This study examines whether the framing mode (narrow versus broad) influences the stock investment decisions of individual investors. Motivated by the experimental evidence, which suggests that separate decisions are more likely to be narrowly framed than simultaneous decisions, we propose trade clustering as a proxy for narrow framing. Using this framing proxy, we show that investors who execute more clustered trades exhibit weaker disposition effects and hold better-diversified portfolios. We also find that the degree of trade clustering is related to investors' stock preferences and portfolio returns. Collectively, the evidence indicates that the choice of decision frames is likely to be an important determinant of investment decisions.
While research on the cost effectiveness of standardization to date has focused on the impact of different degrees of standardization, it has paid insufficient attention to the process an organization uses to formulate and implement standardized procedures. Drawing on the organizational decision-making literature and procedural fairness literature, this study identifies a number of key process features in standardization and argues that variations in these process features across organizations help account for the varying success of standardization in achieving cost effectiveness. Using hospital drug standardization for coronary artery and pneumonia cases to ground much of the argument, I analyzed inpatient discharge data from Florida, Illinois, New York, and Texas, combined with an original survey of 243 hospital pharmacy directors. The results indicated that an increasing degree of standardization was associated with cost effectiveness when the level of formal objectivity in creating the standardized procedure was high and when there was due process in resolving disputes about the standardized procedure. This finding broadly supports the argument that the cost effectiveness of standardization depends not just on the degree of standardization but also on the process by which the standardized procedures are created and implemented.
Public officials with the authority to order hurricane evacuations face a difficult trade-off between risks to life and costly false alarms. Evacuation decisions must be made on the basis of imperfect information, in the form of forecasts. The quality of these decisions can be improved if they are also informed by measures of uncertainty about the forecast, including estimates of the value of waiting for updated, more accurate, forecasts. Using a stochastic model of storm motion derived from historic tracks, this paper explores the relationship between lead time and track uncertainty for Atlantic hurricanes and the implications of this relationship for evacuation decisions. Typical evacuation clearance times and track uncertainty imply that public officials who require no more than a 10% probability of failing to evacuate before a striking hurricane (a false negative) must accept that at least 76%—and for some locations over 90%—of evacuations will be false alarms. Reducing decision lead times from 72 to 48 hours for major population centers could save an average of hundreds of millions of dollars in evacuation costs annually, with substantial geographic variation in savings.
Product recommendation models are key tools in customer relationship management (CRM). This study develops a product recommendation model based on the principle that customer preference similarity stemming from prior purchase behavior is a key element in predicting current product purchase. The proposed recommendation model is dependent on two complementary methodologies: joint space mapping (placing customers and products on the same psychological map) and spatial choice modeling (allowing observed choices to be correlated across customers). Using a joint space map based on past purchase behavior, a predictive model is calibrated in which the probability of product purchase depends on the customer's relative distance to other customers on the map. An empirical study demonstrates that the proposed approach provides excellent forecasts relative to benchmark models for a customer database provided by an insurance firm.
Call center managers are facing increasing pressure to reduce costs while maintaining acceptable service quality. Consequently, they often face constrained stochastic optimization problems, minimizing cost subject to service-level constraints. Complicating this problem is the fact that customer-arrival rates to call centers are often time varying. Thus, to satisfy their service goals in a cost-effective manner, call centers may employ permanent operators who always provide service, and temporary operators who provide service only when the call center is busy, i.e., when the number of customers in system increases beyond a threshold level. This provides flexibility to dynamically adjust the number of operators providing service in response to the time-varying arrival rate. The constrained dynamic operator staffing (CDOS) problem involves determining the number of permanent and temporary operators, and the threshold value(s) that minimize time-average hiring and opportunity costs subject to service-level constraints. We model the CDOS problem as a constrained Markov decision process (MDP) and seek the optimal nonrandomized policy. The only exact method in the literature to obtain the optimal nonrandomized policy for a constrained MDP is enumeration, which is often computationally prohibitive. We provide a novel exact and efficient solution method, the modified balance equations disjunctive constraints (MBEDC) algorithm, yielding a mixed-integer program formulation; the computation times of this algorithm for sample problems are lower than enumeration by up to a factor of 200, and by a factor of 10 on average. Using our algorithm, we quickly solve diverse instances of the CDOS problem, generating managerial insights into the effects of temporary operators and service-level constraints.
This study examines cooperative standard setting in wireless telecommunications. Focusing on the competition among firms to influence formal standardization, the roles of standard-setting committees, private alliances, and industry consortia are highlighted. The empirical context is Third Generation Partnership Project (3GPP), an international standards-development organization in the wireless telecommunication industry. Panel data analyses exploiting natural experiments caused by a consortium merger and entry of Asian firms suggest that participation in industry consortia increases firms' contributions to the development of new technical specifications in 3GPP committees. Moreover, connections to standard-setting peers formed in consortia facilitate change requests to ongoing specifications. These results suggest that if firms in network technology industries want to influence the evolution of their industry, they should identify both formal standard-setting committees and industry consortia in which they can discuss, negotiate, and align positions on technical features with their peers. For policymakers, these results suggest that it is important to ensure that technical consortia remain open for all industry actors and that membership fees do not become prohibitive to small and resource-constrained players.
Sales technology has been touted as a primary tool for enhancing customer relationship management. However, empirical research is sparse concerning the use of information technology (IT) and its effects on the relationship between salespersons and customers. Using an interdisciplinary research approach, we extend task-technology-fit (TTF) theory by examining the mechanisms through which use of IT by the sales force influences salesperson performance. We test a model that incorporates salespersons' customer service, attention to personal details, adaptability, and knowledge—key marketing constructs that could mediate IT's impact on salesperson performance. Results in a pharmaceutical sales setting indicate that IT use can improve customer service and salespersons' adaptability, leading to improved sales performance.
We provide new rationales for corporate venturing, based on competition for talented managers. As returns to venturing increase, firms engage in corporate venturing for reasons other than capturing these returns. First, higher venturing returns increase managerial compensation, to which firms respond by increasing incentives. Managers increase effort, prompting firms to reallocate them to new ventures, where the marginal product of effort is highest. Second, as returns to venturing become large, corporate venturing emerges as a way to recruit/retain managers who would otherwise choose alternative employment. We derive several testable empirical predictions about the determinants and structure of corporate venturing.
We model financial contracting in entrepreneurial ventures. In our incomplete contracts framework, the entrepreneur can design contracts contingent on three possible control right allocations: entrepreneur control, investor control, and joint control, with each allocation inducing different effort levels by both the entrepreneur and the investor. We find that a variety of contracts resembling financial instruments commonly used in practice, such as common stock, straight and convertible preferred equity, and secured and unsecured debt, can emerge as optimal, depending on two key factors: entrepreneur/investor effort complementarity and investors' opportunity cost of capital. The results of our model are consistent with, and yield new explanations for, empirical regularities such as (a) the prevalence of equity-type contracts in high-growth ventures and of debt-type contracts in lifestyle ventures; (b) geographical and temporal differences in equity-type instruments used in high-growth ventures; and (c) the impact of firm and loan characteristics on the choice between secured and unsecured debt.
The most prevalent form of training call center agents is via classroom instruction coupled with role-plays. Role-play training has a theoretical base in behavior modeling that entails observation, practice, and feedback. Emerging simulation-based technologies offer enhancements to behavior modeling that are absent in role-play training. This study evaluates the effectiveness of simulation-based training (henceforth, simulation training) as a behavior modeling technique vis-à-vis role-play training in a real-world call center environment across tasks of different levels of complexity. We collaborate with call centers at two Fortune 50 firms and examine on-job performance metrics to evaluate the effectiveness of simulation training. The performance measures of interest are call accuracy and call duration because these are two important factors that influence customer satisfaction and productivity in call center operations. After controlling for factors such as trainee's learning and technology orientation, age, education, and call center experience, results show that simulation training outperforms role-playing-based training in terms of both accuracy and speed of processing customer calls. Further, the relative superiority of simulation training improves at higher levels of task complexity.