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The purpose of this study to improve the efficiency of customer acquisition and retention through the design of salary information disclosure mechanism.

This study develops a stylized game-theoretic model of delegating customer acquisition and retention, focusing on how firms choose delegation and wage information disclosure strategy.

The results confirm the necessity for enterprises to disclose salary information. When sales agents are risk neutral, firms should choose multi-agent (MA) delegation and disclose their wages. However, when agents are risk averse, firms may disclose the wages of acquisition agents or both agents in MA delegation, depending on the uncertainty of the retention market.

This paper contributes to the literature on delegation of customer acquisition and retention and demonstrates that salary disclosure can be used as a supplement to the incentive mechanism.

Artificial intelligence (AI) is increasingly applied by businesses to optimize their processes and decision-making, develop effective and efficient strategies, and positively influence customer behaviors. Businesses use AI to generate behaviors such as customer retention (CR). The existing literature on “AI and CR” is vastly scattered. The paper aims to review the present research on AI in CR systematically and suggest future research directions to further develop the field.

The Scopus database is used to collect the data for systematic review and bibliometric analysis using the VOSviewer tool. The paper performs the following analysis: (1) year-wise publications and citations, (2) co-authorship analysis of authors, countries, and affiliations, (3) citation analysis of articles and journals, (4) co-occurrence visualization of binding terms, and (5) bibliographic coupling of articles.

Five research themes are identified, namely, (1) AI and customer churn prediction in CR, (2) AI and customer service experience in CR, (3) AI and customer sentiment analysis in CR, (4) AI and customer (big data) analytics in CR, and (5) AI privacy and ethical concerns in CR. Based on the research themes, fifteen future research objectives and a future research framework are suggested.

The paper has important implications for researchers and managers as it reveals vital insights into the latest trends and paths in AI-CR research and practices. It focuses on privacy and ethical issues of AI; hence, it will help the government develop policies for sustainable AI adoption for CR.

To the author's best knowledge, this paper is the first attempt to comprehensively review the existing research on “AI and CR” using bibliometric analysis.

This study explores the mediating effects of relationship marketing orientation (RMO) and service quality orientation (SQO) on market orientation, selling orientation, and policyholder retention in non-life insurance services. Additionally, it offers important recommendations for non-life insurers in Taiwan for policy development and improving policyholder retention.

Data were collected from a sample of policyholders belonging to the top five non-life insurance companies in Taiwan. The data were then analyzed with structural equation modeling.

RMO and SQO mediate the effects of the salesperson’s market orientation on policyholder retention. Thus, RMO and SQO are key factors influencing policyholder retention. Consequently, high levels of market orientation should be maintained to increase RMO and SQO, strengthening the retention rate of non-life insurance policyholders.

The main limitation of this study is its cross-sectional nature. In the future, researchers should collect data from other countries and service industries (e.g. banks, securities, and other financial institutions), expand to different insurance contexts (e.g. life insurance), and conduct longitudinal studies or experimental research.

The results of this study can act as a guide for providers of non-life insurance services. Based on the research results, we recommend decision-makers pay increased attention to increasing policyholder retention rates by strengthening their firm’s RMO and SQO.

Few studies have investigated the relationships among market orientation, selling orientation, RMO, SQO, and policyholder retention in non-life insurance services within Asian contexts in general and specifically in Taiwan. Thus, this study’s theoretical contributions, managerial implications (especially for decision-makers), and the proposed future research directions represent timely and valuable additions to the literature.

This study aims to predict customer churn in resort hotels by calculating the churn probability of repeat customers for future stays in the same hotel brand.

Based on the recency, frequency, monetary (RFM) paradigm, random forest and logistic regression supervised machine learning algorithms were used to predict churn behavior. The model with superior performance was used to detect potential churners and generate a priority matrix.

The random forest algorithm showed a higher prediction performance with an 80% accuracy rate. The most important variables were RFM-based, followed by hotel sector-specific variables such as market, season, accompaniers and booker. Some managerial strategies were proposed to retain future churners, clustered as “hesitant,” “economy,” “alternative seeker,” and “opportunity chaser” customer groups.

This study contributes to the theoretical understanding of customer behavior in the hospitality industry and provides valuable insight for hotel practitioners by demonstrating the methods that facilitate the identification of potential churners and their characteristics.

Most customer retention studies in hospitality either concentrate on the antecedents of retention or customers’ revisit intentions using traditional methods. Taking a unique place within the literature, this study conducts churn prediction analysis for repeat hotel customers by opening a new area for inquiry in hospitality studies.

This study analyzes how tech firms can implement the modern wave of subscription-based business model (SBBM), including value proposition, value creation, value capture and performance. In fact, these elements push tech firms to move from traditional to SBBMs.

To achieve the objectives of this study, we initially construct a theoretical framework for applying SBBM. Subsequently, we employ qualitative research to examine the current implementation of the subscription-based economy within tech firms.

A successful SBBM necessitates capturing value through sustainable revenue transactions and revising aspects of the value proposition, creation and capture. Continuous improvement through business value analysis is imperative. Additionally, an agile operations system is vital to address revenue complexities, enable data collection and enhance value proposition, service innovation, churn rate and customer retention, which are essential for SBBM maintenance.

This study delves into how the subscription-based economy is reshaping the business models of tech firms. Beyond exploring the theoretical foundation of this transformative path, this study offers actionable insights on enhancing the value proposition, creation, capture and business value within subscription-based economy frameworks.

This research paper aims to examine two hybrid models of logistics service quality (LSQ) and its influence on satisfaction, loyalty and future purchase intention in a business-to-consumer (B2C) e-commerce context. This study extends the literature for LSQ by incorporating the second-order assurance quality construct, which comprises personnel contact quality, order discrepancy handling and order returns, into one of the hybrid models.

A survey-based approach is used to collect data. Participant responses to questions concerning multiple LSQ dimensions and behavioral perceptions from their most recent online shopping experience are measured using structural equation modeling.

Findings highlight the importance of including a second-order construct assurance quality as a more explanatory model. Results illustrate that online ordering procedures and assurance quality impact customer satisfaction more than other prominent LSQ dimensions. Furthermore, the findings revealed a customer loyalty is a partial mediator between customer satisfaction and future purchase intention. This underscores the significance of improved logistics services as a competitive edge for e-commerce retailers.

Implications are limited to the e-commerce B2C domain.

The findings of this study underscore critical LSQ dimensions that garner greater satisfaction and retention in the online shopping experience. The results indicate that the effective and efficient handling of the initial order and any order problem significantly influences customer satisfaction and reaps the long-term benefits of customer retention.

The authors present and empirically test a hybrid model of LSQ in a B2C e-commerce domain that captures many of the important elements of the customer experience as espoused in the literature.

The new customer churn prediction (CCP) utilizing deep learning is developed in this work. Initially, the data are collected from the WSDM-KKBox’s churn prediction challenge dataset. Here, the time-varying data and the static data are aggregated, and then the statistic features and deep features with the aid of statistical measures and “Visual Geometry Group 16 (VGG16)”, accordingly, and the features are considered as feature 1 and feature 2. Further, both features are forwarded to the weighted feature fusion phase, where the modified exploration of driving training-based optimization (ME-DTBO) is used for attaining the fused features. It is then given to the optimized and ensemble-based dilated deep learning (OEDDL) model, which is “Temporal Context Networks (DTCN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM)”, where the optimization is performed with the aid of ME-DTBO model. Finally, the predicted outcomes are attained and assimilated over other classical models.

The features are forwarded to the weighted feature fusion phase, where the ME-DTBO is used for attaining the fused features. It is then given to the OEDDL model, which is “DTCN, RNN, and LSTM”, where the optimization is performed with the aid of the ME-DTBO model.

The accuracy of the implemented CCP system was raised by 54.5% of RNN, 56.3% of deep neural network (DNN), 58.1% of LSTM and 60% of RNN + DTCN + LSTM correspondingly when the learning percentage is 55.

The proposed CCP framework using the proposed ME-DTBO and OEDDL is accurate and enhances the prediction performance.

The existing literature has examined the determinants of post-purchase behavioral intentions. However, less attention has been devoted to the factors that contribute to perceived usefulness of fast-food restaurants attributes. The current study considers the servicescapes and social servicescapes of restaurants as well as their relationship with customers' perceived usefulness, with the moderating role of customer experience. It also explores how perceived usefulness influences choice process satisfaction and subsequent behavioral responses, including revisit intention and negative word-of-mouth.

Data from 485 fast-food restaurant consumers in Pakistan were collected using purposive sampling. The data were analyzed using both structural equation modeling (SEM) through AMOS 24.0 and the PROCESS macro in IBM SPSS 27.0.

The research revealed that perceived usefulness in fast-food restaurant industry is positively influenced by servicescapes and social servicescapes. Similarly, choice process satisfaction is primarily caused by perceived usefulness and affects behavioral responses. It also found that choice process satisfaction is positively associated with revisit intentions and negative word-of-mouth. Customer experience significantly moderates the relationship between ambient condition, facility aesthetic, layout, perceived similarity and perceived usefulness. However, customer experience insignificant moderates the relationships of physical appearance and suitable behavior with perceived usefulness.

The findings provide insightful information for both academic and managerial fields, contributing to the literature on consumer psychology, consumer behavior, servicescapes and the stimulus-organism-response theory. The study also assists restauranteurs in the fast-food restaurant industry in overcoming the challenges posed by a highly competitive environment and developing strategies based on consumer perceptions.

This study, conducted in Pakistan, took a pioneer step in testing and confirming a novel perceived usefulness model that incorporates not only servicescapes but also social servicescapes in consumer behavior. It enhances the knowledge of consumer visit intentions by quantifying the significance of perceived usefulness developed by different servicescapes.

E-commerce companies often manipulate customer decisions through dark patterns to meet their interests. Therefore, this study aims to identify, model and rank the enablers behind dark patterns usage in e-commerce companies.

Dark pattern enablers were identified from existing literature and validated by industry experts. Total interpretive structural modeling (TISM) was used to model the enablers. In addition, “matriced impacts croisés multiplication appliquée á un classement” (MICMAC) analysis categorized and ranked the enablers into four groups.

Partial human command over cognitive biases, fighting market competition and partial human command over emotional triggers were ranked as the most influential enablers of dark patterns in e-commerce companies. At the same time, meeting long-term economic goals was identified as the most challenging enabler of dark patterns, which has the lowest dependency and impact over the other enablers.

TISM results are reliant on the opinion of industry experts. Therefore, alternative statistical approaches could be used for validation.

The insights of this study could be used by business managers to eliminate dark patterns from their platforms and meet the motivations of the enablers of dark patterns with alternate strategies. Furthermore, this research would aid legal agencies and online communities in developing methods to combat dark patterns.

Although a few studies have developed taxonomies and classified dark patterns, to the best of the authors’ knowledge, no study has identified the enablers behind the use of dark patterns by e-commerce organizations. The study further models the enablers and explains the mutual relationships.

This study aims to investigate the impact of several factors on customer satisfaction and intention of reusing ride-hailing services that is a new type of passenger urban transport service.

This research applied the Partial Least Squares Structural Equation Modeling analysis method to examine the measurement scale and to analyze the primary data collected from 388 passengers in Vietnam.

This study demonstrates that three dimensions of perceived value, namely, functional value, hedonic value and economic value, positively influence customer satisfaction. The other dimension of perceived value, which is social value, has an ambiguous effect on satisfaction. In addition, personal innovativeness promotes all dimensions of perceived value. In particular, this study highlights that customer satisfaction and corporate image positively impact reuse intention, and corporate image moderates the relationship between customer satisfaction and reuse intention.

This study enriches knowledge about customer behavior using services based on the sharing economy business model. In particular, theoretical and practical implications are provided for researchers and enterprises to find suitable strategies for business.