Exploring Teams' Temporal Factors for Determining Process Tailoring that Promotes the Evolution of Agilebased Software Development

2023 | ACM SIGMIS Database | Citations: 0

Authors: Chen, Chung-Yang; Lee, Jung-Chieh

Abstract: In contemporary software development, software process tailoring (SPT) refers to ... Expand

Abstract: In contemporary software development, software process tailoring (SPT) refers to the continual planning and adjustment of the development process, which evolves to better accommodate dynamics and meet customer needs. SPT is a critical teamwork involving the differences in time-related perceptions among team members when tailoring to adjust and refine the development. This is particularly true in agile-based projects that promote iterative development over shorter cycle times for early release; thus, temporality is critical to efficiently and effectively determine a process solution for the next iteration of time-bound development. In this regard, this study theorizes a temporal model to explore how teams' temporal leadership, temporal reflexivity and shared temporal cognitions (STC) collectively determine SPT performance in agile environments. Based on an empirical examination, we suggest facilitating the role of temporal leadership in agile development, as the results demonstrate that it not only directly increases SPT performance but also stimulates both temporal reflexivity and STC in their collateral influences on effectual development planning. Notably, the results also show that team reflexivity does not exert the same favorable effect in SPT as in the distributed team environment. Theoretical implications are considered, and practical applications of the findings are provided. Collapse

Topics: agile software development virtual teamwork co-located work systems development collaborative work

Methods: survey partial least squares regression design methodology theory development longitudinal research

Trust and deception with high stakes: Evidence from the friend or foe dataset

2023 | Decision Support Systems | Citations: 0

Authors: Chen, Xunyu; Wang, Xinran; Spitzley, Lee; Nunamaker, Jay

Abstract: Many social interactions rely on the premise of mutual trust, but deception viol ... Expand

Abstract: Many social interactions rely on the premise of mutual trust, but deception violates trust and poses risk. Empirically examining trust and deception, particularly in high-stakes situations, is challenging but essential for improving the research realism and generalizability. To address this difficulty, we study trusting and deceptive behaviors in a high-stakes situation by using a novel dataset created from an American game show, Friend or Foe (FoF). In the show, a contestant's reward was determined through a trust game modified from the prisoner's dilemma. We explore how numerous human behaviors including facial expressions, gaze, head pose, body motion, language, and socio-demographic attributes, were related to a contestant's trusting or deceptive decision. Using a data-driven approach, we find that the deceivers' (contestants who chose Foe) behavior featured a neutralized face, negative facial emotions, enhanced upper body motion, and language with a lower sense of immediacy and agreeableness. The contestants who chose to trust (chose Friend) exhibited opposite behavioral patterns. Socio-demographic factors such as age, height, and facial attractiveness were also associated with a contestant's choice. Combining multimodal information, machine learning classifiers could predict the contes­ tant's choice with an accuracy about 25% greater than earlier reported human accuracy. We contribute to both trust and deception literature by examining the generalizability of trusting and deceptive behaviors to a new high-stakes scenario. We also add to the decision support literature by showing the superior predictive perfor­ mances of combining behavioral and socio-demographic features. Furthermore, we contribute to the academic community by introducing the FoF dataset. Collapse

Topics: decision support website YouTube Python decision making

Methods: meta analysis support vector machine correlation analysis descriptive statistic random forest classification

Algorithm-Human-Algorithm: A New Classification Approach to Integrating Judgemental Adjustments

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Christopher; Jain, Nitish; Karamshetty, Varun

Abstract: Modern-day firms face the predicament of blending the comparative advantages of ... Expand

Abstract: Modern-day firms face the predicament of blending the comparative advantages of their two core resources: machines and humans. When forecasting demand (e.g. for a product), extant literature documents that always permitting (or prohibiting) human revision of a machine forecast is beneficial if the humans' private information role is larger (or smaller) than that of machine-accessible public information. We propose and test a complementary framework that shifts the focus to the regulation of each human revision; and in doing so, adjusts for human vulnerability to systematic biases. We collaborate with a European retailer to compile a large dataset (∼ 1.1 mn transactions) on machine-led demand forecasts. Humans revise nearly 38% of these forecasts, but revisions do not always yield an improved final forecast. Compared to the always permit or prohibit strategies, the ideal regulation of each of these revisions could reduce the absolute deviation of the final forecast from realized sales in close to 50% of instances. Any regulation to obtain the best of the machine and human-revised forecasts requires an ability to predict revision quality. In addition to private information indicators, we show that indicators of systematic bias in human judgement (e.g., indicators of cognitive load) can improve the prediction of revision quality. In an out-of-sample analysis, our revision-level regulation approach picks the best of available forecasts in 14% more instances, compared to an always-permit or prohibit strategy at the product-store level. Our paper provides practitioners with a novel approach to combining the machine-human output to improve demand forecasting performance. Collapse

Topics: privacy logistics management supply chain management analytical information system

Methods: computational algorithm machine learning descriptive statistic XGBoost gradient boosting

A Bitter Pill to Swallow? The Consequences of Patient Evaluation in Online Health Question-and-Answer Platforms

2023 | Information Systems Research | Citations: 0

Authors: Chen, Chen; Walker, Dylan

Abstract: Online health question-and-answer (Q&A) platforms (OHQPs), where patients post h ... Expand

Abstract: Online health question-and-answer (Q&A) platforms (OHQPs), where patients post health-related questions, evaluate advice from multiple doctors, and direct a bounty (monetary reward) to their most preferred answer, have become a prominent channel for patients to receive medical advice in China. To explore the quality of medical advice on these platforms, we analyzed data on patients’ evaluation of ∼497,000 answers to ∼114,000 questions on one of the most popular OHQPs, 120ask.com, over a three-month period. We assembled a panel of independent physicians and instructed them to evaluate the quality of ∼13,000 answers. We found that the quality of medical advice offered on the platform was high on average, and that low-quality answers were rare (6%). However, our results also indicate that patients lacked the ability to discriminate advice quality. They were as likely to choose the best answer as the worst. The medical accuracy of patient evaluation was worse in critical categories (cancer, internal medicine) and for vulnerable subpopulations (pediatrics). Given that millions of patients seek medical advice from OHQPs in China annually, the social and economic implications of this finding are troubling. To understand how patients evaluate advice, we trained deep neural networks to think like patients, allowing us to identify patients’ positive and negative responses to different heurist cues. Although our results indicate that OHQPs perform well, we identified several concerns that should be addressed through platform design and policy changes. Because the Q&A process lacks peer review mechanisms, signals of advice quality are not conveyed to patients, forcing them to rely on heuristic cues, which cannot effectively guide them toward the best advice. We also found that the platform reputation metric was not correlated with the quality of the advice giver’s advice, may effectively encourage patients to select lesser quality medical advice, and increased the risk of moral hazard for malicious players to intentionally provide less accurate but more agreeable advice for personal gain. Our analysis revealed bad actors on the platform, including drug promoters and spammers. Finally, we found that OHQPs exacerbated care avoidance. We discuss several potential policy changes to address these shortcomings. History: Param Singh, Senior Editor; Idris Adjerid, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1158. Collapse

Topics: decision making local interpretable model agnostic explanation digital platform health information system pandemic

Methods: artificial neural network psychometrics survey deep learning natural language processing

Theories: lemon market theory

Navigating AI in Personnel Selection: A Scenario-based Study on Applicants' Perceptions

2023 | International Conference on Information Systems | Citations: 0

Authors: Czernietzki, Charlotte; Märtins, Julian; Westmattelmann, Daniel; Grotenhermen, Jan-Gerrit; Oldeweme, Andreas; Baumeister, Viktoria; Schewe, Gerhard

Abstract: AI-based systems are increasingly deployed on organizational tasks, such as per ... Expand

Abstract: AI-based systems are increasingly deployed on organizational tasks, such as personnel selection decisions. As existing research indicates that applicants generally react negatively to the use of AI in personnel selection, this study examines how organizations can mitigate adverse reactions to fully exploit the benefits of AI. To obtain robust results, we recruited an online sample of German participants (N = 1,852) and presented them with various selection scenarios. Using a between-subject design, the process stage (preselection vs. interview) and the degree of process automation (augmented vs. automated) were manipulated. By employing a multidimensional conceptualization of transparency, we show that disclosure and accuracy positively impact procedural justice perceptions, a strong predictor of process quality assessment. This relationship is robust across selection contexts. Results indicate that applicants prefer AI for pre-selection and as human decision support, thus offering overall insights into design choices for AI in selection, optimizing applicant reactions. Collapse

Topics: artificial intelligence business process automation data quality electronic mail virtual reality

Methods: qualitative interview partial least squares path modeling survey parametric test ANOVA

Theories: procedural justice theory organizational justice theory

Corporate Users’ Attachment to Social Networking Sites: Examining the Role of Social Capital and Perceived Benefits

2023 | Information Systems Frontiers | Citations: 0

Authors: Dhar, Suparna; Bose, Indranil

Abstract: The debate on the pros and cons of employee attachment to social networking site ... Expand

Abstract: The debate on the pros and cons of employee attachment to social networking sites (SNS) has led to social media policy paralysis in many organizations, and often a prohibition on employee use of SNS. This paper examines corporate users’ attachment to SNS. An analysis of 316 survey responses showed that corporate users’ socialization in large public SNS was steeped in perceived work-related benefits, which in turn nourished their SNS attachment. Social use outperformed informational use in generating perceived work-related benefits from SNS. Weak ties in large heterogeneous networks resulted in strategic and operational benefits, whereas the effects of strong bonding in homogenous networks were limited to operational benefits. The paper contributes to research on SNS use by corporate users and the debate on the effect of SNS use for work. The findings will benefit SNS strategists of organizations and policymakers to exploit the benefit potential of public SNS. Collapse

Topics: social capital Facebook LinkedIn information system use accounting

Methods: survey data transformation principal component analysis path analysis structural equation modeling

Theories: uses and gratifications theory social capital theory attachment theory

A Needs-Affordances-Satisfaction Perspective on the Use of Connected Objects

2023 | International Conference on Information Systems | Citations: 0

Authors: Hanelt, André; Marz, David; Matt, Christian

Abstract: ... Expand

Abstract: Collapse

Topics: affordance smartphone digital product usability technology adoption

Methods: survey partial least squares regression theory development structural equation modeling design artifact

Theories: theory of affordance self determination theory theory of planned behavior technology acceptance model

The effects of cyber regulations and security policies on organizational outcomes: a knowledge management perspective

2023 | European Journal of Information Systems | Citations: 0

Authors: Hovav, Anat; Gnizy, Itzhak; Han, Jinyoung

Abstract: Knowledge management (KM) has emerged as an operational and strategic organisati ... Expand

Abstract: Knowledge management (KM) has emerged as an operational and strategic organisational tool. However, the influence of recent cyber regulations (CR) and security policies (SP) on the usefulness of KM, are absent from the literature. This study focuses on the intersection of knowledge management and knowledge fortification. The study employs survey data from a sample of firms and utilises partial least squares (PLS) with extensive post hoc analyses, to examine the influence of CR and SP on KM core processes (acquisition, sharing, and utilisation) and subsequently on the operational effectiveness and strategic performance of firms. Contrary to prior research, we found that external knowledge sharing negatively affected performance. These findings support our assertion that KM processes are tied to contingencies that change the relationship between KM outcomes. Collapse

Topics: knowledge sharing organizational productivity security policy knowledge utilization knowledge acquisition

Methods: survey post-hoc analysis partial least squares regression computational algorithm descriptive statistic

An empirical study of community cohesiveness, community attachment, and their roles in virtual community participation

2023 | European Journal of Information Systems | Citations: 2

Authors: Kim, Dan J.; Salvacion, Mark; Salehan, Mohammad; Kim, Dae Wan

Abstract: Although active participation of members is a critical success factor for virtua ... Expand

Abstract: Although active participation of members is a critical success factor for virtual communities, few members actively participate in activities in virtual communities (e.g., posting, commenting, sharing, etc.). This study explores the differences between passive and active community participation from community cohesiveness and community attachment perspectives. Drawing upon a context-specific cohesiveness-attachment-participation framework based on cognition-affection-conation traits of human psychology, we propose a research model and empirically test the roles of community cohesiveness and attachment in virtual community participation. The results suggest that community members’ active participation is positively influenced by both community cohesiveness and community attachment. Visitation, as the passive mode of participation, is influenced by community attachment only. The findings not only inspire future IS research to consider community cohesiveness, attachment, and participation as vital research constructs in other social contexts, but it also helps managers build managerial actions, such as prioritising their limited business resources to increase community members’ active participation, for the success of virtual communities. Collapse

Topics: online community Facebook social network Twitter YouTube

Methods: survey partial least squares path modeling PLS tool survey design statistical hypothesis test

Tacit knowledge acquisition & sharing, and its influence on innovations: A Polish/US cross-country study

2023 | International Journal of Information Management | Citations: 0

Authors: Kucharska, Wioleta; Erickson, G. Scott

Abstract: This study measures the relationship between tacit knowledge sharing and innovat ... Expand

Abstract: This study measures the relationship between tacit knowledge sharing and innovation in the Polish (n = 350) and US (n = 379) IT industries. Conceptually, the study identifies the potential sources of tacit knowledge devel­ opment by individuals. That is, the study examines how “learning by doing” and “learning by interaction” lead to a willingness to share knowledge and, as a consequence, to support process and product/service innovation. This study empirically demonstrates that tacit knowledge internalization and externalization (awareness and sharing) significantly mediate between tacit knowledge experimentation and socialization (acquisition) and its final combination (knowledge in action). While such theoretical assumptions already exist, they have not yet been empirically explained and revealed in a single structural model. Further, this empirical approach enabled a demonstration that internalization and externalization of tacit knowledge may occur consciously or uncon­ sciously with equal success. Even so, the study also showed conscious tacit knowledge’s greater impact on innovation. Therefore, an organizational effort to manage autonomous, informal, and strongly contextual tacit knowledge is worthwhile and creates the capacity for superior competitive advantage. Finally, this study also demonstrates that national context influences tacit knowledge acquisition. In the US, “learning by doing” is dominant, whereas in Poland, “learning by interaction” and critical thinking are more common. This might be related to factors such as risk acceptance that could be studied in more detail and provide opportunities for future research. Collapse

Topics: innovation management implicit knowledge knowledge acquisition business process innovation knowledge sharing

Methods: survey data imputation descriptive statistic SEM tool conceptual modelling