Managing digital business platforms: A continued exercise in balancing renewal and refinement

Central to the evolution of a digital business platform is the organization’s ability to balance exploration (renewal) and exploitation (refinement) simultaneously. Drawing on prior research—including digital platforms, contradictory tensions, and organizational ambidexterity—and our own experience investigating digital business platforms in organizations, this article provides insights into how executives can manage this evolution successfully. More specifically, we present a framework recognizing three pairs of organizational capabilities (i.e., identifying-nourishing, expanding-legitimating, and augmenting-embedding) that enable balancing renewal and refinement of the platform over time. We close by providing critical managerial practices that executives can use in anticipating, adjusting, and evaluating the evolution of a digital business platform over time, including its initiation, development, and growth.     

Does accounting conservatism pay?

We investigate whether or not there is a link between conservative accounting practices and the sensitivity of executive pay to accounting performance. Using several accrual‐based measures of accounting conservatism as well as alternative measures of accounting performance, we estimate an econometric model of CEO compensation that incorporates the interaction of accounting conservatism and accounting performance. Consistent with optimal contracting theory, we find that the sensitivity of executive pay to accounting performance is higher for firms that report conservative accounting earnings. These results support the hypothesis that accounting conservatism, by limiting earnings management opportunities and improving the reliability of accounting performance measures, allows firms to formulate contracts that tie executive compensation more closely to accounting performance.     

Linking Order Fulfillment Performance to Referrals in Online Retailing: An Empirical Analysis

Referral management for online retailers is a strategically important activity, as referrals offer a highly cost‐effective method of customer acquisition. In fact, online customers mention referrals as the second‐most common reason for choosing to shop at a particular retailer, second only to search engine suggestions. However, while online retailers are able to improve their visibility on search engines through focused keyword insertions, they are often unable to pinpoint the drivers of referral behavior among their customers. The current research examines the relationship between two key dimensions of online retailing: order fulfillment cycle times and referral behavior. Employing a theory of customer appraisal and empirically testing the ensuing model using structural equation modeling, we find that excellent order fulfillment is instrumental in generating referrals for the online retailer, even after factoring in product quality.     

Leveraging Information Across Categories

Companies are collecting increasing amounts of information about their customers. This effort is based on the assumption that more information is better and that this information can be leveraged to predict customers' behavior in a variety of situations and product categories. For example, information about a customer's purchase behavior in one category can be helpful in predicting his potential behavior in a related category, which in turn could help a firm in its cross-selling efforts. In this paper, we present a model to better understand and predict a consumer's purchases and preferences when we may have limited or no information about him in one or more product categories. Conceptually this involves leveraging information from purchases of other consumers in multiple categories as well as partial information (e.g., purchase in one of the categories) of the target consumer. Our approach builds on the pioneering work of (Rossi et al. (1996)) who demonstrate the value of purchase information in the context of a single product category. We present results from an extensive simulation as well as an application on scanner panel data. Our simulation shows many interesting and somewhat surprising results. Specifically, we find that compared to a single-category analysis, a cross-category analysis does not lead to any significant improvement in data likelihood in most cases. Therefore, the single-category analysis of (Rossi et al. (1996)) is even more powerful than previously thought. However, we also find that a cross-category analysis does improve parameter recovery in many situations as compared to a single-category analysis. It is in these conditions that retailers can use cross-category information to better implement micro marketing programs. We demonstrate the transfer of information across categories in an application of two grocery products—Breakfast Foods and Table Syrup. In spite of a reasonable correlation (0.21) in the price parameter across these two categories, our simulation guidelines predict very little benefit of cross-category analysis over single-category analysis. Our empirical results confirm this prediction.     

Self‐selection,endogeneity, and the relationship between CEO duality and firm performance

This study focuses explicitly on the methodological implications of the endogenous theory of governance as applied to firm performance. In particular, if firms choose their governance structures as part of a constrained performance maximization process, then application of an appropriate empirical methodology should reveal statistical evidence of such behavior. In this study we take advantage of the endogenous switching regression model framework to determine whether such predicted optimizing behavior can be corroborated by the data. The model allows us to test explicitly for selection behavior in accordance with comparative advantage and, concomitantly, the presence of selectivity bias, in estimating the impact of CEO duality on firm performance. The selection and performance equations are modeled in accordance with the extant accounting, economics, and management literature on the impact of the dual governance structure on firm performance. Overall, we tested four performance measures for the entire sample of firm‐year observations as well as for the largest three industries in terms of sample sizes. The major finding, robust in all cases, is that there is no evidence to support a contention that CEO duality is a structure purposefully chosen for optimizing performance. If firms are indeed choosing the dual leadership structure, they are doing so for reasons other than improving performance from what it would be otherwise. In fact, for performance measured as market return and earnings per share, there is evidence of a significant selectivity bias that acts to lower performance below what it would have been under random assignment. For performance measured by Tobin's q and return on assets, we found neither evidence of selectivity bias, nor any significant marginal performance impacts of CEO duality. Such findings are inconsistent with an endogenous governance theory, at least when applied to firm performance. Copyright © 2009 John Wiley & Sons, Ltd.     

Damaging democracy? Security provision and turnout in Afghan elections†

In emerging democracies, elections are encouraged as a route to democratization. However, not only does violence often threaten these elections, but citizens often view as corrupt the security forces deployed to combat violence. We examine the effects of such security provision. In Afghanistan's 2010 parliamentary election, polling centers with similar histories of pre‐election violence unintentionally received different deployments of the Afghan National Police, enabling identification of police's effects on turnout. Using data from the universe of polling sites and various household surveys, data usually unavailable in conflict settings, we estimate increases in police presence decreased voter turnout by an average of 30%. Our results adjudicate between competing theoretical mechanisms through which security forces could affect turnout, and show behavior is not driven by voter anticipation of election‐day violence. This highlights a pitfall for building government legitimacy via elections in weakly institutionalized and conflict‐affected states.     

Learning under supervision: an experimental study

In many market environments, for example in investment banking, salesforce management and others, workers and supervisors work closely as a team. Workers are paid a fixed salary and supervisors determine any raises, which are typically dependent on how well the organization does. In such scenarios, a supervisor who constantly offers suggestions can create a problem—typically a worker cannot ignore his supervisor’s advice, yet if such advice is wrong and is followed, it will only decrease firm profits. We conduct a laboratory experiment to address a question critical for such settings—does the relationship between advisor and worker interfere with the learning abilities of the worker? The answer is a resounding no. In fact, subjects who have a supervisor advising them and whose advice is costly to ignore actually learn better than those with an advisor whose advice can be ignored. An even more striking result is that advisees as well as advisors in both these conditions learn better than subjects with no advisors. Our result can be attributed to the presence of advice and has direct relevance to learning in many environments.     

Putting one-to-one marketing to work: Personalization, customization, and choice

The tailoring of a firm’s marketing mix to the individual customer is the essence of one-to-one marketing. In this paper, we distinguish between two forms of one-to-one marketing: personalization and customization. Personalization occurs when the firm decides what marketing mix is suitable for the individual. It is usually based on previously collected customer data. Customization occurs when the customer proactively specifies one or more elements of his or her marketing mix. We summarize key challenges and knowledge gaps in understanding both firm and customer choices in one-to-one markets. We conclude with a summary of research opportunities.     

Cash Flow Matching

Abstract

We propose a scenario-based optimization framework for solving the cash flow matching problem where the time horizon of the liabilities is longer than the maturities of available bonds and the interest rates are uncertain. Standard interest rate models can be used for scenario generation within this framework. The optimal portfolio is found by minimizing the cost at a specific level of shortfall risk measured by the conditional tail expectation (CTE), also known as conditional valueat-risk (CVaR) or Tail-VaR. The resulting optimization problem is still a linear program (LP) as in the classical cash flow matching approach. This framework can be employed in situations when the classical cash flow matching technique is not applicable.