Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Deep User Activity Transfer Models for Cross Domain User Matching

With the widespread adoption of information systems, a wide variety of digital data traces of users are exposed. However, as users use multiple information systems for different tasks that are not directly linked with each other, the data will be fragmented or disconnected. Interconnecting these data traces of a user from various information systems is an important research problem known as “User Entity Resolution” (UER). Much of the current UER methods depend on the similarity of user profile attributes and network data. With the decrease in the reliability and availability of user profile attributes and network data, it is important to develop methods that incorporate user activity data to improve UER. In this study, we propose a novel approach to incorporate user activity data in UER. A deep neural network model is proposed as a cross domain transfer model. Given the user activity data from a primary domain, the trained transfer model can be used to generate an estimate for a secondary domain of the user. The estimate could be used to search for the matching user in the secondary domain. Our evaluation results indicate that the proposed model has a far superior ability to rank users.
E-Word-of Mouth for Money - An Examination of the Impact of the Payment Timing on Knowledge Contribution Quality

Knowledge sharing is a determinant for online platforms to run their business. Several strategies have been proposed to increase knowledge sharing, among which monetary incentive is commonly used. While previous studies have found a crowding-out effect of monetary incentive on contribution quality, the research on alleviating this crowding out effect is limited. Drawing upon the autonomy in self-determination theory and the feeling of reciprocity, this study examines the impacts of payment timing of monetary incentives and contents in reminder messages sent by e-commerce platforms on contribution quality. This study intends to provide theoretical and practical implications about motivations on knowledge sharing.
Pricing-oriented Web Technologies and Product Returns in the E-marketplace: The Moderating Role of Seller Reputation

Pricing-oriented web technologies are known as the most important tools for e-marketplace sellers to stimulate customers to buy and increase their sales performance in the purchase stage; however, little is known about their impacts on product returns in the post-purchase stage. Drawing on cognitive dissonance theory and the e-commerce literature on reputation, we developed a research model that incorporates four types of pricing-oriented web technologies, seller reputation and product returns. Specifically, we posit that four common and popular types of pricing-oriented web technologies (i.e., limited time discount, price bundling, shop VIP, and quantity discount) impact product returns differentially. These relationships vary depending on seller reputation, manifested as an overall rating. To validate the research model, we collected a unique longitudinal dataset of 40,000 seller-month observations of 4,000 Taobao sellers in different industries over a period of 10 months
Crafting Agent-based Models to Analyze Service Platforms

New service platforms have emerged in, and transformed, economic activities ranging from hospitality (i.e., AirBnB) to lending (i.e., Lendingclub.com). Understanding and managing the dynamics and functioning of service platforms poses a novel challenge for academic research and managerial practice alike, thus requiring new interdisciplinary research methods and approaches. Here, we address this challenge by demonstrating how agent-based modelling (ABM) can be used to study the intricate nature and dynamics of service platforms. Our contribution consists of a novel conceptualization of service platforms as platform-based service ecosystems, as well as guidelines on how to delineate ABM models of service platforms, including a grid that enables future researchers to study their underlying dynamics and functioning. Finally, our study advances current theoretical and practitioner thinking about how platform-based service ecosystems may enable, transform, and facilitate value cocreation in service.
Spray-Dried Silica Xerogel Nanoparticles as a Promising Gastroretentive Carrier System for the Management of Chemotherapy-Induced Nausea and Vomiting

Purpose The current work aimed to develop spray-dried silica xerogel nanoparticles (SXNs) as a gastroretentive carrier for the dual delivery of chlorambucil (CHL) and granisetron hydrochloride (GR). As a low-density system, it was proposed to float over gastric fluids; allowing for the retention of CHL in the acidic medium where it is more stable while ensuring the solubility of GR. Methods Silica xerogels were developed by sol-gel process, using Tetraethyl orthosilicate (TEOS) water and acetic acid, followed by spray drying. SXNs were evaluated for particle size, zeta potential, entrapment efficiency (EE%), CHL and GR release after 1 hr (P1h) and after 8 hrs (P8h). The best achieved system (SXN4) was evaluated for morphology, pore diameter, total porosity, bulk density, wetting time, floating characteristics. Furthermore, the pharmacokinetics of the loaded drugs were evaluated in rats; relative to an aqueous CHL suspension containing GR. Results SXN4 system had the highest desirability (0.69); showing spherical nanoparticles (181.63 nm), negative zeta potential (−5.18 mV), promising EE% of 59.39% and 73.94% (for CHL and GR, respectively) and sustained CHL and GR release profiles characterized by low P1h (22.75% and 30.74%) and high P8h (60.36% and 99.33%), respectively. It had a mean pore diameter of 8.622 nm, a total porosity of 62.27%, a bulk density of 0.605 g/mL, a wetting time of 292 sec, zero lag time and a floating duration of at least 8 h. Conclusion The prolongation in the mean residence time (MRT(0-∞)) and the promotion of the relative oral bioavailabilities of both drugs could unravel the potential of this system for the management of chemotherapy-induced nausea and vomiting.
Comparative genomic analysis of the PAL genes in five Rosaceae species and functional identification of Chinese white pear

Phenylalanine ammonia lyase (PAL) plays an important role in the biosynthesis of secondary metabolites regulating plant growth response. To date, the evolutionary history of the PAL family in Rosaceae plants remains unclear. In this study, we identified 16 PAL homologous genes in five Rosaceae plants (Pyrus bretschneideri, Fragaria vesca, Prunus mume, Prunus persica, and Malus × domestica). We classified these PALs into three categories based on phylogenetic analysis, and all PALs were distributed on 13 chromosomes. We tracked gene duplication events and performed sliding window analysis. These results revealed the evolution of PALs in five Rosaceae plants. We predicted the promoter of the PbPALs by PLANT CARE online software, and found that the promoter region of both PbPAL1 and PbPAL3 have at least one AC element. The results of qRT-PCR analysis found that PbPAL1 and PbPAL2 were highly expressed in the stems and roots, while expression level of PbPAL3 was relatively low in different tissues. The expression of PbPAL1 and PbPAL2 increased firstly and then decreased at different developmental periods of pear fruit. Among them, the expression of PbPAL1 reached the highest level 55 days after flowering. Three PbPALs were induced by abiotic stress to varying degrees. We transfected PbPAL1 and PbPAL2 into Arabidopsis thaliana, which resulted in an increase in lignin content and thickening of the cell walls of intervascular fibres and xylem cells. In summary, this research laid a foundation for better understanding the molecular evolution of PALs in five Rosaceae plants. Furthermore, the present study revealed the role of PbPALs in lignin synthesis, and provided basic data for regulating lignin synthesis and stone cells development in pear plants.
Empirical Study of Social Capital Factors Formed through Digital Empirical Study of Social Capital Factors Formed through Digital Social Networking Social Networking

Digital social networking (DSN) sites have attracted users pervading geographical, cultural, demographic, social and economic boundaries. DSN adoption has by far surpassed other information systems implementations and introduced novel opportunities to users in communication, information exchange, and human interaction as an individual, in a group, in communities and organizational networks. DSN offers unique features and opportunities compared to offline social networking. In consequence, the size of social network and the quality of network ties formed on DSN are different from those on offline social networks. The findings of this exploratory research upset the established bridging/bonding social capital factors based on network tie strength which served as the basis for academic studies and introduces factors on the functional view of social capital. It establishes the notion that DSN has introduced novel opportunities compared to previous instances of information systems which necessitated a revisit of established information systems theories and epistemology.
Machine Learning Approach for Foot-side Classification using a Single Wearable Sensor

Gait analysis is a common technique used to identify problems related to movement and posture in people with injuries, and foot-side detection is one of its important challenges. As many commercial sensors only provide limited information and traditional lab-based gait analysis is expensive, the aim of this study is to discriminate between left and right foot steps based on acceleration data from a single chest-worn accelerometer. To achieve this goal, an experimental study was conducted with 25 participants wearing an accelerometer on their chest and walking in a static environment. Several machine learning (ML) classifiers were trained to detect a foot-side from collected acceleration data. All machine learning classifiers achieved high classification accuracy, with Random Forest providing the best results. This result shows that ML-based foot-side classification using a single sensor is achievable and can contribute to develop an efficient health monitoring system to track lower limb’s problems.
Detecting Source Code Similarity Using Compression

Automatic assessment systems encounter significant problems when assessing students’ solutions to programming assignments. Main difficulties arise from lack of academic discipline among the students. While the students are expected to submit their original solutions, sometimes this is not the case. The assignments that they are expected to solve are usually very similar, and there is a restricted set of varieties among the correct solutions. Therefore, plagiarism detection is always an open question in the assessment of students’ work. Multiple tools and approaches are available for plagiarism detection. They are based on source code similarity detection algorithms or code clone detection tools. Available approaches rely on lexical, syntactic, structural, or semantic information about the programming solution. Thus, algorithms analyze character streams, token streams, controls structures or dependencies in the observed code. Usually, they are applied to the source code or some of its intermediate representations in the form of an intermediate language, a tree or a graph. All of these similarity analysis approaches have their advantages and disadvantages. Their sensitivity to different categories of similarities and precision varies and therefore their applicability and usefulness depend on objectives of the analysis [Novak et al. 2019]. Although high similarity between two projects does not mean that one is plagiarized from the other, it is often a good indicator. The students that are submitting work of other students usually make
Motivation for Innovation - CEO Regulatory Focus as a Predictor of Innovation Performance

Innovation is at the heart of corporate entrepreneurship (CE) and central to sustaining competitive advantages (Porter 1990, Sharma and Chrisman 1999). However, volatility, unpredictability, complexity, and ambiguity (VUCA) of today’s business world and associated increasing efficiency pressures shift firms’ attention to both sides of the “innovation coin”, i.e., innovation output quantity and quality. Yet, longitudinal research scrutinizing the impact of CEOs’ personality and motivational sets and their interplay with the TMT on innovation output quantity and quality simultaneously remains surprisingly scarce. Addressing a research gap concerning CEO-level, psychological as well as TMT-related drivers of innovation performance, our panel data study draws on upper echelons (Hambrick and Mason 1984) and regulatory focus theory (Higgins 1997) to answer two major research questions: How does CEO regulatory focus affect innovation performance, i.e., innovation output quantity and quality? How does the presence of a Chief Technology Officer (CTO) in the TMT moderate this relationship?
Affordance-Experimentation-Actualization Theory in Artificial Affordance-Experimentation-Actualization Theory in Artificial Intelligence Research – A Predictive Maintenance Story Intelligence Research – A Predictive Maintenance Story

Artificial intelligence currently counts among the most prominent digital technologies and promises to generate significant business value in the future. Despite a growing body of knowledge, research could further benefit from incorporating technological features, human actors, and organizational goals into the examination of artificial intelligence-enabled systems. This integrative perspective is crucial for effective implementation. Our study intends to fill this gap by introducing affordance-experimentation-actualization theory to artificial intelligence research. In doing so, we conduct a case study on the implementation of predictive maintenance using affordance-experimentation-actualization theory as our theoretical lens. From our study, we find further evidence for the existence of the experimentation phase during which organizations make new technologies ready for effective use. We propose extending the experimentation phase with the activity of ‘conceptual exploration’ in order to make affordance-experimentation-actualization theory applicable to a broader range of technologies and the domain of AI-enabled systems in particular.
How Much is Too Much: Employee Monitoring, Surveillance, and How Much is Too Much: Employee Monitoring, Surveillance, and Strain Strain

To maintain security, organizations use increasingly sophisticated methods to monitor employees, such as checking emails, tracking employee's website connections, collecting keystrokes, and surveilling social media activity. While electronic monitoring and surveillance (EM/S) practices represent an attempt to secure the firm’s data and more, they also may have an unintended consequence of creating strain among employees. In this study, we examine EM/S practices, the characteristics of those practices which cause strain and the negative, deviant and non-compliant behaviors the strain evoke in the EM/S subjected employees. We draw from the techno-stress and EM/S literature to build a research model that describe s the relationship between EM/S, strain, and employees’ EM/S related deviant and non-compliant outcomes. The research concludes with the discussion of our plan for testing the research model and its potential implications for research.
The Effect of Social Distance in Donation-Based Crowdfunding

Crowdfunding offers an innovative approach to financing projects by facilitating many individuals and organizations to contribute, often small amounts, to these projects. In the context of donation-based crowdfunding, we study how social distance affects average donations. Drawing on construal level theory, we argue that project images featuring humans reduce the social distance for donors because donors can better identify with the recipients; thus, these campaigns are more concrete, and donors give more. To test the effect of social distance using (human) project images on average donations, we conducted two studies, one observational and one experimental study. In both studies, we show that projects, whose images feature humans, receive higher donations.
Impact of Daily Commuting on Cyberloafing and Procrastination

This paper examines how commute impedances (crowding and time urgency) are associated with recovery experiences (psychological detachment and relaxation), which in turn are associated with cyberloafing and procrastination. Based on a survey of 106 full-time employees who used public transport (buses and mass rapid transit) to work, we found that relaxation significantly mediated the relationship between crowding and cyberloafing, but did not mediate the relationship between time urgency and cyberloafing. Psychological detachment significantly mediated the relationship between time urgency and procrastination, but did not mediate the relationship between crowding and procrastination. Our results suggest that employees should be cognizant of how their psychological states upon arrival at the workplace can affect their work in the morning. Consequently, employees can incorporate morning workplace rituals that facilitate their psychological transition to work. As well, organizations can implement measures that allow employees reattach back to work in the morning.
Information Disclosure in Location-based Services: An Extended Privacy Calculus Model

In today’s interconnected world, disclosing information on location-based services (LBS) has several privacy implications. In line with the general privacy studies, the rationale behind individual’s disclosure motivations has been studied in information systems (IS) through the lens of privacy calculus. However, existing work investigates location-information sharing as uni-dimensional user behavior under highly contextual settings. In this study, we propose an extended privacy calculus model that views location disclosure across three dimensions; (1) extent, (2) location sensitivity and (3) sharing parties. We also introduce amendments to account for data privacy regulations, data streaming economy and interdependent privacy risks. We thus provide a more nuanced conceptualization of location disclosure along with empirical insights from a large-scale empirical study (n=1050). We find that: (1) there is a need for transparent control settings, (2) users are willing to disclose for monetary incentives, and (3) they are not cognizant about interdependent privacy risks.
Beyond Convergence: Rethinking Pluralism in IS Research

A recurrent debate in the IS field has been whether or not IS research should be pluralistic. That is, whether many different forms of research should be allowed, or perhaps even encouraged. This paper presents a review of the literature on pluralism in IS research and analyses the different dimensions and types of pluralism that they adopt. A number of positions towards pluralism in the IS literature are identified and critically assessed. A common theme in this literature is an assumption that different approaches to IS research can be reconciled or even integrated. This is seen as necessary for the credibility of the discipline and to avoid a ruinous decline into relativism. Recent work in the history and philosophy of science, however, has questioned the view that research is, or should be, convergent in this way and offers a more expansive and potentially generative view of pluralism that will be explored in this paper. An argument for “Active Realism” is presented and its implications for IS research discussed.
Developing Critical Realist Process Theory via Explaining Generative Mechanisms

The philosophical perspective of critical realism offers IS researchers the opportunities to theorize the materiality of information systems and to develop in-depth causal explanations for how and why events occur in a sociotechnical phenomenon. Building upon the progressive development of critical realist studies, I identify three issues for improving critical realist case study research. First, although critical realists focus on identifying generative mechanisms, very little research has developed a theory based on the generative mechanisms identified. Second, existing research lacks a consistent view of conceptualizing generative mechanisms. Third, researchers face the challenge of ensuring the rigor of conducting critical realist case studies. To address these issues, I develop a stepwise case study approach for developing critical realist process theory. This approach provides a guideline for researchers to collegially contribute to the development of the critical realist research from both theoretical and methodological perspectives.
The Star Citizen Phenomenon & the "Ultimate Dream Management" Technique in Crowdfunding

Developing and keeping a community motivated is critical for success, particularly in self-hosted crowdfunding. Existing research has identified social capital as an important driver of such endeavors. However, the process of how social capital can be developed, managed, and targeted towards a common goal in digital settings is mostly unknown. Applying social exchange theory and a mixed-methods approach, we leverage a large dataset of one of the world’s most-funded crowdfunding projects – Star Citizen – to zoom in on the process of social capital development as a success factor. We depict how repeated social exchange and reciprocity norms foster the emergence of a cohesive and supportive community from a crowd of strangers. Moreover, we explain how this is used by Start Citizen within five themes of a novel and IS-enabled managerial technique focusing on dreams. Our insights generalize to contexts beyond crowdfunding that require the engagement of crowds for joint value creation.
Motivational strategies and approaches for single and multi-player exergames: a social perspective

Background Exergames have attracted the interest of academics, practitioners, and designers, in domains as diverse as health, human-computer interaction, psychology, and information technology. This is primarily because exergames can make the exercise experience more enjoyable and entertaining, and in turn, can increase exercise levels. Despite the many benefits of exergames, they suffer from retention problems. Thus, the objective of this article was to review theories and game elements that have been empirically examined or employed in an attempt to make exergames more motivating so people engage in sustained physical activity (duration of physical activity) in a repeating pattern over time (frequency of physical activity). Methodology A literature search and narrative review were conducted. Results Five major theories and elements were prevalent in the exergaming literature: (1) self-determination theory, (2) gamification, (3) competition and cooperation, (4) situational interest, and (5) social interaction. These theories and elements are important for encouraging long-term play and show promise for designing exergames to promote sustained engagement and motivate physical activity. We discuss their strengths and weaknesses throughout the paper. Conclusions The long-term effectiveness of exergame interventions is unclear mainly because of the limited amount of long-term studies. Better metrics are also needed to evaluate this effectiveness. We also identified particular attention to social factors and group dynamics, such as multi-player exergames and more effective player matchmaking strategies for increasing social connectedness, as a key area of future research.