Transparent Artificial Intelligence and Human Resource Management: A Systematic Literature Review

As the technological expansion of Artificial Intelligence (AI) penetrates various industries, Human Resource Management has attempted to keep pace with the new capabilities and challenges these technologies have brought. When adopting AI, transparency within HRM decisions is an increasing demand to establish ethical, unbiased, and fair practices within a firm. To this end, explainable AI (XAI) methods have become vital in achieving transparency within HRM decision-making. Thus, there has been a growing interest in exploring successful XAI techniques, as evidenced by the systematic literature review (SLR) performed in this paper. Our SLR starts by revealing where AI exists within HRM. Following this, we review the literature on XAI and accuracy, XAI design, accountability, and data processing initiatives within HRM. The integrated framework we propose provides an avenue to bridge the gap between transparent HRM practices and Artificial Intelligence, providing the industrial and academic community with better insight into where XAI could exist within HRM processes

Rethinking FS-ISAC: An IT Security Information Sharing Network Model for the Financial Services Sector

This study examines a critical incentive alignment issue facing FS-ISAC (the information sharing alliance in the financial services industry). Failure to encourage members to share their IT security-related information has seriously undermined the founding rationale of FS-ISAC. Our analysis shows that many information sharing alliances’ membership policies are plagued with the incentive misalignment issue and may result in a “free-riding” or “no information sharing” equilibrium. To address this issue, we propose a new information sharing membership policy that incorporates an insurance option and show that the proposed policy can align members’ incentives and lead to a socially optimal outcome. Moreover, when a transfer payment mechanism is implemented, all member firms will be better off joining the insurance network. These results are demonstrated in a simulation in which IT security breach losses are compared both with and without participating in the proposed information sharing insurance plan

A Multilevel Investigation of Participation Within Virtual Health Communities

Virtual health communities are a major channel through which health consumers share health-related knowledge and/or exchange social support with their peers. These virtual environments can be a form of, or a potential component of, integrated Patient-centered e-Health (PCEH) applications, which represent emerging healthcare information systems that emphasize the role of patients and revolve around providing patient-focus, patient-activity, and patient-empowerment services. Because of the collaborative nature of virtual health communities, user participation is a critical factor for community growth and prosperity. In this study, we examine user participation at the individual and group (thread) levels. At the individual level, we investigate the impact of reciprocity and homophily (similarity of user characteristics such as age, gender, and tenure) on user participation within virtual health communities. At the thread level, we study the role of highly active users (power users) as thread initiators as well as the role of thread initiators’ participation on the overall thread vibrancy. To do so, we analyzed 2,176 threads initiated by 130 users and 1,947 messages exchanged between these users and their peers. Our results support short-term reciprocity, but refute the positive relationship associated with long-term reciprocity. Among homophily hypotheses, our results support gender homophily, but not age or tenure homophily. At the thread level our findings suggest that a discussion thread is vibrant if the thread initiator is a power user or participates actively within the thread. These findings have important implications for future research and practice in PCEH applications

The Effect of the Recommendation System in the Mobile App Market

Product recommendation systems have been widely adopted in e-commerce to improve product visibility and promote sales. This study examines the effect of recommendation system in the increasingly popular mobile app market, which is uniquely characterized with its multitude of product choices and the prevailing use of the freemium model. We constructed a panel dataset using a wide range of daily app data collected from the world’s leading Google Play app store. This rich dataset allows us to examine how the competition between the focal app and its recommendations affects their relative adoptions, and how the heterogeneity of the recommendations influences market inequality. By introducing new research angles on competition within recommendation system and market inequality, our study will help platform operators and developers better understand the dynamics in the mobile app market and offer practical guidance on how to enhance the design of the mobile app recommendation system

Voters' Impacts on Creators' Popularity Disparity and Network Size in Two-sided Decentralized User-Generated Content Market

The development of decentralized technologies greatly facilitates the growth of user-generated content (UGC) markets. However, existing literature debates whether the decentralized UGC platform model can be economically sustainable. This study investigates the differential impacts of four voter groups, categorized by their social engagement and financial investment, on the two critical issues pertaining to decentralized UGC markets (i.e., creator popularity disparity and content contribution). We empirically tested our hypotheses using data from a leading decentralized UGC platform. The results indicate a consumer engagement tradeoff between promoting fair growth opportunities in the interest of the creators and extending the creator network in the interest of the platform. Our findings shed light on how creator popularity disparity may arise through votes from the four voter groups and their differential network externalities exerted on the creator network

An Empirical Examination of an Agile Contingent Project/Method Fit Model

While research has demonstrated positive productivity and quality gains from using agile software development methods (SDMs), some experts argue that no single SDM suits every project context. We lack empirical evidence about the project contextual factors that influence when one should use these methods. Research suggests several factors to explain agile method appropriateness; however, generalizable empirical evidence supporting these suggestions is weak. To address this need, we used contingency theory and the information processing model to develop the agile contingent project/method fit model. Subsequently, we used the model to analyze the influence of project contextual factors and agile practices on software development professionals’ perceptions regarding agile SDM appropriateness. We tested the model using survey data collected from 122 systems development professionals who provided information regarding: 1) contextual factors surrounding a recent agile development project, 2) agile practices applied during the course of that project, and 3) perceptions regarding the relative fit (appropriateness) of the agile method used. Linear regression identified several significant relationships between project contextual factors, agile practices, and respondents’ relative fit perceptions

Where Does My Product Stand? A Social Network Perspective on Online Product Reviews

Customer reviews often include comparative comments on competing products. Adopting the The Strength of Weak Ties theory, we build a product social network around “strong tie” and “weak tie” entities. By performing text mining on comparative customer reviews collected from Amazon, we successfully identify strong and weak ties in a product network and compute the strength of these ties. Utilizing these network properties, we generate network graphs based on different product features and discover the underlying competitive relationships among them. In particular, our regression analysis shows that the strength of ties positively contributes to the review rating of a product and the strength of weak ties plays a more significant role than the strength of strong ties. These results will benefit vendors in online market to discover potential competitors, effectively tailor their marketing and product development efforts, and better position their products to increase profit and explore new market opportunities

Understanding factors influencing employees’ consumptive and contributive use of enterprise social networks

There has been an exponential growth use of enterprise social networks for improved communication, connection, collaboration and enhanced knowledge sharing within organizations. However, the intended benefits of this social network deployment have not been fully realized due to the relative low usage among employees. This study provides an insight into the underlying factors deemed likely to influence employees’ enterprise social networks consumptive and contributive use by modifying and extending the Unified Theory of Acceptance and Use of Technology (UTAUT). An online survey was conducted and data were collected from 158 employees whose organizations are currently deploying a workplace social platform. The data were validated and analyzed using partial least square (PLS-SEM) method and Ordered Logistic Regression (OLR). Significant differences were found regarding the factors that influence consumptive and contributive use. The most influential factors for consumptive use are performance expectancy and content value, followed by facilitating conditions and effort expectancy. On the other hand, the contributive use is strongly influenced by social influence, content value and relationship expectancy. Moreover, a more balanced use pattern as measured by a smaller gap in consumptive and contributive use is shown to be positively associated with increased overall enterprise social network use. This study provides implications for managers to develop appropriate interventions to address idiosyncratic enterprise social network use patterns, minimize resistance and maximize effective utilization of the social platform among employees. This paper fulfills the need to identify important factors to be actively managed and manipulated to fully realize the benefits from the investment of enterprise social network

Will Cooperation Help Content Creators Grow? Empirical Evidence from Twitch.tv

The increasingly popular live streaming platforms have attracted many content creators through various revenue sharing and incentive mechanisms. Despite intense competition, many content creators actively cooperate with other competitors, even at the risk of losing their audience. In this study, we develop an integrated theoretical framework to examine the drivers for different types of cooperative behaviors and explore their impacts on market performance. By analyzing monthly activities of 412 cooperative pairs of channels on Twitch, we found a positive impact of specialty homophily, reciprocity, and social influence on content creators’ cooperative behaviors, although such impacts differ across different types of cooperative activities. Moreover, our results show that spreading behavior has a positive impact on content creators’ market performance, while supporting behavior has a negative impact. This negative effect becomes weaker when content creators are more popular. Our results offer important implications to foster a sustainable growth of UGC platforms

Diversification Strategy for Mobile App Developers: Understanding the Role of App Category Characteristics on App Performance

App developers adopt diversification strategy with their app-offerings to improve market performance and gain more revenue. The success of this strategy depends on different factors such as app category characteristics and competitive dynamics. As mobile app platforms are characterized by the presence of both positive and negative network effects, competition within an app category (i.e. category concentration) and the popularity of an app category, are expected to play a strong role in influencing apps’ performance. In this study, we focus on how category concentration and category popularity interact and influence the outcome of developers’ diversification. We find that releasing app in a category with higher category concentration will have a negative impact on an app’s performance, and that category popularity has a positive influence on an app’s performance. Considering the interaction between these two factors, we find that the negative influence of category concentration overwhelms the positive impact of category popularity. Implications for app developers are discussed