Cheng Suang Heng

De-escalation of commitment in software projects: who matters? what matters?

Through de-escalation of commitment (DoC), software projects with poor prospects for success may be sensibly abandoned and precious resources channeled to more productive uses. In spite of its importance, there has been little research on factors contributing to DoC in software projects. Using a laboratory experiment, this study investigates the impact of individuals (superiors and peers) and approach (shoulder blame and provide assurance) on DoC under varying conditions of sunk cost (high versus low). Results showed that, under conditions of low sunk cost, superiors who helped to shoulder blame (the shelter strategy) or provide assurance (the support strategy) appeared to be useful in facilitating DoC. Peers who helped to shoulder blame (the sharing strategy) or provide assurance (the sympathy strategy) also appeared to be useful in facilitating DoC, with the former strategy being particularly effective. But under conditions of high sunk cost, none of these four strategies appeared to be able to facilitate DoC. Implications of these results for practice are discussed.

The Paradoxes of Word of Mouth in Electronic Commerce

Abstract Challenging conventional wisdom, we unravel three paradoxes of word of mouth (WOM) in e-commerce. Specifically, the WOM valence paradox contends that higher WOM valence of a product results in a larger subsequent decrease in the WOM valence of the product, the WOM volume paradox propounds that higher WOM volume of a product results in a smaller subsequent increase in the WOM volume of the product, and the WOM spillover paradox proposes that an improvement in the WOM of a product also improves the WOM of connected products in a product network. These paradoxes caution online retailers that superior WOM may at times backfire and not boost further sales. Drawing theoretical support from expectation-confirmation theory and network theory, we collect data from China’s largest business-to-consumer platform, Tmall.com, and use linear panel data models to examine WOM evolution in a product network, controlling for relevant factors at the individual product, product network, and time unit levels. Importantly, we base our identification strategies on the use of instrumental variables and the difference-in-differences estimation approach. Numerous statistical checks confirm the robustness and consistency of our findings. We contribute to a much richer theoretical understanding of WOM, by extending the applicability of expectation-confirmation theory and network theory to novel predictions and contexts, adding a dynamic perspective, unveiling three important WOM paradoxes, and offering practical insights.

The demand effects of product recommendation networks: an empirical analysis of network diversity and stability

With the increasing popularity of product recommendation networks in e-commerce, researchers and practitioners are eager to understand how they can strategically manage product assortments through the manipulation of such networks to drive demand. We examine product recommendation networks in e-commerce to investigate how the demand of a product is influenced by product network attributes in terms of network diversity and network stability. We also examine whether the demand of a product is influenced by both the incoming network and the outgoing network, and if the effects differ between co-view and co-purchase recommendation networks. Using data from Tmall.com for four product categories, we apply linear panel data models to examine the impact of network diversity and network stability on product demand, controlling for relevant factors at the individual product, pricing, product network, product category, and time unit levels. Importantly, we account for implicit demand correlation (i.e., substitution and complementarity) and potential simultaneity of demand and network structures. We unravel several important findings. First, a 1% increase in the category diversity of the incoming (outgoing) co-purchase network of a product is associated with a 0.011% (0.012%) increase (decrease) in the products demand. Second, a 1% increase in the stability of the outgoing co-purchase network is associated with a 0.012% decrease in demand. Third, the demand effects of network diversity and stability are both stronger in the co-purchase network, compared to their insignificant effects in the co-view network. Thus, this research provides theoretical contributions in terms of the economic effects of product recommendation networks through its focus on network diversity and stability in incoming/ outgoing and co-view/co-purchase networks. We also provide notable implications for recommendation-based product marketing and recommendation systems design.

Effect of multi-vendor outsourcing on organizational learning

The growing trend of engaging multiple vendors, coupled with the concern over possible loss of knowledge in information technology (IT) outsourcing relationships, compels us to investigate organizational learning in multi-vendor outsourcing from a social relation perspective. Social relation has been conceptualized in terms of how organizational relationships with social entities facilitate and/or inhibit organizational learning. In particular, we examine how two different relational structuressingle vendor dominant model and multi-vendor dominant modelare associated with two types of organizational learning, namely, exploitative and exploratory. Furthermore, we investigate how three critical dimensionsstructural (core vs. non-core functions), affective (partnership vs. transaction), and cognitive (business value vs. IT capability)in social relation affect the relationships between the two relational structures and the two types of organizational learning in the context of IT outsourcing. The proposed hypotheses were tested using 203 Korean client firms that have adopted the multi-vendor approach in IT outsourcing. Results reveal that the single vendor dominant model exerts a more significant effect on exploitative learning than exploratory learning. By contrast, the multi-vendor dominant model has a more significant effect on the latter than the former. Furthermore, out of the six moderating variables, four (core IT function, non-core IT function, partnership relationship, and transaction relationship) have significant influences on exploitative and exploratory learning. We conclude with theoretical contributions in the area of social relation theory and IT outsourcing literature, and implications for practices on how clients can enhance engagement in exploitative and exploratory learning to reap the benefits of multi-vendor outsourcing.