Value Creation Tradeoff in Business Ecosystems: Leveraging Complementarities While Managing Interdependencies

2023 | Organization Science | Citations: 0

Authors: Agarwal, Shiva; Kapoor, Rahul

Abstract: Complementary assets play an important role in shaping an innovation’s commercia ... Expand

Abstract: Complementary assets play an important role in shaping an innovation’s commercialization success. In this paper, we broaden the locus of complementarities to examine the role of complementary technologies residing in the business ecosystems that are becoming an important source of value creation for innovating firms. We argue that, on one hand, complementary technologies help innovations create more value for their users. On the other hand, they can also limit the focal innovation’s value creation by exposing them to performance bottlenecks as the underlying technological architecture of the ecosystem evolves. We further extend the notion of specialization of complementary assets to ecosystems by considering complementary technologies that are specialized to a focal ecosystem and those that are available across multiple ecosystems. We highlight that, although the complementary technologies that are specialized to an ecosystem facilitate greater value creation, they are more likely to subject the focal innovation to performance bottlenecks. Evidence from 244,034 apps launched by software developers for Apple’s iPhone ecosystem during 2008–2015 offers strong support for our framework. In summary, the study sheds light on the value creation tradeoff for firms innovating in business ecosystems—the opportunities associated with leveraging complementarities and the challenges associated with managing technological interdependencies. Funding: S. Agarwal was funded by the McCombs School of Business at The University of Texas at Austin [Research Excellence Grant]. Collapse

Topics: mobile application innovation management value creation Apple iPhone iOS

Methods: statistical hypothesis test instrumental variables estimation cross sectional research proportional hazards model robustness check

Symbolic Shareholder Democracy: Toward a Behavioral Understanding of the Role of Shareholder Voting in CEO Dismissals

2023 | Organization Science | Citations: 0

Authors: Andrei, Alina G.; van Oosterhout, J. (Hans); Sauerwald, Steve

Abstract: We investigate the effect of expressive shareholder dissent voting, in which sha ... Expand

Abstract: We investigate the effect of expressive shareholder dissent voting, in which shareholders use their votes symbolically to express their discontent with management, on subsequent chief executive officer (CEO) dismissals. Using the routine but highly symbolic executive board discharge proposal voted on at the annual shareholder meetings of German firms, we argue that the board of directors understands these votes as a “vote of confidence in management” that challenges the CEO’s mandate to lead the firm. Arguing that board chairs are uniquely positioned to take up the stance of a steward of the firm and its leadership, we examine how independent and family board chairs moderate the board’s response to expressive voting dissent. Using a sample of German public firms over the period 2008–2015, we find that expressive voting dissent increases the chance of CEO dismissal increasingly with the level of dissent expressed. Contrary to prevailing agency theoretical expectations, we do not find that independent chairs are more responsive to expressive voting dissent, nor that this relationship is strengthened by the degree of minority institutional investor ownership of the firm. Consistent with the symbolic perspective on shareholder voting that we seek to develop, however, we find that family chairs are more likely to lead the board to dismiss the CEO due to the intrinsic disvalue they incur from symbolic leadership legitimacy challenges in their firms, and that the positive effect of having a family chair on the dissent induced chance of CEO dismissal is strengthened by the level of family ownership in the firm. Funding: A University of Illinois at Chicago Summer Research Grant is gratefully acknowledged for S. Sauerwald. Collapse

Topics: corporate governance financial performance accounting organizational productivity

Methods: theory development statistical hypothesis test post-hoc analysis longitudinal research survival analysis

Theories: stewardship theory behavioral theory

Optimizing Substance Use Treatment Selection Using Reinforcement Learning

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Baucum, Matt; Khojandi, Anahita; Myers, Carole; Kessler, Larry

Abstract: Substance use disorder (SUD) exacts a substantial economic and social cost in th ... Expand

Abstract: Substance use disorder (SUD) exacts a substantial economic and social cost in the United States, and it is crucial for SUD treatment providers to match patients with feasible, effective, and affordable treatment plans. The availability of large SUD patient datasets allows for machine learning techniques to predict patient-level SUD outcomes, yet there has been almost no research on whether machine learning can be used to optimize or personalize which treatment plans SUD patients receive. We use contextual bandits (a reinforcement learning technique) to optimally map patients to SUD treatment plans, based on dozens of patient-level and geographic covariates. We also use near-optimal policies to incorporate treatments’ time-intensiveness and cost into our recommendations, to aid treatment providers and policymakers in allocating treatment resources. Our personalized treatment recommendation policies are estimated to yield higher remission rates than observed in our original dataset, and they suggest clinical insights to inform future research on data-driven SUD treatment matching. Collapse

Topics: electronic health record individual characteristic healthcare data missing data

Methods: hazard function machine learning random forest classification descriptive statistic propensity score method

When Does the Pre-entry Experience of New Entrants Improve Their Performance? A Meta-Analytical Investigation of Critical Moderators

2023 | Organization Science | Citations: 0

Authors: Cao, Zhi; Posen, Hart E.

Abstract: Although pre-entry experience is widely regarded as a critical asset that positi ... Expand

Abstract: Although pre-entry experience is widely regarded as a critical asset that positively influences new entrant performance, empirical support is mixed. To address this inconsistency, we conduct a meta-analysis of the empirical findings in 272 papers. We draw theoretically on the organizational learning literature to argue that the pre-entry experience–new entrant performance relationship is contingent on the characteristics of pre-entry experience, the environmental context of the new entrant, and the interaction between the two. In particular, we examine the effects of two levels of pre-entry experience (firm and founder), four types of founder-level pre-entry experience (entrepreneurial, managerial, industry, and functional experience), and two types of environments (industry and institutional). The meta-analysis results show a significant and positive correlation between founder-level pre-entry experience and economic performance of 0.07. Likewise, the failure rates of spinouts and diversifying entrants are 11% lower than that of start-ups. The moderating analysis results show that the correlation of founder-level pre-entry experience and economic performance is lower in knowledge- or technology-intensive (KTI) industries and higher in low-KTI manufacturing and service industries. The correlation is also higher in institutional environments with high power distance and individualism. These findings provide compelling new evidence for the importance of pre-entry experience and advance our understanding of the boundary conditions on the pre-entry experience–new entrant performance relationship. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1589. Collapse

Topics: national culture organization learning entrepreneurship productivity research and development

Methods: descriptive statistic hazard function meta analysis literature sample chi squared test

Maximizing Online Revisiting and Purchasing: A Clickstream-Based Approach to Enhancing Customer Lifetime Value

2023 | Journal of Management Information Systems | Citations: 0

Authors: Jabr, Wael; Ghoshal, Abhijeet; Cheng, Yichen; Pavlou, Paul

Abstract: Online retailers are increasingly focused on maintaining a long-term relationshi ... Expand

Abstract: Online retailers are increasingly focused on maintaining a long-term relationship with customers, encouraging repeat visits rather than single-time purchases to increase customer lifetime value. To help retailers maximize the probabilities of customers' revisiting and purchasing, we develop a two-stage model to better characterize and predict these two fundamental customer activities. In the first stage, we characterize the propensity of a customer revisiting the retailer's website. In the second stage, we develop a stochastic model that predicts revisits while also incorporating individual customer heterogeneity in exerted search effort during repeated visits. This heterogeneity is based on individual customer preferences in the choice of consideration sets, product information, pricing, and the search environment. Using customer level clickstream data, we show that our approach is not only better at predicting repeat customer visits, compared to existing methods, but also explainable and managerially interpretable. Most importantly, using computationally efficient simulation-based prescriptive analytics, we leverage our modeling approach to propose practical intervention strategies that maximize the joint likelihoods of customers revisiting and purchasing at the individual customer level. Collapse

Topics: website price management mobile system accounting logistics management

Methods: simulation logistic regression proportional hazards model cluster analysis machine learning

Could Gamification Designs Enhance Online Learning Through Personalization? Lessons from a Field Experiment

2023 | Information Systems Research | Citations: 1

Authors: Leung, Alvin Chung Man; Santhanam, Radhika; Kwok, Ron Chi-Wai; Yue, Wei Thoo

Abstract: The COVID-19 pandemic has highlighted the importance of online learning. As lear ... Expand

Abstract: The COVID-19 pandemic has highlighted the importance of online learning. As learner autonomy is relatively high in online environments, learners must engage in self-regulated learning (SRL) to achieve optimal learning outcomes. Because most learners are unable to consistently engage in SRL, gamification interventions are being implemented to improve SRL engagement; however, mixed results cast doubt on the efficacy of this approach. Massively open online courses (MOOCs), a type of online learning environment, are currently experiencing rapid growth due to widespread adoption by many institutions. In MOOCs, there is no instructor intervention; hence, students have difficulty regulating their own learning and are easily distracted. Therefore, this study investigates whether mixed-research results regarding the efficacy of gamification can be attributed to lack of attention to individual learner traits during design. For this purpose, the study analyzes MOOCs as an instance of online learning by applying SRL theory and gamification principles. We altered a traditional MOOC platform to provide different types of gamified performance feedback to facilitate learners’ SRL engagement. We then examined whether this matched with goal orientation, an individual learner trait to influence SRL and learning outcomes. Using learning-analytics tools, we tracked 760 college students’ SRL engagement on a MOOC platform over five weeks. As theorized, SRL engagement and learning outcomes of participants who had a strong performance-avoidance goal orientation increased with positively framed performance feedback that involved no social comparisons; however, the same feedback had a negative impact on participants with a strong mastery goal orientation. Our findings add to SRL theory by demonstrating that gamification designs can enhance SRL engagement and learning outcomes in online learning, but with a caveat—this occurs only when there is a match with learner traits—confirming the gamification principle stating that task improvements and meaningful engagement can only occur through thoughtful gamification design.History: Raghu Santanam, Senior Editor; J. J. Hsieh, Associate Editor.Funding: Financial supported from the University Grants Committee’s (UGC’s) Special Grant for the Strategic Development of Virtual Teaching and Learning [Project Number 6430900], the UGC Teaching and Learning Funding [Project Numbers 6391211 and 6391001], and the Digital Innovation Laboratory of Department of Information Systems, City University of Hong Kong is gratefully acknowledged.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1123. Collapse

Topics: gamification online learning system electronic mail IT skill business intelligence

Methods: experimental group pilot experiment survey experiment digital trace data

Theories: learning theory

How Lending Experience and Borrower Credit Influence Rational Herding Behavior in Peer-to-Peer Microloan Platform Markets

2023 | Journal of Management Information Systems | Citations: 0

Authors: Lowry, Paul Benjamin; Xiao, Junji; Yuan, Jia

Abstract: This paper analyzes the herding behavior that characterizes lenders' lending dec ... Expand

Abstract: This paper analyzes the herding behavior that characterizes lenders' lending decisions on a microloan platform and explains how rational herding behavior can resolve the information-asymmetry problem, which is a well-known reason for the failure of online microloan platforms. Using a set of panel data on individual lending decisions acquired from Paipaidai.com (PPDai), an online microloan platform, we examine the influence of the lending decisions of prominent, experienced lenders on novice lenders to identify rational herding behavior. Our empirical analysis demonstrates that rational herding behavior can in fact efficiently reduce lender loss from borrower defaults caused by limited information. Although it is typically assumed that herding behavior is irrational, we find that it can be rational in this context and can thus shed light on why PPDai has succeeded while most other microloan platforms have failed. Accordingly, we make three key contributions: 1) we use heterogeneous herding effects to empirically determine whether lenders' herding behavior on PPDai is rational based on observational learning; 2) we investigate the moderating effect of borrower credit and novice-lender experience on herding, and we leverage this heterogeneity in lender experience to better explain loan results; and 3) because PPDai publicly provides potential lenders with a transparent credit score—in contrast to platforms like Prosper.com, which leverage hidden proprietary credit information from Experian—we further analyze the credit composition of prominent lenders to better understand the crucial determinants of rational herding. In fact, our follow-up survival simulations indicate that without rational herding, the total number of successful PPDai loans would have decreased by around 46 percent during the study period—a finding that further underlines the crucial influence of rational herding and the unique contextual factors of PPDai that have fostered it. Collapse

Topics: peer-to-peer model decision making peer-to-peer lending crowdsourcing data breach

Methods: simulation survival function proportional hazards model longitudinal research hazard function

Theories: lemon market theory loss aversion

Cryptocurrency Rewards and Crowdsourcing Task Success

2023 | HICSS | Citations: 0

Authors: Meng, Shan; Zhao, Xia; Zhao, Xi

Abstract: Crowdsourcing task success depends on the contributions of developers. How to id ... Expand

Abstract: Crowdsourcing task success depends on the contributions of developers. How to identify capable developers and motivate them to actively contribute to a task is a challenging issue. This study investigates how the use of cryptocurrency rewards, i.e., the choices of stablecoins and unstablecoins affects the crowdsourcing task success, and how the relationship depends on task difficulty. Based on 3858 crowdsourcing tasks, we find that the use of unstablecoins reduces the number of participating contributors and extends the time period of having the first contributor, but has no significant effect on the likelihood of task success. In addition, task difficulty alleviates the negative effect of the unstablecoins on the number of participants. Our study potentially provides important implications for the use of cryptocurrency tokens as task rewards. Collapse

Topics: crowdsourcing cryptocurrency stablecoin task characteristic open source

Methods: experimental group regression analysis method propensity score matching survival analysis hazard function

From Opaque to Accountable Governance: Investor Activism for Transparency in Social Media Amid Disruption in Cryptocurrency

2023 | International Conference on Information Systems | Citations: 0

Authors: Myeong, Jaeho; Jung, Miyeon; Auh, Seigyoung; Han, Sang Pil

Abstract: ... Expand

Abstract: Collapse

Topics: Twitter social media cryptocurrency blockchain Bitcoin

Methods: proportional hazards model survival analysis mediation analysis longitudinal research econometric modeling

Theories: uncertainty reduction theory

The role of web browsing in credit risk prediction

2023 | Decision Support Systems | Citations: 0

Authors: Rozo, Betty Johanna Garzon; Crook, Jonathan; Andreeva, Galina

Abstract: Online mail order and online retail purchases have increased rapidly in recent y ... Expand

Abstract: Online mail order and online retail purchases have increased rapidly in recent years worldwide, with Covid-19 forcing almost all non-grocery shopping to move online. These practices have facilitated the availability of new data sources, such as web behavioural variables providing scope for innovation in credit risk analysis and de­ cision practices. This paper examines new web browsing variables and incorporates them into survival analysis as predictors of probability of default (PD). Using a large sample of purchase and repayment credit accounts from a major digital retailer and financial services provider, we show that these new variables enhance the predictive accuracy of probability of default (PD) models at account level. This also holds in the absence of credit bureau data, therefore, the new information can help people who may not have a credit history (thin file) who cannot be assessed using traditional variables. Moreover, we leverage on the dynamic nature of these new web variables and explore their predictive value in short and long- term horizons. By adding macroeconomic variables, the possibility for stress-testing is provided. Our empirical findings provide insights into web browsing behaviour, highlight how the inclusion of non-standard variables can improve credit risk scoring models and lending de­ cisions and may provide a solution to the thin files problem. Our results also suggest a direct value added to the online retail credit industry as firms should leverage the increasing trend of consumers embracing the digital environment. Collapse

Topics: website mobile system electronic commerce personality risk management

Methods: machine learning survival analysis parametric test hazard function Student's t-test