CEO regulatory focus and myopic marketing management

This study details the mechanisms on how CEO regulatory focus affects the salience of the gains versus losses involved in myopic marketing decision-making, and how such CEO psychological attributes interact with internal equity-based compensation, external pressure from equity analysts, and environmental turbulence to affect firms’ myopic marketing management propensities. We find that when faced with short-term earnings pressure to meet earnings expectations and when time is no longer a resource, predominantly promotion-focused are more likely to engage in myopic marketing management to benefit from the temporary stock price increase, which comes from meeting or beating earnings expectations. Conversely, predominantly prevention-focused CEOs are less prone to such short-termist actions which results in long-term value loss. For the moderating variables, we find that: (1) equity-based compensation tends to attenuate myopic marketing tendencies of promotion-focused CEOs but have no impact on prevention-focused CEOs, (2) whether equity analysts improve monitoring or aggravate short-term earnings pressure depends on the CEO’s regulatory focus, and (3) environmental turbulence does not increase the myopic marketing management tendencies of predominantly promotion-focused CEOs but rather intensifies the relunctance of prevention-focused CEOs to take short-termist actions. We further find that myopic marketing management mediates the impact of CEO regulatory focus on future firm performance. These findings have important implications for firms and boards when selecting new CEOs and structuring the compensation of existing CEOs. Firms need to simultaneously consider the fit between the CEOs’ regulatory focus, firms’ needs, the business environment, as well as CEO compensation structure.     

When push comes to shove: How coach versus student athlete misconduct affects event attendance intentions

Though onand off-the-field misconduct is common among U.S. college athletic programs, little is known regarding the ramifications that may result. Drawing on social learning theory, the current research suggests consumers intentions (e.g., likelihood of attending a game) differ depending on violator's team role. Across one qualitative and five experimental studies, we demonstrate that consumers' intentions are influenced by violator's team role, such that likelihood of attending a game is lower when a coach (vs. student athlete) misbehaves, an effect driven by evaluation of the academic institution. This effect is robust across both winning and losing records and moderated by perceived fairness of the university's actions toward the violator.     

Personalized managerial response and negative inconsistent review helpfulness: The mediating effect of perceived response helpfulness

Understanding the effect of managerial response on online review management has attracted the attention of scholars in recent years. However, the effect of personalized managerial response on the negative inconsistent review is a lack of evidence. This study investigates how personalized managerial responses influence negative review helpfulness according to the various levels of review inconsistency and the underlying mechanism behind the effect. We adopted a secondary data analysis based on the TripAdvisor dataset and used an online experiment study to investigate the impact mechanism. Findings reveal that the personalized managerial response positively influences review helpfulness, review inconsistency (negative rating with positive textual sentiment) moderates the effect of personalized managerial response on review helpfulness, and perceived response helpfulness mediates the above relationships. More specifically, personalized managerial response to negative inconsistent reviews has a pronounced effect on consumers’ perception of response helpfulness and their evaluation of review helpfulness. This study improves the current understanding of managerial responses, and provides practical guidance for hoteliers, consumers, and travel websites.     

The antecedents of digital collaboration through an enhanced digital platform for destination management: A micro-DMO perspective

While the tourism sector shifts towards digital transformation, Destination Management Organisations (DMOs) often struggle to adapt to their changing technological environment. This study explores the antecedents of digital collaboration and develops a framework for micro-DMOs to enhance effective destination management through digital technologies. An integrated sequential qualitative approach was adopted by conducting multi-phase interviews, in addition to designing and trialling a real-world trial digital platform. The research provides empirical evidence that digital collaboration is essential for micro-DMOs, necessitating them to transform their current “websites” into digital platforms which act as a hub for business stakeholders to actively be involved in. Antecedents of successful digital collaboration include mutuality, trust, control, and leadership which may be manifested differently from non-digital collaboration. Additionally, the study identifies three aspects for digital collaboration; marketing, networking and knowledge sharing that demands specific attention. Our results have theoretical, methodological, and practical implications for academia, industry and policymakers.     

游客感知视角下乡村旅游质效提升维度研究——以西双版纳傣族园为例

发展乡村旅游是实施乡村振兴战略的重要途径，对促进乡村经济发展具有重要作用。以西双版纳傣族园为案例研究地，通过SPSS和AMOS实证探究游客感知视角下乡村旅游质效提升的维度。结果表明：游客感知视角下的乡村旅游质效提升包含旅游地感知、地方认同感、主观幸福感、环境责任行为4个维度；地方认同感和旅游地感知对乡村旅游质效提升的影响力较强，其次是主观幸福感和环境责任行为。     

Is online-to-offline customer care support essential for consumer service?

Customer satisfaction is the main pillar for convenient and profitable retailing. The retail sector continuously tried to develop new strategies to improve consumer satisfaction. It is impossible to provide each service directly to the consumer by the retailer. In this direction, the retailer establishes customer care to provide the best service to consumers. Consumer care can provide promotional or prevention services, increasing the consumer's satisfaction level. This study is developed to show the impact of retailers' customer care service for an offline-to-online retailing strategy. The demand is consumer support, advertisement, and selling price dependent. For maintaining consumer service, some free home delivery policies are offered, when consumers ordered more than a certain percentage of the amount. Finally, the total profit of this O2O retailing system is calculated by the classical optimization technique. Some special cases are discussed in the numerical section to prove the impact of customer care services. Numerical results prove that customer care support enhances the profit by 48.20%, whereas investment in the advertisement and home delivery strategy helps the retail industry to earn 44.80% and 16.74% more profit, respectively. Finally, from this study, it is clear that customer care activities are essential to increase the profit of the retailing sector.     

The bright side of disorganization: When surprise generates low-price signals

The research problem of this paper attempts to understand the effects of disorganized versus organized shelves on search information. Contemporary retail studies have tended to discover how the organization of displays can evoke increased consumer attention. Thus, the purpose is to examine how disorganization cues shelf displays foster surprise through information search and the implications of that process for visualizing low-price signals. In three experiments carried out using eye-tracking devices and face reader techniques (two in the laboratory and one in the field), empirical evidence is presented that disorganization cues can generate an increase in information search generated by cognitive processing, increased emotional surprise manifested levels via schema discrepancy, and perception of low-price provoked by the attempt to reduce the risk of choice.     

The impact of reverse logistics process coordination on third party relationship quality: A moderated mediation model for multichannel retailers in the fashion industry

Multichannel retailing is a widely adopted strategy in the fashion industry. Companies in this industry find it a source of competitive advantage to invest in reverse logistics infrastructure. However, limited empirical studies investigate the enablers of the relationship quality between the retailers and their reverse logistics service providers. This study quantifies the impact of reverse logistics process coordination between retailers and logistics service providers on relationship quality. Moreover, it tests the mediating role of reverse logistics service quality and the moderating role of conflict frequency in this relationship. Data were collected through a survey using a purposive sample of 241 retail store managers from the fashion retail industry of Pakistan. For this purpose, a self-administered questionnaire was developed using a five-point Likert Scale to gauge the responses. Conditional process analysis was used to evaluate the moderated mediation model. The findings showed a significant positive impact of reverse logistics process coordination on the relationship quality with a logistics service provider, a significant positive mediation effect of reverse logistics service quality, and a significant moderation effect of conflict frequency on the indirect relationship. However, conflict frequency, contrary to the hypothesis in the study, strengthened the indirect relationship. Furthermore, the moderation effect of conflict frequency on the direct relationship was insignificant. This study will help managers better understand the best practices leading to effective management of reverse logistics processes, particularly product returns.     

The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types

While artificial intelligence products are widely used in the market, their anthropomorphic appearance design is becoming a frontier issue in product strategy and consumer behavior research. The aim of this study was to investigate the influence of anthropomorphic appearance on consumer behavior and brand evaluation under different AI product types. It was conducted in China, a new but rapidly-growing country in the field of Internet, AI technology and AI product consumption. This study conducted four situational experiments with a 2 (anthropomorphic design: anthropomorphic vs. non-anthropomorphic) × 2 (product type: hedonic vs. utilitarian) between subjects’ experimental design. Data was collected from 1172 Chinese “Digital Natives” by using a structured questionnaire. The findings revealed that for hedonic AI products, anthropomorphic appearance improves consumers' purchase intention and brand evaluation through perceived entertainment, and intelligence level significantly moderates the mediating effect of perceived entertainment; while for practical AI products, anthropomorphic appearance improves consumers' purchase intention and brand evaluation through perceived usefulness, and intelligence level does not significantly moderate the mediating effect of perceived usefulness. There is no significant moderating effect of intelligence level on perceived usefulness. The study contributes to development and validation of a more comprehensive understanding and theoretical foundation of anthropomorphism, and furthermore explores the impact of anthropomorphic appearance on consumer behavior and brand evaluation under different AI product types. This study also provides insights for companies to apply anthropomorphic strategies.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.