Post-script—Retail forecasting: Research and practice

This note updates the 2019 review article “Retail forecasting: Research and practice” in the context of the COVID-19 pandemic and the substantial new research on machine-learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.     

Does justice affect brand advocacy? Online brand advocacy behaviors as a response to hotel customers’ justice perceptions

This study focuses on customers' information-sharing behavior in the context of online brand advocacy behavior regarding hotel brands. We aim to explain hotel customers' online brand advocacy behavior through three-sided justice evaluations (i.e., justice for employees, justice for the self, and global belief in a just world), and their hotel satisfaction. Hypotheses are tested by using survey data acquired from 688 individuals on Amazon's Mechanical Turk (MTurk) through partial least squares structural equation modeling (PLS-SEM). The findings show that customers' perceptions of justice for the self positively affect their hotel satisfaction, perceptions of justice for employees and the global belief in a just world positively affect online brand advocacy behavior, and hotel satisfaction also positively affects online brand advocacy behavior. We expand current research efforts on online brand advocacy research and provide theoretical and managerial implications for the development of marketing and management research and practice.     

Performance effects of innovation in two-sided markets: The paradigmatic case of OTAs

Online Travel Agents (OTAs) play an important intermediary role in the two-sided travel distribution market. A critical factor that enhances a firm's competitive advantage is innovation. Yet, the analysis of innovation in the OTA context is scarce. The main objective of this article is to fill this gap and examine the effect of OTA innovations on firm performance. We analyze the effect of two-sided market specific innovations (same-side and cross-side) on performance and contribute to the literature by expanding the theoretical understanding of innovations. We find that producer-to-consumer innovations have a greater effect on OTA performance than producer-to-producer and consumer-to-consumer innovations. A fundamental managerial implication is that exchange management is an area to be enhanced when innovating in travel market distribution.     

The new wave of AI-powered luxury brands online shopping experience: The role of digital multisensory cues and customers’ engagement

Academic literature retains a dearth of empirical evidence of the cutting-edge aspect of artificial intelligence (AI)-powered digital assistance and digital multisensory cues, despite the prospect of these factors on real-life customers' luxury brand online shopping experience. Thus, the aim of this study is to examine the significant pathway and effects of AI-powered digital assistance toward customers’ luxury brand online shopping experience. Drawing on S–O-R (Stimulus, organism, and response) and TRAM (Technology Readiness and Acceptance Model) paradigm, a multi-method research design was deployed to investigate constructs. Firstly, semi-structured interviews were utilized to explore customers' online behavior under the luxury brands and information technology aspect. Secondly, survey data were collected and analyzed by using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The PLS-based analysis of quantitative data confirmed the exploratory insights of qualitative findings, establishing the connections of AI-powered digital assistance, customer engagement, and customers' luxury brand online shopping experience. Research findings also suggest that customer engagement plays a mediation role in the relationship between AI-powered digital assistance and customers' luxury brand online shopping experience. Besides, digital multisensory cues moderate the relationship between AI-powered digital assistance and customer engagement. Further, fsQCA complements the findings of PLS-SEM that reveal the significant combination of factors that lead to the perceptions of customers' luxury brand online shopping experience.     

Air-travelers' concerns emerging from online comments during the COVID-19 outbreak

This research aims to assess air travelers' concerns affected by the Coronavirus pandemic, expressed in the comments they wrote online. A sample of 639 comments written on the Italian National Consumer Union website and related to the airline industry was assessed through an automated sentiment analysis in this study. The achieved results showed that travelers' concerns were directed mainly towards compensations, cancellations, and COVID-19 and at the same time, they had mixed and unpredictable feelings. This element suggests that consumers may have understood that airline companies are facing unsustainable cash-flow and revenue situations. Moreover, all our hypotheses, grounded on existing literature, were refuted. Accordingly, we argue that the actual context prevents assessments based on previous assumptions, and studies related to the impact of COVID-19 need to be conducted anew.     

广东省县域农业生产效率分析——基于2015-2019年揭阳市5县域DEA模型为例

农业生产效率提升是实现农业现代化的重要手段。本文以《揭阳市统计年鉴(2015-2019)》的农业相关数据,从县域的视角研究揭阳市农业生产效率,运用DEA模型对揭阳市5个县域的3个投入指标和1个产出指标进行实证分析。结果显示,2015-2019年揭阳市农业生产效率均属于DEA无效,总体效率提升并不明显,且各县域呈现发展不平衡的状态,仅惠来县位于生产前沿面上,普宁市的农业生产效率最低,规模报酬属于递减态势。因此,恰逢“十四五”谋划之时,对揭阳市各县域农业生产效率做出分析研究,为现阶段提升揭阳市农业生产效率提出有参考价值的建议,为广东省县域农业生产效率分析提供范本。     

Customer purchase forecasting for online tourism: A data-driven method with multiplex behavior data

Online tourism has received increasing attention from scholars and practitioners due to its growing contribution to the economy. While related issues have been studied, research on forecasting customer purchases and the influence of forecasting variables, online tourism is still in its infancy. Therefore, this paper aims to develop a data-driven method to achieve two objectives: (1) provide an accurate purchase forecasting model for online tourism and (2) analyze the influence of behavior variables as predictors of online tourism purchases. Based on the real-world multiplex behavior data, the proposed method can predict online tourism purchases accurately by machine learning algorithms. As for the practical implications, the influence of behavior variables is ranked according to the predictive marginal value, and how these important variables affect the final purchase is discussed with the help of partial dependence plots. This research contributes to the purchase forecasting literature and has significant practical implications.     

Exploring the motivations to use online meal delivery platforms: Before and during quarantine

Online meal delivery platforms (OMDPs), like UberEats, have received more attention since quarantine was established to prevent the spread of COVID-19. The research into antecedents of satisfaction and continuous usage intention for OMDPs, in general, is under researched. The current study compared the antecedents of satisfaction before quarantine to the antecedents during quarantine through the lens of Expectation-Confirmation Theory. Through multiple regression analyses, the researchers found that before quarantine, sharing economy ethos, price-value, food quality, ease of use and confirmation of beliefs had a significant impact on satisfaction while during quarantine, food quality, service speed, ease of use, and confirmation of beliefs were significant. Practical and theoretical implications are discussed.     

Unraveling the interplay of review depth,review breadth,and review language style on review usefulness and review adoption

To examine whether long reviews are uniformly more useful to readers, this study utilizes the experimental design approach to investigate the interplay of review depth, review breadth, and review language style on readers’ perceived usefulness and adoption intention of online hotel reviews. Drawing on the results from two experiments, review depth is proven to exert a positive impact on readers’ review usefulness and adoption intention. The moderating role of review breadth is verified, and the positive effect of review depth on review usefulness is attenuated (accentuated) when review breadth is high (low). The moderating role of review language style is also confirmed, and the result shows longer reviews written in literal language are considered more useful to readers than those written in figurative language. Being one of the first studies examining the interplay of review content and review style on review usefulness, this study provides important theoretical and practical contributions.