCULATE_QUANTITY() outputs a pair of data with the item's
quantity and the corresponding warehouse. It is the necessary parameter for
the following function UPDATE_INVENTORY().



Function GENERATE_REPORT() is used to generate the corresponding
report for each sheet, usually in PDF format.

After the report is generated, the artifact uploads the report withfunction UPLOAD_REPORT() to an ECS side's stored file server.



Function CREATE_SHEET() is used for the sheet creation. The parameters
will separate into two parts: the header and the other is the content. As thecategories of a table that we mentioned in Table 6-4, the SuccERP API willsubmit the parameters for insert, update, or query the relevant table.
82

Function CREATE_MESSAGE() is used to create a message of the ECS for
further communication and monitoring. The two most important parameters(title and content) are generated by functions GENERATE_TITLE() and
GENERATE_CONTENT().



After completing the message creation and sheet creation, the artifact willinsert the log data to the ERP system according to the type of sheet byfunction CREATE_SHEET_LOG().

83

CHAPTER 7 Results, Demonstration and Evaluation of
SuccERPIn this chapter, it begins by using the sequence diagram to describe how thosefunctions of the Pseudo-Code: sheet creation in Figure 6.8 work between API services,servers, and database sequentially. Besides, the demonstration also arranges snapshotsin pairs with each function.

7.1 The Demonstration of proposed SuccERP

Figure 7.1 the sequence diagram for the case of sheet creation.
84

In Figure 7.1, we first provide the physical and dynamic IP options that users candecide how to operate ERP depending on their current environment. Our proposed
artifact - SuccERP has been deployed in the public server. However, as we discussed
earlier, some enterprises struggle with internal hosting issues that might not build aconnection with the public server. Hence, we also provide the guideline about installingand deploying SuccERP within a server in the local environment instead of the case byaccessing the public server. As shown in Figure 7.1, we provide a red label for eachsolution to let the user download the SuccERP installation files and guidelines.

Figure 7.2 snapshot of SuccERP for providing options to decide which type of
connection prefer in the current environment.
After confirming the connection between each service and servers also work well,the ECS API will send a request to the SuccERP API for the user binding that the
purpose is to create and assign the association between these two user data. As we
mentioned earlier, typically, the association is according to the email of the user. As
shown in Figure 7.3, the snapshots show that three companies existed in an ERP system.
The user will choose one of the companies to connect and bind their user information
with the existing user in the ERP system by email address afterward. Then, provide
users with the options of ERP procedures, bill creation, and order creation. Finally,
return the ViewModel of sheet creation as a user interface and related initial data to the
ECS user for subsequent processes according to the selected option.
Figure 7.4 is the user interface for the sheet creation, which allows the user to carryout the specific procedure of the ERP system within our artifact. In this example, theuser has complete most of the content of this sheet. There are three items in the sheet's
content, and it will create the message in the BILL OF SALE group of the EnterpriseA+ company. The user can create more or fewer items by clicking the add button or theremove button. While the user fills all the necessary data of the sheet, after they click85

the submit button, the ECS API will send several requests to the SuccERP API and ERP
database, including creating a sheet, generating a report, creating the logs, and update
the inventory.

Figure 7.3 snapshots including company binding, user binding, and specificprocedures selection.

As shown in Figure 7.5, the ERP system can correctly read the data from its
database after the sheet data submit from ECS API to the ERP database. It confirmsthat the artifact SuccERP able to deliver the data to the ERP database correctly.
Through SuccERP, the ECS owns the ability to provide a User Interface to carry out
ERP procedures within SuccMail.

86

Figure 7.4 snapshot of the user interface for the bill message creation.

Figure 7.5 snapshot of created sheet in an ERP system.
Simultaneously, the related message will be created in the SuccMail, as describedin Figure 7.6. On the left-hand side, it is the discussion list of the Enterprise A+, andthe created message is on the right-hand side.
While the user clicks the submit button as illustrated in Figure 7.5, our artifact willcomplete the data-processing in both ERP and ECS, and the results present in Figure
7.5 and Figure 7.6. Moreover, as we illustrate an example of the right-bottom corner inFigure 6.6, Figure 7.6 is the practical case.
In this example, the message comprises a single subject, and two replies allow theemployees and managers to do further discussion and communication after creating asheet. The subject's content is equal to the corresponding sheet data in the ERP system.
The members (employees or managers) can create the reply to do further
communication on this sheet; as we can see, there are two replies in this example. The
generated report data is right on the bottom of the subject in PDF format that can87

preview online or download. Each subject and reply will show the read or unread statusof all the receipts. The username with a red color shows the user has not read this subject
or reply yet; on the contrary, the username with a black color shows the user read thesubject or reply. The managers can grasp the status of message delivery based on thestatus of reading. Based on the integration between ECS and proposed artifact, each
sheet expands as information flow in an enterprise instead of only stored in the database.

7.2 The Evaluation of proposed SuccERP
Next, we get into the evaluation part. To verify that the proposed artifact-SuccERP
is workable and straightforward to design, we apply the metrics to discuss it further.
Table 7-1 the notation for the evaluation of SuccERP.
Notation

Descriptions𝑛

The number of distinct operators.

𝑛

The number of distinct operands.

𝑛

Program vocabulary.

𝑁

The total number of operators.

𝑁

The total number of operands.

𝑁

Program length.

𝐷

Halstead difficulty.

𝑉

Halstead volume.

𝐶𝐶

Cyclomatic complexity.

𝐶𝐶𝐷

Cyclomatic complexity density.

𝑀𝐼

Maintainability Index.

𝐿𝑂𝐶

Lines of Code.
𝑛

𝑛

𝑛

(29)

𝑁

𝑁

𝑁

(30)

𝐷

𝑁

log 𝑛

(31)

𝑉

𝑛
2

𝑁
𝑛

(32)

88

𝑀𝐼

𝑀𝐴𝑋 0, 171

5.2

𝑙𝑛 𝑉

0.23

𝐶𝐶

16.2

𝑙𝑛 𝐿𝑖𝑛𝑒𝑠 𝑜𝑓 𝐶𝑜𝑑𝑒

100/171(33)
We apply both the Cyclomatic Complexity and the Maintainability Index toevaluate the performance of the proposed artifact. We consider Cyclomatic Complexity
to count the number of independent paths through the source code. McCabe [134]recommends that developers consider the Cyclomatic Complexity of each module anddivide module with the value higher than ten into the smaller modules. Furthermore,
there is another research to suggest the value of Cyclomatic Complexity could accept
as fifteen. However, it should provide a written explanation for exceeding limitations[135]. The Maintainability Index is an index value between 0 and 100. Welker andOman suggest the Maintainability Index value between twenty and one hundred
indicates the program has good maintainability. Between ten and nineteen indicates the
program is moderately maintainable, and between zero and nine, that means low
maintainability [136]. We divide the evaluation of SuccERP into two parts: the front
end and the back end. We regard the back end to process the database operation, andthe front end is the ViewModel in our case, which is used to process the user interface
and the relative logic.
Table 7-2 the metrics of functions in the back end part of SuccERP.
Function

MI

CC

LOC

InitConnection()

43

8

37

GetWarehouseList()

44

8

35

GetTermsList()

44

8

35

GetItemsList()

36

13

55

GetItemsTypeList()

44

8

33

GetMemberList()

42

8

39

GetIdentifyCode()

44

9

34

GetMaxSheetCount()

44

9

34

GetInvoiceInfo()

41

8

43

GetDepartmentList()

44

8

35

GetObjectList()

36

8

62

89

GetCurrencyList()

41

8

43

GetCompDeailInfo()

33

5

73

GetAddressList()

40

8

43

CalculateTotal()

63

1

8

InsertOrderSheet()

29

9

98

InsertBillSheet()

29

9

97

UpdateUserBinding()

49

5

22

SynchronizeSheetInfo()

63

2

11

GenerateSheetReport()

47

4

22

GenerateReportHeader()

82

1

3

GenerateSheetPageContent() 54

6

13

GenerateSheetPageContext() 50

420

As shown in Table 7-2, the back-end part's evaluation shows all the functions havegood maintainability by considering the MI metrics. However, both functions
InsertOrderSheet() and InsertBillSheet()re lower than thirty on MI's value. The reason
is that the function also includes inventory adjustment, log creation parts. We
recommend the two functions for future research into smaller modules if there arefurther requirements. Then, about the Cyclomatic complexity, the function
GetItemsList()is higher than ten but lower than fifteen; the primary reason is thisfunction scrutinizes each item's value and process those invalid values, including nullvalue invalid date-time string and extra space. Next, the evaluation of the front-end part,as shown in Table 7-3. It presents all the functions have good maintainability byconsidering the MI metrics, and the Cyclomatic complexity value is lower than ten. The
result shows that the ECS is relatively straightforward to maintain and low complexity
for expanding applications via the support from SuccERP.
Table 7-3 the metrics of functions in the front end part of SuccERP.
Function

MI

CC

LOC

CCD

D

V

onSheetHeaderView()

47.46

1

30

5.88%

7.57

762.25handleDiscChange()

62.76

1

10

50%

2

152.29handleObjectChange()

37.79

4

62

16.67% 19.72 1673.73

90

handleDateChange()

70.5

1

6

33.33% 2.85

58.81

handleAddressChange()

66.43

1

8

33.33% 2.8

91.37

handleTaxChange()

78.82

1

3

100%

1.16

33

onSettingItemTaxChange()

78.39

1

3

100%

1.14

38.03

onSheetContentView()

57.87

1

15

14.29% 5215.22

onAddItem()

67.41

1

7

33.33% 5.35

100.37

onRemoveItem()

62.78

2

10

40%

7.5

144.67

handleWarehouseChange()

69.29

1

6

33.33% 2.8

87.56

onQuantityChange()

68.3

2

7

33.33% 2.16

71.69

onPriceChange()

58.57

3

16

75%

6

128

onGenerateECSMsg()

33.34

2

84

4.35%

10.4

3066.7191

CHAPTER 8 Conclusion, Implication, Limitation and Future
Work
Some research topics are progressively gaining importance as more and more
employees work in complex and knowledge-intensive environments, such as
Communication, Knowledge Management, and Knowledge Transfer. These topics may
not be linked to each other at first glance; however, they are associated with thecollaboration concept.
There is a noteworthy keyword – information flow, which covers Knowledge
Management and Knowledge Transfer. Alavi and Leidner [137] repressed that the main
challenge for Knowledge Management (KM) is to facilitate the information flow, basedon the assumption that the information flow created by individuals is valuable and can
improve performance. In order to achieve the maximum amount of knowledge transfer,individuals can share knowledge during decision making and also access the stored
information flow to develop group knowledge if necessary. On the other hand,achieving knowledge transfer with organizations can be organized informally in emailcommunications.
It is imperative to have a more appropriate and explicit definition of collaboration,
IEEE's definition of collaboration also emphasizes the concept of information flow andknowledge sharing among systems. The diverse work environment leads to numerous
collaboration challenges; in particular, two features of collaboration, unstructured
collaboration (information collaboration) and structured collaboration (processcollaboration). First, the definition of information collaboration is used to find answers
to the unknown question by utilizing IT tools. An email is undoubtedly the most popularand general tool in information collaboration. Thus, the first direction we took is to
explore the issues encountered in the email domain and improve them. Second, thedefinition of process collaboration is used to allowing business processes to be sharedby sharing common information, structured written rules, and set workflows. Moreprecisely, scholars have identified the integration between ERP and external resources
is one of the most critical fields for strengthening the collaboration and business process.
Thus, the second direction we took is implementing and building the integration
between ERP and ECS systems. Such integration problems become more complex and92

challenging, especially for Small and Medium-sized Enterprises (SMEs). When dealing
with such integration requires a considerable investment of resources, including
technology, finance resources, and workforce. On the technical level, when dealing withdifferent ERP systems, integration needs to consider the database structure and systemarchitecture, as well as the attributes from different data that need to be treated in aheterogeneous way.
Generally speaking, this dissertation has focused on several problems related tocollaboration and communication, especially in the context of email and ERP systems.
Firstly, in Chapter 1, we have introduced the definition and description of collaborationand identified two features: information collaboration and process collaboration. For
information collaboration, we select email as the primary target by considering thefollowing ingredients in order: discovering one of the features in collaboration:
information collaboration, identifying the most popular and general approach to theinformation collaboration: IT tool (email), identifying the crucial issues in email
management domain: email overload, lastly, exploring the potential solutions andresearch direction: topic identification able to enhance the communication andcollaboration, and improve the email overload issue. On the other hand, for the processcollaboration, we select the ERP system as the primary target by considering thefollowing ingredients in order: Identify the orientation of business process
improvement: conducting business process within the single and seamless system rather
than two independent ones, Identify the concept an purpose for implementing system
integration: Eliminating effectiveness communication, coordination and collaboration,
Identify the most fundamental components of an enterprise before implementing
system integration: ERP system.
Second, in Chapter 2, by conducting a review of email research, in which 65% of
the research is concentrating on spam and fishing email detection and 20% of theresearch is focusing on email clustering and relative applications, where the remaining
part is a minority of research considering cross-disciplinary and management related
issues. Next, after acknowledging the current research trend, we found that whether in
spam, phishing, and ham email literature, the topic model's application is showing an
increasing trend. Further, a part of the works conducts the weighting function to assign
varying weights to words to give more attention to essential words during topic
inferences. At the same time, we also take into account the findings from cross93

disciplinary. The theories of cognitive science that each piece of information like anaddress in the form of (x, y) coordinates and the 'WHERE' may serve to recall the'WHAT' while reading a document. The above theoretical grounds in Chapter 2 are what
motivate us to conduct a more in-depth study, which is to propose the Attention
orientation Latent Dirichlet Allocation (AttLDA) model.
Third, in Chapter 3, many email studies have used Enron Email Corpus data thathave been processed into CSV format. However, what we need is as similar as possible
to the format that people read. Thus, we designed a framework for processing the data,from downloading the compressed data to insert it into a MySQL database until
exporting it to CSV format for the following analysis. Subsequently, we review theoriginal LDA model and associated statistical distributions before presenting the
proposed AttLDA model's graphical representation. We further present the data
generation mechanism, i.e., the joint probability distribution of the hidden and observed
variables. As for the estimation of visual attention, we refer to the previous literature todefine the relevant parameters, including the Window size, Line length, and the pseudocount definition. Lastly, we conduct the Collapsed-Gibbs-Sampling to carry out theinference process.
Fourth, in Chapter 4, we describe the preliminaries of the experiment, including
the execution environment, the programming language, and how we split the dataset
into training and test datasets. In the inference of the topic model, how to define thespecific number of topics is a considerable issue. Therefore, we conduct the Coherence
model to estimate the number of topics in our experiment. Lastly, we calculate theperplexity and information rate to measure the performance of proposed AttLDA model,
and further interpret it based on the results of topic inference and the heat map of visual
attention map.
Fifth, in Chapter 5, we introduced another feature of collaboration – processcollaboration and identified the integration of ERP systems with external
resources/systems could enhance the performance of the business process of anenterprise. We also consider the life cycle of the ERP system to get a concrete idea ofwhen to implement the integration between ERP and external resources/systems.
Finally, scholars suggest that the implementation process should be presented as a
pseudo-code to provide a practical way for subsequent researchers and developers.
After identifying the demand for building ERP system integration, we reviewed the94relevant literature. First, we focused on the ECS section to explain why it is the target
to carry out ERP integration, including two primary advantages: it is an essential
enabler of the modern digital workplace and a suitable longitudinal work tool.
Meanwhile, several shortcomings, including ineffective content, are easily generated,making user's usage gradually inactive. The laissez-faire approach cannot get the fullpotential to project success. However, we have found that the ERP system's
standardized content is a complementary component to the ECS. Afterward, we
reviewed the literature related to ERP systems. It is worth mentioning that we haveorganized the findings into a keyword list, such as internal hosting, functional upgrade,enhancement, automating, informating, information sharing, and monitoring. Lastly,
Design Science's belief is the relationship between theory and artifact that the truth(justified theory) and utility (practical artifacts) are two sides of the one coin. The
scientific research should consider its practical implications for evaluation; hence, we
employ the Design Science methodology to implement and build the proposed artifactSuccERP.
Sixth, in Chapter 6, we follow the framework and methodology based on DS
proposed by Hevner et al. [126] and Peffers et al. [127] to carry out our artifact –SuccERP. First, we conduct the user requirements definition and system requirements
specification from the Software Engineering to achieve the "Problem identification andmotivation" section. Then, in the "Define the objectives for a solution" section, we
demonstrate the system architecture of SuccERP to make a further interpretation.
Afterward, in the "Design and development" section, we present how to address theinternal hosting issue by proposing two different system architectures, proceeding tocollect the outline and schema from the ERP database. Lastly, the integration between
the ERP system and ECS is shown by the pseudo-code.
Finally, in Chapter 7, the sequence diagram explains how SuccERP implements
the integration between ERP system, ECS API service, SuccERP API service, SuccERP
database, and Files server. Then, we show a series of snapshots of the practicaloperation of the artifact-SuccERP. In the end, we apply the Cyclomatic complexity and
Maintainability Index to verify the proposed artifact is easy to maintain and develop.
Also, we attempt to answer two fundamental questions proposed by [126] below:(1) What utility does the new artifact provide?

95

First, artifact-SuccERP enables the architecture of both internal and external
hosting. Meanwhile, allow the enterprise to expand the applications withlegacy ERP systems via our artifact. The SuccERP keeps the consistency
between the ERP system and the ECS, not only implement the enhancementof communication but also meticulously sustain all the data in the legacy ERP
systems. In short, SuccERP can be regarded as a bridge to connect with theexternal systems. We proposed the complete architecture and implementation
process in this research.
(2) What demonstrates that utility?
Exploring the CSFs and learning issues is still in the post-implementation
stage; however, most of the results are academic. For the enterprise, they needmore practical cases as a guideline. Hence, we present the complete steps andarchitecture to construct the artifact and present the snapshots and complexity
metrics to show how it works and integrates the legacy ERP system andexternal systems.
Moreover, in our previous research works [138], we implemented and released the
ECS system – SuccMail and employing the questionnaire survey to investigate
how the ECS improves communication and e-management capabilities andperformance improvement of enterprises in Taiwan, especially under the industry
4.0 trend. Likewise, after released the ECS system, we further discuss and explore
the possibility and conception of building integration between the ECS and ERP
systems [132]. We highlight the contributions as below:(1) We believe that this is the first research to construct a post-implementationand show how to integrate the ERP system and ECS using a complete method.
(2) Instead of providing case studies, we present a complete process forinvestigating various ERP systems' structure. It will make our research results
widely available to develop communication between different ERP systems
and ECS.
(3) Truth and utility (practical artifacts) are two sides of the one coin. We list the
weaknesses of both ECS and ERP systems from the literature review toensure this research is based on the theoretical foundation. Also, we consider

96the guidelines of DS to demonstrate how to develop and design the artifact
completely.

8.1 Implication
In this dissertation, we focus on improving the performance of an enterprise's
collaboration and communication, and we work on two features of the collaboration.
To the best of our knowledge, the AttLDA model is the first to take visual attention into
account when the topic inferences. Compared with previous studies, we provide the listof keywords related to each topic and the ratio of attention of the words under each
topic. Meanwhile, in Chapter 3, we present how we define a document as a twodimensional space with knowledge of numerous works of literature, and then further
estimate the location of words and calculate the visual attention by the state-of-the-art
visual attention model – Bayesian model. Besides, in the ERP and ECS integration
section, we have collected numerous keywords and noteworthy issues in the postimplementation stage in Chapter 6. More importantly, we explore multiple ERP systems
to integrate Chapter 7 to identify vital distinctions between different ERP systems. We
believe that our results will help the managers and developers enhance their existing
ERP systems and provide various ERP system architecture to the scholars for carryingout further research works. Furthermore, due to the COVID-19 pandemic, Byrnes et al.
[139] emphasize that the global business urgently needs to conduct new technologies
to enhance communication and collaboration whilst maintaining a physical distance.

8.2 Limitation
There are several inherent limitations of this dissertation as shown in the following:(1) It is possible to receive emails consisting of multiple languages; however, ourproposed AttLDA model is not currently implemented.
(2) In the visual attention model, we merely conduct the TFIDF for the visualfeature part, while more visual features should be considered for future
research evaluation.
(3) In ERP system integration, we focused on Collaboration and Communication
issues, which also limited our integration target to the ECS system, and the97

way to deal with this issue can be discussed from the exploratory stage
towards other factors and develop system integration for numerous issues.

8.3 Future WorkAs future work, two ideas can be considered. The first is to make AttLDA generate
the probability of topics in each email, including the value of each word's attention, andsummarize it to create feature vectors. Based on that, to make further approaches suchas email classification, categorization, or thread identification. The second idea is toimprove the experience of an Enterprise Collaboration System (ECS). While the ECS
is increasingly choosing and follow by the entrepreneur or manager, more and moresharing information and conversation keep accumulating. It will be out of control soon
if without considering how to manage it. We consider our proposed AttLDA as thesolution for dealing with the management of messy messages. In other words, they
provide the suggestion (topic) and give the comprehensible description (attention
distribution) to provide the hint and direction for the users to manage and classify theirmessage in an ECS.

98

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