Calculative Trust and Interfirm Contracts
Management ScienceAnjana Susarla, Martin Holzhacker, Ranjani Krishnan
2020
Interfirm contracts are plagued by opportunism arising from exchange hazards that increase the seller’s gains from holdup in fixed price contracts. These exchange hazards are higher when the seller can engage in unverifiable deliberate obfuscation. Although cost-plus contracts reduce holdup losses, they suffer from cost inefficiency. Past research has underscored the importance of trust as a control instrument to mitigate losses from exchange hazards, especially social relational trust that develops from past experiences. However, trust can also be calculative when it develops from the expectation of future economic gains to the buyer-seller dyad. We identify two dyadic mechanisms that generate calculative trust and curtail the likelihood of cost-inefficient behavior in cost-plus contracts. These mechanisms include future potential and bilateral reputation capital for cost containment. Analysis using probit estimations on 149 information technology outsourcing contracts for the period 1998 to 2005 suggests that calculative trust increases the likelihood of cost-plus contracts. Thus, calculative trust can mitigate inefficiencies in interfirm contracts.
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No Pain, No Gain: Examining the Digital Resilience of the Fitness Sector During the COVID-19 Pandemic
SSRNUttara M Ananthakrishnan, Jenny Chen, Anjana Susarla
2020
The COVID-19 pandemic has had a negative impact on small businesses, substantially accelerating business failure and adversely affecting financial viability. With several sectors of the economy adopting digital transformation initiatives to deal with the pandemic, we consider digital resilience in the market for fitness clubs and gyms. The traditional economics of the fitness industry made them less vulnerable to digital disruption, pre pandemic. The pandemic also shifted patterns of human mobility, where gyms’ operating model was suddenly negatively impacted by digital substitution. We consider how these businesses adapted by being entrepreneurially alert and digitally agile in adopting virtualization and greater social media enabled connections with consumers, and how digitization substitutes for co-presence in the relationship between consumers and fitness centers. We examine these issues using a fairly granular data on foot traffic from Safegraph and combining it with data from Google Places, supplemented with hand collected data from individual websites.
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Understanding Content Contribution Behavior in a Geosegmented Mobile Virtual Community: The Context of Waze
Information Systems ResearchChenhui Guo, Tae Hun Kim, Anjana Susarla, Vallabh Sambamurthy
2020
We examine content creation in a geosegmented, crowdsourced social mobile virtual community app, Waze. We conceptualize a virtual and spatial factor, virtual crowdedness (defined as the density of Waze users in a particular geospatial location), and we examine its role in encouraging user contribution. We posit that the relationship between virtual crowdedness and user contribution is driven by the tension between audience effects and bystander/content saturation effects. We analyze a panel data set of user contributions on Waze from New York City to test our hypotheses. First, our findings indicate that although virtual crowdedness has a positive influence on total number of contributions, the magnitude of the influence decreases as virtual crowdedness increases. Second, the concave-down increasing relationship is more pronounced for rush hours with high physical crowdedness than for non-rush hours with low physical crowdedness.
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People Do Not Know What They Want Until You Show it to Them. But When?
SSRNMei Li, Xi Xiong, Xiangyu Chang, Anjana Susarla, Subodha Kumar
2021
Recommendation systems are critical tools for online retailers in their pursuit of enhanced operational performance and improved shopper experience. As such, firms invest heavily to improve their algorithms. Notwithstanding these efforts, there is usually a serious omission of demand-timing element in prevalent online recommendation systems. As a result, recommendations are often presented out of synchronization with the next demand cycle, leading to squandered marketing opportunities and customer dissatisfaction. In this research, we propose a novel demand-driven recommendation system that factors in predicted demand timing. The core of our novel design consists of a predictive model that forecasts product-level repurchase cycles for the online retail environment. We propose a new approach to incorporate the predicted repurchase cycles into three key recommendation generating stages: (i) \textit{retrieval}, (ii) \textit{ranking}, and (iii) \textit{re-ranking}.
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Online to Offline: The Impact of Social Media on Offline Sales in the Automobile Industry
Information Systems ResearchYen-Yao Wang, Chenhui Guo, Anjana Susarla, Vallabh Sambamurthy
2021
Given the limited research into the impact of social media on offline sales of durable goods, this study examines the dynamic relationships between firm-generated content (FGC), user-generated content (UGC), traditional media, and offline light vehicle sales. Data were collected from the official Facebook and Twitter pages of 30 U.S. car brands from 2009 to 2015. We utilized a panel vector autoregressive model to investigate the dynamic relationships among multiple time series variables while controlling for influential durable goods characteristics. The empirical results suggest that Facebook and Twitter have heterogeneous effects on offline vehicle sales. Moreover, FGC is more effective than UGC for influencing offline light vehicle sales. Viral impressions from Facebook and Twitter are essential, although the effects vary by social media platform (Facebook versus Twitter) and content type (FGC versus UGC).
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