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Ambidextrous frontline service employees (FSEs), capable of delivering quality services and carrying out sales responsibilities too, are crucial to service firms. This study seeks to extend ambidexterity research by examining how a manager's goal orientation could influence FSEs' ambidextrous conversion. The authors draw on achievement goal theory and conceptualize a link between a manager's achievement goal orientation and employees' service–sales ambidexterity (SSA). The authors then apply conservation of resources theory to complement this high-level conceptualization, hypothesize mediating roles of important resources that can facilitate employees' SSA, and the authors test them empirically.

This study adopts a questionnaire survey design. The empirical test relies on multilevel path analyses of dyadic data from 341 FSEs and 39 managers of a major logistics service company in Taiwan.

Managers with a prominent learning goal orientation can facilitate and foster FSEs' SSA through developmental inducements and change-related self-efficacy, two important resources for their ambidextrous conversion. Managers with a strong performance-avoid goal orientation instead might hinder employees' SSA conversion, due to a negative impact on developmental inducements. Furthermore, SSA enhances FSEs' service delivery value and sales performance.

By analyzing and empirically testing the influence pathways of essential resources perceived by FSEs, which channel the effects of a manager's goal orientation to employees' SSA conversion, this study offers insights about how managers can support and foster FSEs' service–sales ambidextrous conversion.

The purpose of this paper is to explore the impact of ChatGPT on the development of corporate social responsibility (CSR).

Based on the current applications of ChatGPT in enterprises and existing challenges in CSR, the paper analyzes how the tool promotes corporate sustainable development and what potential risks and challenges are in the practical application.

This paper finds that ChatGPT can promote the development of CSR from four aspects: to support social responsibility activities, to strengthen internal control, to promote external supervision and to convey the notion of sustainability and other corporate philosophies for enterprises.

This paper provides the ground for applying ChatGPT to CSR and includes the potential risks and challenges of the practical applications that enterprises can use for reference.

ChatGPT is becoming increasingly popular and mature, but its applications and effectiveness are less mentioned in CSR. In the future, the research in this area needs to be further advanced.

This paper makes contributions to the link between ChatGPT and CSR. To the best of the authors’ knowledge, this is one of the first studies that explore the applications, impacts, challenges and opportunities of the technology in the area of social responsibility.

Although service researchers have long suggested that customer mistreatment adversely impacts service employees' outcomes, statistical integration of current empirical findings has been lacking. This meta-analysis aims to review and statistically synthesize the state of research on the relationship between customer mistreatment and service employees' affective, attitudinal and behavioral outcomes.

The authors included 221 effect sizes of 135 independent samples from 119 primary studies (N = 47,964). The authors used a meta-analytic approach to quantitatively review the relationship between customer mistreatment and service employees' affective, attitudinal and behavioral outcomes. Meta-analysis structural equation modeling was used to explore the mediation mechanism of service employees' affective outcomes on the relationships between customer mistreatment and employees' attitudinal and behavioral outcomes. Meta-regression was applied to explore the impact of contextual-level moderators (i.e. service provider type and service delivery mode) on these relationships. Furthermore, we compared the effects of customer mistreatment with the effects of other organizational-related factors on some commonly measured employee outcomes.

The results show that customer mistreatment has a significant negative impact on service employees' affective outcomes (i.e. negative emotions), attitudinal outcomes (i.e. job satisfaction, organizational commitment, work engagement and turnover intention) and behavioral outcomes (i.e. job performance, surface acting and emotional labor). Additionally, service employees' negative emotions mediate the association between customer mistreatment and employees' job satisfaction, turnover intention, surface acting and emotional labor. Furthermore, the relationships between customer mistreatment and service employees' negative emotions and job performance are influenced by a contextual-level moderator (i.e. service delivery mode).

The authors contribute to the literature by providing robust meta-analytic estimates of the effects of customer mistreatment on a variety of service employees' affective, attitudinal and behavioral outcomes, as well as the different magnitudes of the effect sizes between customer mistreatment and other job-related and personality-related factors by quantifying the true variability of the effect sizes. The authors draw on current theories underpinning customer mistreatment to test a theoretical model of the mediation mechanism of service employees' affective outcomes (i.e. service employees' negative emotions) on the relationships between customer mistreatment and employees' attitudinal and behavioral outcomes. The authors explore the effects of two contextual-level factors (i.e. service provider types and service delivery mode) related to the service delivery context that may account for the variability of effect sizes across empirical studies.

The purpose of this paper is to investigate the impacts of technological capability (TC) and technology management (TM) on the development of China’s telecommunications industry by examining two interrelated questions: Which is the order parameter for China’s telecommunications industry? and What are the relationships between TC and TM? The paper will develop models to analyze TC, TM and the development of China’s telecommunications industry quantitatively.

The paper reviews literature related to TC and TM and investigates the technical features of China’s telecommunications industry. Then, the paper makes a theoretical analysis of the relationships among them. First, it analyzes the effects of TC and TM on the industry and tries to explore the order parameter. Second, the paper analyzes the interaction between TC and TM. Then, the paper applies the Haken model to construct an analysis model. It also constructs models for measuring TC and TM. Using the data of China’s telecommunications industry from 1991 to 2010, the paper identifies the order parameter for the industry and tests the relationships between TC and TM.

The paper finds that TC plays a dominant role in the development of China’s telecommunications industry. The paper also finds that TC promotes the development of TM. However, TM fails to promote the development of TC. The paper presents two other important findings. The first one is that China’s telecommunications industry has constructed a positive feedback mechanism for continuous development of TC, which provides an impetus for industry upgrades and development. The second one is that there is no positive feedback growth mechanism for TM capability, which has increased the lag between TM and TC and has become one of the important reasons why TM does not promote TC.

Theoretical and managerial implications are put forward. Thus, TC must be improved to propel the development of China’s telecommunications industry; TM should be greatly improved and an effective growth mechanism for TM should be constructed; the synergy between TM and TC should be achieved. Research limitations are also pointed out. Thus, the results in this paper probably cannot be generalized to other industries; it does go on to discuss the synergetic mode of TM and TC that can best compel industrial development, and it does not explain what the growth mechanisms for TM and TC should be.

The paper is original, as it provides new perspectives of TM and TC for analyzing industrial development, and it provides the method and model for quantitatively analyzing TC and TM at an industry level. It discovers the order parameter and new problems of China’s telecommunications industry, which no one has identified before. The paper also offers references for analyzing other industries from the perspectives of TM and TC.

An increasing number of investors have begun using financial data to develop optimal investment portfolios; therefore, the public financial data shared in the capital market plays a critical role in credit ratings. These data enable investors to understand the credit levels of debtors from a bank perspective; this facilitates predicting the debtor default rate to efficiently evaluate investment risks. The paper aims to discuss these issues.

A credit rating model can be developed to reduce the risk of adverse selection and moral hazard caused by information asymmetry in the loan market. In this study, a random forest (RF) was used to evaluate financial variables and construct credit rating prediction models. Data-mining techniques, including an RF, decision tree, neural networks, and support vector machine, were used to search for suitable credit rating forecasting methods. The distance to default from the KMV model was then incorporated into the credit rating model as a research variable to increase predictive power of various data-mining techniques. In addition, four-level and nine-level classification were set to investigate the accuracy rates of various models.

The experimental results indicated that applying the RF in the variable feature selection process and developing a forecasting model was the most effective method of predicting credit ratings; the four-level and nine-level feature-selection settings achieved 95.5 and 87.8 percent accuracy rates, respectively, indicating that RF demonstrated outstanding feature selection and forecasting capacity.

The experimental cases were based on financial data from public companies in North America.

Practical implication of this study indicates the most effective financial variables were dividends common/ordinary, cash dividends, volatility assumption, and risk-free rate assumption.

The RF model can be used to perform feature selection and efficiently filter numerous financial variables to obtain crediting rating information instantly.

Tourism destination marketers use videos that incorporate storytelling and visual and audio components to evoke emotional arousal and memorability. This study aims to examine the increase in participants’ level of arousal and the degree of memorability after watching two different videos.

A quasi-experimental study was conducted with 45 participants who watched two destination promotional videos. One video used storytelling whereas the other used scenic images and music. The level of arousal was measured using both tonic and phasic electrodermal activity levels. The memorability of each video was measured after seven days by testing the recall accuracy.

Scenic imagery and music videos were associated with higher-than-average arousal levels, while storytelling videos generated larger-amplitude arousal peaks and a greater number of arousal-evoking events. After a week, the respondents recalled more events from the storytelling video than from the scenery and musical advertisements. This finding reveals that the treatment, storytelling and sensory stimuli in advertising moderate the impact of arousal peaks and memorability.

These results indicate that nonnarrative videos using only sceneries and music evoked a higher average level of arousal. However, memorability was associated with higher peak levels of arousal only in narrative storytelling. This is the first tourism study to report the effects of large arousal peaks on improved memorability in advertising.

The purpose of this paper is to help understand the mathematical model of inductive sensor and to improve the sensitivity of nonferrous metal particle detection.

The expression of impedance change is established, while the distribution regularities of the magnetic field inside and outside the metal particle are obtained based on the Maxwell equations in complex forms, the analytic solution of the electromagnetic field is obtained and the experiment validation is implemented.

The expression of impedance and the analytic solution of the electromagnetic field are obtained. It is shown that the inductance change is more obvious than resistance change for the iron particles, but for copper particles, resistance change is more obvious and the resistance change increases with the frequency. In this work, copper particles (size: 20 µm) are detected at 2 MHz excitation frequency, and the imaginary part of impedance changes without adding any device, which is provided with a prominent guideline for detection of nonferrous particles of size less than 100 µm.

The expression of impedance change is established, the analytic solution of the electromagnetic field is obtained and copper particles (size: 20 µm) are detected at 2 MHz excitation frequency, and the imaginary part of impedance change without adding any device, which is provided with a prominent guideline for detection of nonferrous particles of size less than 100 µm.

The study examines how process/organizational innovation and R&D spending mediate the relationship between financial performance and the resource dependence theory in Fintech, providing insights into effective innovation strategies for achieving sustainable financial performance.

Data from 191 financial firms in Taiwan was collected from annual reports using the Taiwan Economic Journal (TEJ), a financial information provider. Content analysis was used to measure innovation activities and financial performance, with process and organizational innovation defined. R&D expenditures were also collected and used in statistical analysis to explore the relationship between variables.

This study on the financial services industry shows that process innovation and R&D expenditure positively impact firm performance, while organizational innovation may have a negative short-term effect but could have long-term benefits.

Limitations of this study include vulnerability to spurious effects and the use of data from only listed financial service firms. Future research should use more short-term performance data and include unlisted firms in the financial services industry to extend the study’s coverage.

This study extends resource dependence theory to financial services and explores the effects of process and organizational innovation on firm performance. Results show that internal process management boosts performance, while external collaboration with startups enhances Fintech innovation and efficiency, with positive short-term effects. The study highlights the importance of interacting with external organizations to access resources and improve performance in financial services.

To present the detailed implementation processes of the IDEAL algorithm for two-dimensional compressible flows based on Delaunay triangular mesh, and compare the performance of the SIMPLE and IDEAL algorithms for solving compressible problems. What’s more, the implementation processes of Delaunay mesh generation and derivation of the pressure correction equation are also introduced.

Programming completely in C++.

Five compressible examples are used to test the SIMPLE and IDEAL algorithms, and the comparison with measurement data shows good agreement. The IDEAL algorithm has much better performance in both convergence rate and stability over the SIMPLE algorithm.

The detail solution procedure of implementing the IDEAL algorithm for compressible flows based on Delaunay triangular mesh is presented in this work, seemingly first in the literature.

To date, researchers examining the motivations of volunteers at festivals and events have used a range of measurement indexes, most of which appear to have common antecedents in the psychology literature. It is not clear if different events actually require different scales, or if individual scales are more generalizable than is currently recognized so that the proliferation of scales is largely an academic exercise. The current study takes a preliminary step in exploring this issue by using an existing scale developed to measure the motivations of volunteers at western sporting events to examine the motivations of volunteers at a music festival in China.

The Volunteer Motivation Scale for International Sporting Events (VMS-ISE) is administered to volunteers at the Midi Music Festival in China. The 467 respondents are divided into two subgroups. Exploratory factor analysis (EFA) is applied to the first subsample to explore the factor structure of the index. Confirmatory factor analysis (CFA) is then applied to the second subsample to test the factor structure of the resolved scale.

The results of the EFA are promising, yielding a resolved factor structure, which is very close to the hypothesized index. The resolved scale is reasonably well supported by the subsequent CFA.

The findings suggest it may be possible for researchers to use a smaller number of scales on a larger range of festivals leading to a better understanding of similarities and differences in motivations across event volunteers. The results should also be helpful to festival organizers in their continuing efforts to recruit volunteers.