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

A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction

2023 | Information Systems Research | Citations: 0

Authors: Chen, Gang; Xiao, Shuaiyong; Zhang, Chenghong; Zhao, Huimin

Abstract: As artificial intelligence and digitalization technologies are flourishing real- ... Expand

Abstract: As artificial intelligence and digitalization technologies are flourishing real-time, online interaction–based commercial modes, exploiting customers’ purchase intention implied in online interaction processes may foster huge business opportunities. In this study, we target the task of voice chat–based customer response prediction in an emerging online interaction–based commercial mode, the invite-online-and-experience-in-store mode. Prior research shows that satisfaction, which can be revealed by the discrepancy between prior expectation and actual experience, is a key factor to disentangle customers’ purchase intention, whereas black-box deep learning methods empirically promise us advantageous capabilities in dealing with complex voice data, for example, text and audio information incorporated in voice chat. To this end, we propose a theory-driven deep learning method that enables us to (1) learn customers’ personalized product preferences and dynamic satisfaction in the absence of their profile information, (2) model customers’ actual experiences based on multiview voice chat information in an interlaced way, and (3) enhance the customer response prediction performance of a black-box deep learning model with theory-driven dynamic satisfaction. Empirical evaluation results demonstrate the advantageous prediction performance of our proposed method over state-of-the-art deep learning alternatives. Investigation of cumulative satisfaction reveals the collaborative predictive roles of theory-driven dynamic satisfaction and deep representation features for customer response prediction. Explanatory analysis further renders insights into customers’ personalized preferences and dynamic satisfaction for key product attributes. History: Yong Tan, Senior Editor; Jingjing Zhang, Associate Editor. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971067 and 72271059] and the China Postdoctoral Science Foundation [Grant 2022M722394]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1196. Collapse

Topics: customer satisfaction willingness to pay decision making marketing information system customer service

Methods: deep learning experiment machine learning BERT theory development

Token Incentives in a Volatile Crypto Market: The Effects of Token Price Volatility on User Contribution

2023 | Journal of Management Information Systems | Citations: 0

Authors: Chen, Kun; Fan, Yifan; Liao, Shaoyi Stephen

Abstract: Crypto tokens, issued and managed via smart contracts, function as rewards in bl ... Expand

Abstract: Crypto tokens, issued and managed via smart contracts, function as rewards in blockchain systems to encourage user participation. Distinct from monetary incentives, token incentives are uncertain in reward magnitude due to the large swings in token prices on crypto markets. By focusing on token price volatility, this study investigates how the reward uncertainty affects user contribution in a tokenized digital platform. Our empirical setting is Steemit, a platform where bloggers write posts and share token rewards based on their posts' popularity. We find that while high token price volatility induces a large volume of blog posts, it diminishes post quality. The dichotomous effects are explained via two mechanisms: users' direct reactions to reward uncertainties and their indirect reactions, mediated by altered token preference amid volatility shocks. Deeply exploring this dynamic process, our results reveal that token price volatility facilitates a platform's network short-term effect but impairs long-term user creativity. Our empirical findings thus extend the literature on blockchain economics and cryptocurrencies and have practical implications for the design of incentive mechanisms on tokenized digital platforms. Collapse

Topics: blog blockchain digital platform cryptocurrency user behavior

Methods: longitudinal research instrumental variables estimation data transformation principal component analysis vector autoregression

Theories: motivation crowding theory

Trust in Online Ride-Sharing Transactions: Impacts of Heterogeneous Order Features

2023 | Journal of Management Information Systems | Citations: 0

Authors: Cheng, Xusen; Fu, Shixuan; Sun, Jianshan; Zuo, Meiyun; Meng, Xiangsong

Abstract: With the development of the sharing economy, online ride-sharing has become a pr ... Expand

Abstract: With the development of the sharing economy, online ride-sharing has become a primary form of commuting. Using secondary transaction data, this study investigates the associations between the heterogeneous features and mutual trust in sharing economy-driven online ride-sharing transactions. Based on an examination of 12,404 ride-sharing orders in Beijing, we propose a set of trust distribution maps using order location data to reveal heterogeneous spatial patterns of the relationship between online ride-sharing transactions and mutual trust. The results show that the historical order completion rate and order distance are positively associated with mutual trust in ride-sharing transactions, whereas order time and departure density negatively and significantly influence mutual trust. Furthermore, we use machine learning algorithms to predict trust. The implications for theory and practice and future research directions are discussed. Collapse

Topics: ride sharing platform sharing platform logistics management geographic information system online review

Methods: machine learning computational algorithm regression analysis method experiment exploratory data analysis

Theories: signaling theory

The Crisis in Local Newspapers and Organizational Wrongdoing: The Role of Community Social Connectedness

2023 | Organization Science | Citations: 0

Authors: Choi, Tony Jaehyun; Valente, Mike

Abstract: Drawing on institutional anomie theory, we examine how the crisis in local newsp ... Expand

Abstract: Drawing on institutional anomie theory, we examine how the crisis in local newspapers has induced organizational wrongdoings in local communities. We argue that, because local newspapers are the primary source of accountability journalism in local communities, their decline leads to an anomic state that increases the scale of organizational wrongdoing. We also investigate whether institutional complementarity helps overcome the anomic state: Due to functional similarity, community social connectedness compensates for the scarcity of local newspapers. Our analysis of U.S. metropolitan areas for the period of 2007–2015 reveals that the positive relationship between local newspaper scarcity and the scale of organizational wrongdoing is not present in all communities but does appear when a community lacks community social connectedness. We also find that this moderating role of community social connectedness is observed only for internal organizational wrongdoings that are less visible to the public than external ones. Collapse

Topics: digital news missing data decision making social capital database system

Methods: descriptive statistic statistical hypothesis test longitudinal research longitudinal data analysis hierarchical linear modeling

Theories: organizational behavior theory

A temporal graph framework for intelligence extraction in social media networks

2023 | Information & Management | Citations: 0

Authors: Chung, Wingyan; Lai, Vincent S.

Abstract: Social media networks (SMNs) are increasingly used in professional management of ... Expand

Abstract: Social media networks (SMNs) are increasingly used in professional management of knowledge workers and related assets. However, the factors affecting behavioral trends and activity levels in these networks are not well understood. Although social and cognitive theories can help to explain human behavior in traditional social networks, their application to SMNs has not been validated. Traditional social network modeling techniques may not accurately predict real-world SMN activities. This research developed a temporal graph framework for in­ telligence extraction in SMNs. Theory-based, data-driven models (Conformity Model (COM), Recency-Primacy Model (REM), Trend Interaction Model (TIM), Periodic Interaction Model (PIM)) were developed based on the framework to capture various aspects of user behavior: conformity effect, recency, primacy, periodicity, and dynamic trend. The models capture the activity history and dynamically combine pricing information to enhance predictive accuracy. Using data of 83,536 GitHub software repositories on cryptocurrency, this article reports the results of experiments that compare the models’ performance in predicting SMN activities over time. Experi­ mental results show that the model (REM) that captures recency/primacy effects of human cognitive processing outperformed other models in 9 (out of 18) measures pertaining to engagement, contribution, influence, and popularity. Primacy plays a dominant role in predicting engagement, contribution, and popularity, whereas recency plays a key role in predicting influence. Short-term trend (modeled with TIM) was found to yield significantly better performance on predicting user contribution. The models also outperformed an integrated machine learning (IML) model by most measures. Overall, the effects modeled by REM and TIM were found to be more significant than the effects modeled by COM, PIM, and IML. The research contributes to enhancing un­ derstanding of SMN behavior, developing new models to simulate and predict SMN activities, and designing new artifacts for information systems practitioners to manage knowledge assets and to extract SMN intelligence. Collapse

Topics: cryptocurrency social network systems development situation awareness user behavior

Methods: experiment machine learning longitudinal research computational algorithm time series analysis

Theories: psychological theory social contagion theory social interactionist theory social impact theory social sciences theory

A Visual's Worth a Thousand Codes: Illustrative Techniques for Grounded Theory Methodology

2023 | HICSS | Citations: 0

Authors: Clary, G.; Van Slyke, C.

Abstract: Academic research should show a transparent methodology. Transparency is import ... Expand

Abstract: Academic research should show a transparent methodology. Transparency is important for replicability, trust in the results, and adapting to new contexts. Due to its subjective nature, transparency is especially important for qualitative work, such as grounded theory methodology (GTM). In this paper, we report aspects of a GTM study that highlights several visuals aimed at increasing transparency. This paper aims to contribute novel, transparencyenhancing GTM illustrations that others can adapt for their purposes. The illustrations are analyzed and discussed with suggestions for implementation. Collapse

Topics: remote work IT supported collaboration pandemic

Methods: qualitative interview qualitative coding triangulation design principle qualitative content analysis

Theories: self determination theory

A Task-Technology-Identity Fit Model of Smartwatch Utilisation and User Satisfaction: A Hybrid SEM-Neural Network Approach

2023 | Information Systems Frontiers | Citations: 0

Authors: El-Masri, Mazen; Al-Yafi, Karim; Kamal, Muhammad Mustafa

Abstract: Smartwatches are wearable devices intended to be smartphone companions that capt ... Expand

Abstract: Smartwatches are wearable devices intended to be smartphone companions that capture health data and ease access to notifications. They have also become personalisable standing as a fashion statement. This combination resulted in staggering adoption rates recently leading to question whether smartwatch users’ choice and use satisfaction emerge from utility features or from its fashion characteristics. This paper proposes and validates a fit theory to investigate the antecedents of adopters’ satisfaction. Besides evaluating fit with identity, the model assesses both perceived and actual task-technology fit of smartwatches. A questionnaire-based quantitative approach is used to collect data from about 300 smartwatch users in Qatar. To test the proposed model, data is analysed using structural equation modeling (SEM) and artificial neural networks (ANN). Furthermore, ANN sensitivity analysis ranks the importance of the fit factors affecting users’ choice during pre- and post-adoption stages. Both task-technology and technology-identity fit factors are quasi-equally important in explaining 62% of satisfaction variance. ANN analysis revealed that post-adoption satisfaction is primarily attributed to smartwatches’ ability to fit with users’ identity and secondarily to its perceived fit with tasks. Nevertheless, pre-adoption choice of smartwatches is mainly guided by their functionality. This paper is the first to propose and validate an integrated task-technology-identity fit model to explain smartwatch utilization and users’ satisfaction. The originality also lies in assessing actual task-technology fit and as perceived by users. Employing two modes of analysis revealed extra insights too. Collapse

Topics: smart watch mobile system user satisfaction personality smart wearable device

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

Theories: theory of task technology fit unified theory of acceptance and use of technology technology acceptance model

The Effect of Community Managers on Online Idea Crowdsourcing Activities

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Hornuf, Lars; Jeworrek, Sabrina

Abstract: In this study, we investigate whether and to what extent community managers in o ... Expand

Abstract: In this study, we investigate whether and to what extent community managers in online collaborative communities can stimulate community activities through their engagement. Using a novel data set of 22 large online idea crowdsourcing campaigns, we find that moderate but steady manager activities are adequate to enhance community participation. Moreover, we show that appreciation, motivation, and intellectual stimulation by community managers are positively associated with community participation but that the effectiveness of these communication strategies depends on the form of participation managers wish to encourage. Finally, the data reveal that community manager activities requiring more effort, such as media file uploads vs. simple written comments, have a stronger effect on community participation. Collapse

Topics: crowdsourcing innovation management online community missing data privacy

Methods: propensity score method statistical power analysis Poisson regression regression analysis method econometric modeling

Theories: transformational leadership theory

Pictures that are Worth a Thousand Donations: How Emotions in Project Images Drive the Success of Online Charity Fundraising Campaigns? An Image Design Perspective

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Hou, Jian-Ren; Zhang, Jie; Zhang, Kunpeng

Abstract: Charity fundraising is becoming increasingly reliant on online platforms such a ... Expand

Abstract: Charity fundraising is becoming increasingly reliant on online platforms such as crowdfunding platforms. However, overwhelmingly, crowdfunding campaigns are not meeting their goals. Therefore, it is imperative to examine how the success of charity fundraising campaigns can be improved. In this paper, we focus on the design of project images on a crowdfunding website, which portray the themes and content of the projects. Employing the stimulus-organism-response (S-O-R) model, we investigate the relationships between image attributes (S) and image emotions (O), and between image emotions (O) and campaign outcomes (R). We developed and trained a deep neural network model to identify the emotions conveyed in the images, and then implemented it to analyze project images from a popular crowdfunding platform. We applied the obtained image emotions together with the objective image attributes and the project outcome metrics to explore from a design perspective, what image attributes evoke image emotions, and how image emotions are related to the success of charity fundraising projects. Our results confirm these relationships and further suggest that the roles of image emotions on the success of crowdfunding campaigns vary with project characteristics such as the project budget and category. In addition, the image emotions of competing projects on the crowdfunding platform were found to reduce the project's performance. In an extended study, we conducted an online randomized controlled experiment by manipulating image attributes to reexamine the causal relationships and verify the mediating roles of positive and negative empathies between image emotions and campaign outcomes. This research contributes to the charity fundraising literature from a novel perspective of emotions in project images. It presents new and unique findings regarding the mediation roles of positive and negative empathies and the limitations of the emotion of sadness in certain types of charity fundraising. In addition, our findings provide useful insights for practitioners seeking to design successful online charity campaigns. Collapse

Topics: crowdsourcing crowdfunding project productivity Google+ website

Methods: experiment deep learning computational algorithm machine learning descriptive statistic