A process perspective on experience co-creation: How pre-trip involvement prompts destination loyalty

Although the existing literature has suggested that tourists' experience co-creation during or after trips benefits destination loyalty, knowledge of how pre-trip behavior impacts future behavioral intention from a process perspective is limited. To address the gaps, we develop a logic chain linking past behavior, cognition, emotion, and future behavioral intention under a stage-by-stage process of tourists' experience co-creation using the service-dominant logic of value co-creation theory and the self-consistency principle of attitude theory. Empirically, utilizing PLS-SEM and mediation analysis in a survey (n = 500) and an experiment (n = 128), we found a serial mediation pathway whereby high-degree pre-trip involvement heightened satisfaction with the co-creation of experience, which increased place identity or place dependence, and which ultimately enhanced destination loyalty. This research is the first attempt to examine the impact of past behavior on future intention for tourists’ experience co-creation from a process perspective.     

Reducing employee turnover intention: a customer relationship management perspective

Research examining ways to prevent employee turnover has observed varied and often inconsistent findings, yet this remains a challenge to many firms, particularly call centers. This study therefore examines the impact of customer relationship management (CRM) dimensions on employee job satisfaction and intention to quit. A two-step methodological approach utilizing data from the Malaysian call center industry was used. The study conducted preliminary qualitative investigations (from literature and a focus group), and survey data were then used to test the conceptual model via structural equation modeling. The findings establish that a properly initiated and implemented CRM strategy will engender employee job satisfaction thereby resulting in significantly reduced intention to quit. The study identifies the key CRM dimensions that can be useful for call center managers in enhancing employee job satisfaction and averting employee turnover intentions. Implications for marketing theory and practice as well as areas for future research are discussed.     

Customer base analysis with recurrent neural networks

One of the primary goals that researchers look to achieve through customer base analysis is to leverage historical records of individual customer transactions and related context factors to forecast future behavior, and to link these forecasts with actionable characteristics of individuals, managerially significant customer sub-groups, and entire cohorts. This paper presents a new approach that helps firms leverage the automatic feature extraction capabilities of a specific type of deep learning models when applied to customer transaction histories in non-contractual business settings (i.e., when the time at which a customer becomes inactive is unobserved by the firm). We show how the proposed deep learning model improves on established models both in terms of individual-level accuracy and overall cohort-level bias. It also helps managers in capturing seasonal trends and other forms of purchase dynamics that are important to detect in a timely manner for the purpose of proactive customer-base management. We demonstrate the model performance in eight empirical real-life settings which vary broadly in transaction frequency, purchase (ir)regularity, customer attrition, availability of contextual information, seasonal variance, and cohort size. We showcase the flexibility of the approach and how the model further benefits from taking into account static (e.g., socio-economic variables, demographics) and dynamic context factors (e.g., weather, holiday seasons, marketing appeals). We make an open-source reference implementation of the newly developed method available at https://github.com/valendin/rfm2lstm.     

Exploring the impact of different factors on brand equity and intention to take up online courses from e-Learning platforms

Understanding service branding from the customer perspective is helpful for various organizations to attain a competitive edge and build a strong customer base. This research work aims to evaluate the various dimensions and constructs that affect brand equity and user's willingness to undertake courses from various e-Learning providers. Data for the quantitative study was obtained using a questionnaire based survey. Analysis of the 378 responses reveals that consumer experience is a strong predictor of both brand meaning and customer satisfaction. Although brand meaning positively impacts brand equity, brand awareness demonstrates a negative effect on brand equity. However, brand equity and customer satisfaction have a significant and positive impact on intention to undertake courses from online learning platforms. The study concludes with discussion on different implications, shortcomings, and directions for future research.     

Impact of actual service provider failure on the satisfaction with aggregator

The purpose of the paper is to investigate the impact of the failure caused by the actual service provider on consumer satisfaction with the aggregator firm, where the aggregator firm works as a bridge between the consumers and the actual service providers using technology, resources, information etc., thus reducing the information search cost, process-related costs, and quality risks for the consumers. We have applied retrospective experience sampling to collect data and used structural equation modeling to validate the conceptual model of the study. We found that people consider the actual service provider’s failure as the aggregator firm’s responsibility when the attributional factors (control and stability) are strong. Moreover, when consumers perceive that the aggregator firm is responsible, they will ask for less punishment of the actual service provider and more monetary compensation. Additionally, severity of the failure increases the consumer’s recovery expectations. However, the sense of power of the consumers leads to increasing expectations of punishment of actual service provider and decreasing expectations of monetary compensation. Lastly, the actual monetary compensation or actual punishment of the service provider leads to higher level of satisfaction for the consumers. We discussed the implications of the results in the light of relationship management and service recovery design of aggregator firms.     

Tipping policy effects on customer satisfaction: An informative failure to replicate

Analysis of online reviews indicates that Royal Caribbean’s abandonment of tipping on March 1, 2013 had no reliable effect on its customers’ ratings of either the overall cruise experience or the cruise service/staff. This finding stands in opposition to previous studies which reported that customer satisfaction and service ratings fell after organizations abandoned voluntary tipping policies in contexts where tipping is normative. Since tipping is no longer common in the cruise industry, the failure to replicate suggests that earlier effects were probably caused by people’s subjective preference for the tipping policies they were used to in those contexts rather than by tipping’s actual effects on service delivery.     

Virtual reality through the customer journey: Framework and propositions

While virtual reality's importance is increasingly recognized in marketing, its role in the customer journey remains nebulous. We define virtual reality through the customer journey (VRCJ) as firms' use of computer-mediated interactive environments capable of offering sensory feedback to engage consumers, strengthen consumer/brand relationships, and drive desired consumer behaviors at any stage of their journey. To better understand VRCJ, we classify VR archetypes, formats, and content features, followed by the development of a conceptual framework and an associated set of propositions of VRCJ. We conclude by discussing important theoretical and practical implications that arise from our analyses.     

An efficient airline seat reallocation algorithm considering customer dissatisfaction

Nowadays, many airlines tend to provide an advance seat selection service to maximize customer satisfaction to secure market shares in the competitive air transport market. However, the preassigned seat configuration of certain flight legs can be changed often due to aircraft changes for certain reasons, such as maintenance and reservation rates. Then certain customer's seat features, such as row location, position of aisle/window, relative location, and position with the same group member can be different before/after the change, and it can lead to huge customer dissatisfaction. Therefore, an efficient airline seat reallocation algorithm to minimize customer dissatisfaction is introduced in this study, which is based on mathematical model-based optimization with several types of objective functions that reflect customer dissatisfaction levels. A numerical example is performed to validate the developed airline seat reallocation algorithm, and an additional analysis is also executed to derive important insight.     

Note: A new approach to the modeling of spatially dependent and heterogeneous geographical regions

We propose a new spatial modeling approach to calibrate the potential impact of spatial dependency and heterogeneity on the underlying drivers of customer service and/or satisfaction measurement. The newly proposed procedure derives regionally varying coefficients, provides more flexible fitting, improves calibration fit and predictive validation, and can potentially result in augmented managerial implications compared to existing procedures by utilizing a hierarchical Bayes framework with geographical boundary effects. Using synthetic datasets, we illustrate how the proposed model outperforms four relevant benchmark models including ordinary linear regression, a Spatially Dependent Segmentation model (Govind, Rabikar, and Mittal 2018), classic Geographically Weighted Regression, and Bayesian Geographically Weighted Regression. The improved performance is most prominent when there exist significant differences between geographic boundaries and/or irregular patterns of observation locations. In our automobile customer satisfaction application study, the proposed approach also demonstrates favorable performance compared to these benchmark models. We find a dramatically heterogeneous pattern regarding two covariates in the Mountain U.S. geographic division: dealership service is more important in urban areas (e.g., Phoenix, Salt Lake City and Denver) than in rural areas, but vice-versa concerning vehicle quality.     

Beyond good and bad: Challenging the suggested role of emotions in customer experience (CX) research

Our paper challenges several notions regarding emotion's role and their influence on the customer experience (hereafter CX). Based on our analysis, we develop the following five propositions to advance our understanding of emotions' role in customer experience research. First, we argue that positive and negative emotions can coexist during the consumption experience. Second, positive emotions do not automatically lead to positive consumption outcomes, and negative emotions may not necessarily generate negative results. Third, positive or negative emotions toward a company employee might not automatically transfer to the company as a whole. Fourth, customers are not apathetic victims of their emotions with no capability to control their emotional experiences. Our last proposition is that consumption emotions are not a purely intrapersonal phenomenon, but that the social context matters and influences the consumption experience. We propose a related future research agenda highlighting opportunities for scholars and managers alike.