Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving L2 English Rhythm Evaluation with Automatic Sentence Stress Detection

English is a stress-timed language, for which sentence stress or prosodic stress plays an important role. It’s then difficult for Chinese who are used to the syllable-timed rhythm to learn the rhythm of English [1]. In this paper, we investigate how to improve the rhythm evaluation based on the sentence stress for Chinese who learn English as a second language (ESL). Particularly, we explore some rhythm measures to quantify rhythmic differences among second language (L2) learners based on sentence stress. To relieve the dependency on labeled data of sentence stress, we predict sentence stress automatically utilizing a hierarchical network with bidirectional Long Short-Term Memory (BLSTM) [2]. We evaluate the proposed method based on the corpus consisting of 3,500 sentences recorded by 100 Chinese speakers aging from 10 to 20 years old, which was marked with the sentence stress labels and scored by three experts. Experimental results show the proposed sentence stress measure is well correlated with labeled prosody scores with a correlation coefficient of −0.73 and the automatic labeling method achieves comparable results with the method with gold labels.
Digital Nudging for Technical Debt Management: Insights from a Technology-driven Organization

Today’s fast-growing and ever-changing business environments force software development teams to release high-quality software on a tight schedule. The notion of technical debt (TD) captures the technical compromises wherein software quality is sacrificed for short-term goals. One of the most significant challenges for technical debt management (TDM) is time as a factor of complexity. TD arises from decisions that are favorable in the short term but cause a need for complex and costly actions in the long term. Building on the applications of nudges – the use of small design modifications in choice environments to guide people’s behavior – and on their psychological effects, in this design science research we develop and evaluate design elements of a Technical Debt Management Label (as a TDM nudge) in a technology-driven organization. The TDM label aims to guide software developers’ decisions towards adopting TDM activities that are favorable in the long term.
Artificial Intelligence and Decision-Making: the question of Accountability

Public sector organizations literature has addressed the influence of AI on decision-making process, looking mainly at rationalization and efficiency. However, recent adoptions of AI have been challenged because of their discriminatory nature. As a result, questions emerged on the accountability of AI supported decision-making processes in the public sector. This research sheds light on how AI transforms decision-making processes in the public sector and hence on their accountability. The paper illustrates that AI adoptions lead to the emergency of technolegal entanglements – assemblages – which might impact upon AI accountability. Building on the findings of some of the most controversial and discussed cases of AI adoption in the public sector – COMPAS in the US and UKVI in the UK – the paper makes the case for a new approach to AI supported public sector decision-making accountability.
Investigating the Role of Stakeholders in Agile Information Systems Development Projects: A Mixed Methods Approach

Agile information systems development (ISD) strives for a high amount of interaction between the agile team and stakeholders to ensure that high quality software within commonly defined project goals is produced. The literature has acknowledged that agile ISD significantly changes the work of team members. How do agile practices affect the work of stakeholders? Unfortunately, little theory exists to answer this question. This paper addresses this gap by investigating the effect of agile practices on stakeholders’ job satisfaction. Adopting a mixed-methods approach, we use a review of the literature with an exploratory case study to develop the theoretical model, which was evaluated with a survey among stakeholders in agile ISD projects. We contribute to agile ISD literature by providing empirical evidence on stakeholders’ job satisfaction and highlight the relevance of interaction and collaboration between team members and stakeholders in agile ISD projects.
To Treat, or Not to Treat: Reducing Volatility in Uplift Modeling Through Weighted Ensembles

When conducting direct marketing activities, companies strive to know whom to target with a marketing incentive to maximize the campaign effect. For example, which customer should receive churn prevention incentive to minimize overall churn rate? Uplift modeling is a promising approach to answer such a question. It allows to separate customers who would likely react positively to a treatment from those who would remain neutral or even react negatively. However, while different uplift approaches have been proposed, they usually suffer from high volatility and their performance often depends largely on data set and application context. Thus, it is difficult for practitioners and researchers to apply uplift modeling. To overcome these problems, we propose a weighted ensemble of different uplift modeling approaches to reduce volatility and improve robustness. We evaluate the novel approach against single uplift modeling approaches on multiple data sets and find that the ensemble is indeed more robust.
Semi-Automated Analysis of Large Privacy Policy Corpora

Regulators, policy makers, and consumers are interested in proactively identifying services with acceptable or compliant data use policies, privacy policies, and terms of service. Academic requirements engineering researchers and legal scholars have developed qualitative, manual approaches to conducting requirements analysis of policy documents to identify concerns and compare services against preferences or standards. In this research, we develop and present an approach to conducting large-scale, qualitative, prospective analyses of policy documents with respect to the wide-variety of normative concerns found in policy documents. Our approach uses techniques from natural language processing, including topic modeling and summarization. We evaluate our approach in an exploratory case study that attempts to replicate a manual legal analysis of roughly 200 privacy policies from seven domains in a semi-automated fashion at a larger scale. Our findings suggest that this approach is promising for some concerns.
Comparison of Voluntary versus Mandatory Vaccine Discussions in Online Health Communities: A Text Analytics Approach

Vaccines are vital health interventions. However, they are controversial and some people support them while others reject them. Social media discussion and big data are a rich source to understand people’s insights about different vaccines and the related topics that concern most of them. This study aims to explore the online discussions about mandatory and voluntary vaccines using text analysis techniques. Reddit social platform is popular in online health discussion and thus data from Reddit is analyzed. The results show that different aspects are discussed for different types of vaccines. The discussion of mandatory vaccines is more interactive and is focused on the risks associated with them. Voluntary vaccines’ discussion is focused on their effectiveness and whether to get them or not. The study have important implications for health agencies and researchers as well as for healthcare providers and caregivers.
Introduction to the Minitrack on Streaming Media in Entertainment

Streaming technology is a special delivery method of different media (usually audio or video) from a server to a client and, nowadays, increasingly applied in the entertainment industry (as an alternative to the conventional file download). We differentiate between on-demand streaming (e.g. Netflix, Spotify, YouTube), short-form video formats embedded in social media (e.g. TikTok, Snapchat or “Stories”-feature on Facebook or Instagram) and live streaming (e.g. Twitch, Taobao, YouNow, YouTube Live) [1;2]. On-demand streaming targets a more consumption-oriented audience (with focus on passive entertainment), whereas live streaming offers the possibility for user-generated content and participation between users (which can motivate more active social exchange) [3;4]. This information production and information consumption behavior on streaming media in entertainment has since become an important aspect for monetization, which is applied by brands, micro-celebrities, and streamers alike [2;5]. As the possibilities for viewer interaction on streaming media expand, a new phenomenon emerges: live stream shopping as a form of eCommerce. This special form of consumer-to-consumer streaming grants brands and live streamers the opportunity to engage with customers in real time to market and trade various products and services [6]. Motivations to watch eCommerce live streams range from gathering product information, convenience, as well as interactions with micro-celebrities who demonstrate the products [6]. With this the question arises, how can live streamers increase viewer engagement to possibly support product sales? Liu, Sun and Lee [7] investigate this aspect in their study on viewer engagement in eCommerce streaming. The intimacy theory, used as a foundation for the study, suggests that attachment results from a process that is initiated when one person (i.e., the streamer) communicates authentic and personally relevant information to another person (i.e., the viewer). Survey results show that the streamers’ authenticity, meaning the balance between commercial motivations and sincerity, the customer response capability, meaning the streamers’ ability to respond to the viewers’ requests timely and effectively, as well as, with a slightly milder impact, attitudinal similarity, are sufficient to enhance the intimacy perceived by the viewers. This in turn helps to substantially eliminate mistrust and leads to viewers’ online engagement, which includes behavioral engagement (e.g. sharing information about the product), affective or emotional engagement, and cognitive engagement. Hence, the study gives new insights into the supporting role of streaming media in entertainment for the eCommerce industry.
The Antecedents and Consequences of Legitimate Corporate Participation in the DarkNet: A Preliminary Model

The DarkNet is a purposefully hidden collection of disconnected web sites that are not indexed by conventional or mainstream search engines, are not accessible by conventional means, and requires specialised software, specific knowledge, and authorisation to gain access. While typically associated with illegality and criminality, there are an increasing number of legitimate organizations active on the DarkNet. While explored widely in Computer Science, Humanities and Social Science, Engineering and Medicine, there is a paucity of research in the business and IS disciplines. The objective of this paper is to introduce the DarkNet and DarkNet Commerce, summarise extant business and IS research, albeit limited, and outline a proposed model to explore a previously unexplored relationship in the business and IS literature, namely the antecedents and consequences of the participation of legitimate organisations on the
Do they Play as Intended? - Comparing Aggregated and Temporal Behavioral Analysis in a Persuasive Gamified System

Gamified systems nurture an ulterior goal set by their designers (e.g., a positive behavioral change). Behavioral profiling allows understanding whether users play as intended and reach such a goal. Analyzing in-game behaviors can also highlight unexpected interaction patterns or unengaged users. Current logging systems can track and store any in-game action. However, such high-dimensional data should be carefully processed to retain relevant knowledge while filtering unnecessary noise. Analysts can either aggregate data into a single data point per player or maintain temporal information. This study compares aggregated and temporal behavioral analysis conducted on a gamified system, promoting sustainable mobility (Play&Go). Results show how, in Play&Go, aggregated analysis conveys information on long-term winning strategies, whereas temporal analysis describes short-term strategies. Additionally, studying the temporal evolution of players’ behaviors emphasizes a sharp division among engaged and unengaged users. We show how aggregated and temporal analysis hold a complementary view of players’ experiences.
Design and Development of a Diabetes Self-Management Platform: A Case for Responsible Information System Development

Diabetes Mellitus a prevalent chronic disease that affects people from all genders and ages, continues to grow exponentially with predictions of nearly 578 million people affected by 2030. Self-management, known to be an essential aspect of any care program, can help patients with diabetics to control blood glucose and thereby, reduce the impact and likely complications. However, self-management to date has included the development of digital health solutions which have poor sustained uptake. This is primarily since such digital solutions have a poor fit with patient and clinician needs. In this paper, we propose a digital platform for supporting patients with diabetes. The proposed platform is a work-in-progress research and has been co-designed and co-developed (jointly with patients and clinicians) based on design science principles and includes key aspects of task-technology fit information system theory for further evaluation.
IaaS, PaaS, or SaaS? The Why of Cloud Computing Delivery Model Selection - Vignettes on the Post-Adoption of Cloud Computing

Most large-scale organizations adopted Cloud Computing (CC) on a company level in recent years. Managers now face the challenge to appropriately implement CC "operationally", i.e., for information systems (ISs). We refer to this as post-adoption, addressing the extent of technology usage after adoption. Specifically, managers need to choose among the CC delivery models Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-asa-Service (SaaS). We differentiate the determinants of this post-adoption decision for IaaS, PaaS, and SaaS. Based on this analysis, we derive criteria that guide managers' delivery model selection: Adopt 1) IaaS for ISs requiring flexibility and reduced time to market, 2) PaaS to access specialized resources, and 3) SaaS to focus on core competencies. Moreover, we analyze the impact on the CC strategy and postulate them as recommendations: I) acknowledge the interplay between governance and time-to-market, II) realize cost savings on company level, and III) consider strategically important ISs for CC.
Emerging innovation patterns in digital agriculture: A study of 198 digital solutions from 116 startups

To tackle humanity’s food challenges, agricultural companies need to exploit technological advancements by innovating their business models. Solution business model patterns (SBMPs) play a central role in business model innovation. However, the current literature on SBMPs is outdated and has not been adapted for the field of agriculture. By analyzing 198 digital solutions from 116 agriculture startups, we identify 18 SBMPs, nine of which have not been identified in any previous research. We thus significantly expand the existing literature on SBMPs and outline the technological drivers of emerging SBMPs. Furthermore, putting the 18 identified SBMPs into the context of agriculture will help agricultural companies innovate their business models. Three SBMPs with high potential to tackle humanity’s food challenges are further discussed.
Bridging the Sustainability Leadership Chasm: A Case Study of the Sustainability Advantage Program of the NSW Government, Australia

Business operations worldwide have significantly impacted the environment, prompting organisations to consider how to align strategy and conduct to advance sustainability targets. Collaboration is critical for sustainability progress, thus organisations are becoming interested in the practices of supply chain partners. To incorporate sustainability across networks, leadership is critical in coordinating activities. Although the effectiveness of leadership for sustainability is widely discussed, there is a lack of empirical field studies on how organisations can develop this leadership capability. This research-in-progress paper presents preliminary findings from an industry level case study of a Government-run initiative. This initiative provides a platform for organisations to collaborate and access resources that supports the development of sustainability leadership capabilities. Preliminary analysis provides insight on the dynamic capabilities organisations must develop to influence its supply chain partners, the technologies used to progress sustainability initiatives and the role that the Government has in promoting collaboration and leadership. Implications to both research and practice, and future work are discussed in conclusion.
Designing Warning Interfaces causing Discomfort for Awareness of Risks: Revisited

Making users aware of the risk by giving them a sense of discomfort and helping them not to access dangerous sites is crucial. Thus, we focus on developing a warning interface, causing discomfort, allowing smartphone users to be aware of danger and risks. We studied discomfort feelings while using smartphones and extracted five discomfort factors from a questionnaire survey and factor analysis. We implemented a prototype of warning interfaces for web browsing on a smartphone considering five factors. In the experiments, we have found that three factors out of the five, namely, “Unintended operation or display,” “Sudden changes,” and “Understanding of the application,” are significant for risk awareness, while the other two are not. This paper reports on the findings
Direct Policy Search for Multiobjective Optimization of the Sizing and Operation of Citizen Energy Communities

Citizen Energy Communities (CECs) are increasingly promoted in the European Union and beyond as a form of locally operated microgrids. While traditional microgrid research is often focused on an optimized operation, individual preferences regarding conﬂicting objectives are becoming more important in such communities. In this study, we present an evolutionary algorithm that has previously been used for a multiobjective operation of microgrids and include the perspective of heat consumption and initial sizing decisions using direct policy search. This way, the developed tool can be used by CEC planners to integrate conﬂicting objectives of residents in the installation phase. We introduce the algorithm formulation and demonstrate its functionality on a case study for different ambient conditions. The results show the opportunities to size and operate CECs through the presented algorithm.
The Effect of Operating in Many Realities on Memory: An Experiment on Memory Recognition in Extended Realities

Extended reality (XR) such as VR and AR have been increasingly adopted across domains in cognitively challenging activities such as learning, shopping, and gaming among others. There are a few concerns about the inferior cognitive affordance of XR-mediated functioning, e.g., with respect to memory retention. For better understanding how different XR technologies influence memory performance (e.g., recognition), we examine the effects of VR and AR mediation on the ability to remember (i.e., recognize afterward) text and image-based information based on a 2 × 2 between-subject experiment (n = 155). The results indicate that VR had a negative main effect on text-based information recognition but no effect on image-based information recognition. AR had no significant main effect on the recognition of either information types. Overall, the findings as further supported by the interaction effects analysis, suggest that for memory recognition, it is always best to have a fully physical (no-AR & no-VR) or fully digital environment (AR & VR) compared to having either VR or AR alone.
Techno-Eustress: The Impact of Perceived Usefulness and Perceived Ease of Use on the Perception of Work-Related Stressors

An extensive body of literature elaborates on the negative side of technostress. However, appraising stressors as challenges rather than as threats evidently leads to positive perceptions of stress, namely eustress. We derive from the person-environment fit model that the higher the acceptance of information and communication technologies is, the higher is the perception of eustress. As perceived usefulness and perceived ease of use are proven antecedents of technology acceptance, we study how these two technology beliefs affect the perception of challenge stressors and how the challenge stressors influence the psychological response in terms of perceived eustress. We collected data from 168 employees in a web-based survey and used structural equation modeling. The results support our propositions and confirm that perceived usefulness and perceived ease of use are significant determinants of work-related challenge stressors enhancing the perception of eustress.
An Empirical Assessment of the Impact of Strategy Implementation Actions on Digital Business Strategy

In this paper we investigate the effect of strategy implementation actions (SIA) on the implementation of a digital business strategy (DBS). The result presented in this paper are based on an online survey with 191 responses from senior vice presidents and vice presidents of a large German manufacturing organization. Using structural equation modeling (SEM), we find a positive significant effect SIA on the implementation of a DBS. This is an important implication for academics and practitioners alike, as the actions can serve as implementation guidelines in the future.
Toward Understanding the Technology Trust Calculus in Healthcare: A Generation Z and Millennial View

Generation Z and Millennial comprise 50% of the American population and are considered the savviest users of Information Technology (IT). They are also critical beneficiaries of the transformation of healthcare processes and services enabled by IT. Increasingly, the capabilities to leverage digital healthcare depends on the richness of collected data. Consequently, it is imperative to understand the contextual factors that influence Millennial and Gen Z trust in healthcare IT to disclose personal health information. To address this question, we draw on social cognitive theory, social exchange theory, and privacy calculus framework to propose a healthcare technology trust calculus model. We validated it using a survey study collecting responses from 736 individuals. Findings indicate that although the concern of disclosing personal health information negatively influences trust in healthcare IT, organizational trust, perceived benefits, and risks of health information disclosure have a more substantial effect on it.
Information Protection in Dark Web Drug Markets Research

In recent years, there have increasingly been conflicting calls for more government surveillance online and, paradoxically, increased protection of the privacy and anonymity of individuals. Many corporations and groups globally have come under fire for sharing data with law enforcement agencies as well as for refusing to cooperate with said agencies, in order to protect their customers. In this study, we focus on Dark Web drug trading sites as an exemplary case of problematic areas of information protection, and ask what practices should be followed when gathering data from the Dark Web. Using lessons from an ongoing research project, we outline best practices for protecting the safety of the people under study on these sites without compromising the quality of research data gathering.