Antecedents of salesperson effectiveness and efficiency performance: A data envelopment analysis

The objective of this dissertation was to (1) measure salesperson efficiency; (2) investigate both personal and organizational factors that determine salesperson efficiency; and (3) investigate both personal and organizational factors that determine salesperson effectiveness. Salesperson efficiency was assessed by data envelopment analysis (DEA). Two different DEA models were employed in order to increase the reliability of the efficiency results. Antecedents of salesperson efficiency and effectiveness were tested using Tobit regression analysis and ordinary least square regression analysis, respectively. These antecedents include not only personal level variables such as working smart, working hard, learning goal orientation, and performance goal orientation, but also organizational variables such as organizational culture, sales force control systems, and training. The sample frame consisted of a national sample of insurance agents who subscribed to Life Insurance Selling magazine. A self-report questionnaire was mailed to a stratified random sample of 1,000 potential respondents. The life insurance professionals were sent the study questionnaire three times. The resulting response rate was 23.00% in the present study. At the individual level of analysis, this study provides evidence that engaging in working smart behaviors enhances salesperson efficiency. While working hard was found to positively influence salesperson effectiveness, working smart was found to make salespeople more efficient and effective in selling. These results are a distinct contribution to the personal selling research literature. The results also indicate that a learning goal orientation enhances salesperson efficiency and effectiveness. In addition, the relationship between performance goal orientation and effectiveness was found to be moderated by salesperson self-efficacy. At the organizational level, this study found that the clan organizational culture type negatively influences salesperson effectiveness, while the market culture type positively influences efficiency. While past studies have found that organizational culture directly influenced organizational performance, the current study was the first to find a direct influence on individual performance. Additionally, behavior control systems were found to enhance salesperson efficiency and positively influence, although marginally, salesperson effectiveness. Finally, the application of data envelopment analysis in sales research was extended. This study showed how DEA can be used to measure individual salesperson efficiency and subsequently identify those variables that influence this important measure of salesperson performance

Leveraging Deep-learning and Field Experiment Response Heterogeneity to Enhance Customer Targeting Effectiveness

Firms seek to better understand heterogeneity in the customer response to marketing campaigns, which can boost customer targeting effectiveness. Motivated by the success of modern machine learning techniques, this paper presents a framework that leverages deep-learning algorithms and field experiment response heterogeneity to enhance customer targeting effectiveness. We recommend firms run a pilot randomized experiment and use the data to train various deep-learning models. By incorporating recurrent neural nets and deep perceptron nets, our optimal deep-learning model can capture both temporal and network effects in the purchase history, after addressing the common issues in most predictive models such as imbalanced training, data sparsity, temporality, and scalability. We then apply the learned optimal model to identify customer targets from the large amount of remaining customers with the highest predicted purchase probabilities. Our application with a large department store on a total of 2.8 million customers supports that optimal deep-learning models can identify higher-value customer targets and lead to better sales performance of marketing campaigns, compared to industry common practices of targeting by past purchase frequency or spending amount. We demonstrate that companies may achieve sub-optimal customer targeting not because they offer inferior campaign incentives, but because they leverage worse targeting rules and select low-value customer targets. The results inform managers that beyond gauging the causal impact of marketing interventions, data from field experiments can also be leveraged to identify high-value customer targets. Overall, deep-learning algorithms can be integrated with field experiment response heterogeneity to improve the effectiveness of targeted campaigns

Customer Satisfaction, Analyst Stock Recommendations, and Firm Value

Although managers are interested in the financial value of customers and researchers point out the importance of stock analysts who advise investors, no studies seem to have explored the implications of customer satisfaction for analyst stock recommendations. On the basis of a large-scale longitudinal dataset, the authors find that positive changes in customer satisfaction not only improve analyst recommendations but also lower dispersion in those recommendations for the firm. These effects are stronger when product market competition is high and financial market uncertainty is large. Also, analyst recommendations at least partially mediate the effects of changes in satisfaction on firm abnormal return, systematic risk, and idiosyncratic risk. Analyst recommendations represent a mechanism through which customer satisfaction affects firm value. Thus, if analysts pay attention to Main Street customer satisfaction, then Wall Street investors should have good reason to listen and follow. Overall, our research reveals satisfaction’s impact on analyst-based outcomes and firm value metrics and calls attention to the construct of customer satisfaction as a key intangible asset for the investor community

Retargeting Ads for Shopping Cart Recovery: Online Field Experiments

Retargeting ads (RA) aim to convert customers who previously browsed the websites or abandoned shopping-carts. We exploit several randomized field experiments to test how the effects of RA vary depending on ad-copy content and purchase-funnel stages. Results suggest that compared to hold-out without retargeting, RA in lower funnel based on shopping-cart abandonment history significantly enhances purchase responses. The effects are driven by ad content that highlights product return information rather than product reminder or shipping information. Due to lack of touch/feel/trial of online orders, such ad-copy can nudge customers to try the products by reducing shopping risks and, thus, increase purchase rates. Net revenue for RAs with product return information is 49.7% larger than conventional RAs with product information. Also, lower funnel retargeting is 2.25 times as effective as upper funnel retargeting in lifting purchase rates. These findings indicate how to design RAs to recover abandoned carts and boost sales

All that Glitters is not Gold: Understanding the Impacts of Platform Recommendation Algorithm Changes on Complementors in the Sharing Economy

Sharing platforms often leverage recommendation algorithms to reduce matching costs and improve buyer satisfaction. However, the economic impacts of different recommendation algorithms on the business operations of complementors remains unclear. This study uses natural quasi-experiments and proprietary data from a home-cooked food-sharing platform with two recommendation algorithms: word-of-mouth recommendation (WMR) and botler personalization recommendation (BPR). Results show the WMR negatively affects revenue while BPR has a positive effect. The contrast revenue effects have been attributed to capacity constraints for complementors and matching frictions for consumers. WMR encourages sellers to specialize in high-quality products but limits new product development. BPR promotes innovation to suit diverse customer tastes but may reduce quality. This reflects the exploration-exploitation trade-off: WMR exploits existing competences, while BPR explores new products to satisfy personal preferences. The authors discuss implications for how to utilize recommendation algorithms and artificial intelligence for the prosperity of sharing economy platforms

FRIENDING AND GOAL ATTAINMENT: AN EMPIRICAL STUDY IN VIRTUAL WORLD

Encouraging individual goal pursuit through social influence is a growing trend. While friendships can be both an asset but also a burden, the impact friends have on goal attainment is not well established in the literature. We explore the influence that friend quantity and quality have on individual task-oriented goal-directed behavior using a unique set of online gaming data with a sample of about 33,000 individuals. Our results indicate a nonlinear relationship that suggests the number as well as the intimacy level of friendships positively impact individual goal achievement, but too much social friending becomes detrimental to individual goal pursuit. Females benefit slightly more from friendship amount and intimacy level, but also suffer more from too many exchanges with friends. Similarly, novice individuals not only benefit more from social influence than more experienced individuals in terms of goal pursuit, but also hurt more from friending behaviors. Our follow-up surveys with actual gamers provide additional evidence that friends indeed proffer information and emotional support that can promote goal attainment. However, too much friending can hurt goal completion due to information overload and time demands. These findings have important implications for consumers and managers regarding how social others influence individual goal attainment

Striking Isotopologue-Dependent Photodissociation Dynamics of Water Molecules:The Signature of an Accidental Resonance

Investigations of the photofragmentation patterns of both light and heavy water at the state-to-state level are a prerequisite for any thorough understanding of chemical processing and isotope heterogeneity in the interstellar medium. Here we reveal dynamical features of the dissociation of water molecules following excitation to the (C) over tilde (010) state using a tunable vacuum ultraviolet source in combination with the high-resolution H(D)-atom Rydberg tagging time-of-flight technique. The action spectra for forming H(D) atoms and the OH(OD) product state distributions resulting from excitation to the (C) over tilde (010) states of H2O and D2O both show striking differences, which are attributable to the effects of an isotopologue-specific accidental resonance. Such accidental-resonance-induced state mixing may contribute to the D/H isotope heterogeneity in the solar system. The present study provides an excellent example of competitive state-to-state nonadiabatic decay pathways involving at least five electronic states

Consumer Connectivity in a Complex, Technology-Enabled, and Mobile-Oriented World with Smart Products

Today’s consumers are immersed in a vast and complex array of networks. Each network features an interconnected mesh of people and firms, and now, with the rise of the Internet of Things (IoT), also objects. Technology (particularly mobile devices) enables such connections, and facilitates many kinds of interactions in these networks - from transactions, to social information sharing, to people interfacing with connected devices (e.g., wearable technology). We introduce the POP-framework, discuss how People, Objects and the Physical world interconnect with each other and how it results in an increasing amount of connected data, and briefly summarize existing knowledge on these inter-connections. We also provide an agenda for future research focused on examining potential impact of IoT and smart products on consumer behavior and firm strategies