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The purpose of this paper is to identify the factors that explain the acceptance of self-service ordering systems (SOSs) for restaurants and to explore the effects of “self-service system service quality” (SSQ) and “interpersonal service quality” (ISQ) on the acceptance factors extended from the Unified Theory of Acceptance and Use of Technology model.

This study targets customers who have recently used SOSs to order foods in middle-class restaurants. In total, 402 valid survey samples were obtained. Partial least squares (PLS) analysis was used to examine the factors of user acceptance of using SOSs.

The results of the PLS-SEM analysis indicate that SSQ has a significant effect on accuracy expectancy, speed expectancy and effort expectancy; ISQ has a significant effect on accuracy expectancy, speed expectancy, effort expectancy and facilitating conditions; and accuracy expectancy, speed expectancy, effort expectancy, social influence, facilitating conditions and budget expectancy significantly influence user acceptance of SOSs. Furthermore, user experiences moderate the effect of speed expectancy and effort expectancy on user acceptance.

This study introduces three technology acceptance factors (accuracy, speed and budget) for researchers to consider in the future. It also extends the knowledge about the human service factor when middle-class restaurants adopt self-service technologies (SSTs). Recommendations are provided for system developers to improve the system quality of SSTs and service staff to rethink their roles in adopting SSTs in the service industry.

本文研究目的有:（1）确认解释客人接受餐厅自助订餐系统（SOSs）的决定因素（2）探索自助系统服务质量（SSQ）和人机服务质量（ISQ）对于UTAUT模型科技接受因素的作用。

本论文的目标受众为近期使用过SOSs在中等餐厅点餐过的客人。样本为402份有效问卷数据。本论文使用PLS分析检测用户接受SOSs的各项因素。

PLS-SEM分析结果表明, SSQ对准确预期、速度预期、努力预期, 有显著作用; ISQ对于准确预期、速度预期、努力预期、以及辅助条件, 有显著作用; 准确预期、速度预期、努力预期、社会影响、辅助条件、以及预算预期对于SOSs用户接受有显著作用。此外, 用户体验调节速度预期和努力预期对于用户接受的作用。

本论文新增了三种科技接受因子（准确度、速度、和预算）, 为未来的科研创造土壤。本论文还扩展了我们对于人员服务因子在中等餐厅采用SSTs的认知。本论文建议系统开发者应该提高SST系统质量, 以及建议服务人员重新审视在服务产业采用SST中自己的位置。

Employs the integral transform method in the hybrid numerical‐analytical solution of fully developed laminar flow within a class of irregularly shaped ducts, with respect to the co‐ordinate system chosen to represent the geometry under consideration. A quite general formulation of a two‐dimensional steady‐state diffusion problem is initially considered, and a formal solution is provided. The original partial differential equation is analytically transformed into an infinite system of ordinary differential equations for the transformed velocity field in the flow direction. On truncation to a sufficiently large finite order, adaptively chosen to meet prescribed accuracy requirements, well‐established numerical schemes for boundary value problems are utilized, readily available in scientific subroutines libraries. Illustrates convergence rates for a few typical duct geometries and critically examines previously reported numerical solutions.

Points out that customer satisfaction and retention are critical for retail banks, and investigates the major determinants of customer satisfaction and future intentions in the retail bank sector. Identifies the determinants which include service quality dimensions (e.g. getting it right the first time), service features (e.g. competitive interest rates), service problems, service recovery and products used. Finds, in particular, that service problems and the bank’s service recovery ability have a major impact on customer satisfaction and intentions to switch.

Many studies, motivated by concerns for activity-based costing (ABC) implementation efforts being less than successful, have suggested that the lack of success in this area stems more from behavioral, as opposed to technical, factors. This concern for the behavioral aspects of systems implementation has also emerged from much of the more general information systems research examining determinants of implementation success. Accordingly, the purpose of this study is to determine if a popular process theory of motivation, expectancy theory, would be useful in explaining the motivation of managers to incorporate ABC information into their job. Data obtained from two experiments employing a judgment modeling methodology support the relevance of both the valence and force models of expectancy theory in this context. Further, the judgments provided by the subject managers suggest they perceive improved product cost accuracy as the most beneficial outcome of ABC use, followed by an equivalent appreciation for both an enhanced ability to communicate the underlying economics of the firm and to identify non-value-added activities. Additionally, subject managers exhibited a greater concern for the possibility that obtaining the data to maintain the ABC system would be difficult and costly than they did for concerns that the ABC information would increase the level of complexity of the information that they use.

This study aims to explore both the drivers (performance expectancy and perceived usefulness of ChatGPT) and the barrier (effort expectancy) that Indonesian youth encounter when adopting generative AI technology, such as ChatGPT, as they pursue digital entrepreneurship.

This study utilizes Hayes' Process Model to evaluate the proposed hypotheses through survey data collected from 518 Indonesian youth.

This study's findings highlight a paradoxical relationship that emerges when effort expectancy intersects with performance expectancy and perceived usefulness of ChatGPT. Specifically, we discovered that when young individuals perceive the adoption of generative AI technology as requiring significant effort, their motivation to engage in digital entrepreneurship is significantly enhanced if they also view the tool as highly useful and beneficial to their future business endeavors.

The findings provide valuable insights for educators and policymakers focused on advancing digital entrepreneurship in developing nations through the integration of generative AI technology.

Our study enriches an underexplored niche within the field of entrepreneurship by examining the intersection of Indonesian youth, generative AI technology and digital entrepreneurship. By incorporating the Expectancy-Value Theory, it brings a fresh perspective to the study of paradoxical relationships in contemporary research in this domain.

A great deal of work has been done by industrial and organisational psychologists to express the utility of validated employee selection systems. Much of this work has focused on methods for estimating economic utility, to show how many dollars can be gained or saved by implementing a new selection procedure. These methods can be quite useful, yet it is often difficult for the sponsors of selection systems to appreciate the credibility of the resulting dollar value estimates. Many users of selection systems still need to see utility analyses that demonstrate the selection system's value in more tangible behavioural terms that can be anchored in familiar performance metrics or standards of the organisation. This article presents all the information needed to compute theoretical expectancies for improvement of job performance under three well‐known models, eliminating the need to rely on incomplete expectancy tables. The methods described produce highly accurate results for all possible input values, and can be implemented easily using a variety of widely available software tools.

Firms have already begun integrating artificial intelligence (AI) as a replacement for conventional performance management systems owing to its technological superiority. This transition has sparked a growing interest in determining how employees perceive and respond to performance feedback provided by AI as opposed to human supervisors.

A 2 x 2 between-subject experimental design was employed that was manipulated into four experimental conditions: AI algorithms, AI data, highly experienced human supervisors and low-experience human supervisor conditions. A one-way ANOVA and Welch t-test were used to analyze data.

Our findings revealed that with a predefined fixed formula employed for performance feedback, employees exhibited higher levels of trust in AI algorithms, had greater performance expectations and showed stronger intentions to seek performance feedback from AI algorithms than highly experienced human supervisors. Conversely, when performance feedback was provided by human supervisors, even those with less experience, in a discretionary manner, employees' perceptions were higher compared to similar feedback provided by AI data. Moreover, additional analysis findings indicated that combined AI-human performance feedback led to higher levels of employees' perceptions compared to performance feedback solely by AI or humans.

The findings of our study advocate the incorporation of AI in performance management systems and the implementation of AI-human combined feedback approaches as a potential strategy to alleviate the negative perception of employees, thereby increasing firms' return on AI investment.

Our study represents one of the initial endeavors exploring the integration of AI in performance management systems and AI-human collaboration in providing performance feedback to employees.

The purpose of this paper is to investigate the factors influencing Palestinian consumer attitudes toward artificial intelligence (AI)-powered online banking, focusing on performance expectancy, effort expectancy, social influence and facilitating conditions while considering the moderating role of trust in financial institutions.

To test the hypotheses, an empirical study with a questionnaire was carried out. The study was completed by 362 Palestinian customers who use online banking services.

The findings of this paper show that performance expectancy, effort expectancy, social influence and facilitating conditions significantly influence consumer attitudes toward AI-powered online banking. Furthermore, trust in financial institutions as a moderating variable strengthens the impact of performance expectancy, effort expectancy, social influence and facilitating conditions on consumer attitudes toward AI-powered online banking. Therefore, more studies should focus on certain fields and cultural contexts to get a more thorough grasp of the variables influencing adoption and acceptability.

The study's findings may be specific to the Palestinian context, limiting generalizability. The reliance on self-reported data and a cross-sectional design may constrain the establishment of causal relationships and the exploration of dynamic attitudes over time. In addition, external factors and technological advancements not captured in the study could influence Palestinian consumer attitudes toward AI-powered online banking.

Financial institutions can leverage the insights from this research to tailor their strategies for promoting AI-powered online banking, emphasizing factors like perceived security and ease of use. Efforts to build and maintain trust in financial institutions are crucial for fostering positive consumer attitudes toward AI technologies. Policymakers can use these findings to inform regulations and initiatives that support the responsible adoption of AI in the financial sector, ensuring a more widespread and effective implementation of these technologies.

This research delves into Palestinian consumer attitudes toward AI-powered online banking, focusing on trust in financial institutions. It aims to enrich literature by exploring this under-explored area with meticulous examination, robust methodology and insightful analysis. The study embarks on a novel journey into uncharted terrain, seeking to unearth unique insights that enrich the existing literature landscape. Its findings offer valuable insights for academia and practitioners, enhancing understanding of AI adoption in Palestine and guiding strategic decisions for financial institutions operating in the region.

Large supermarket chains are adopting customer-service robots to improve service delivery in physical stores. Successful deployment of these robots depends on shoppers' willingness to interact with them, requiring an understanding of influencing factors. This study, grounded in the Cognitive-Affective-Normative (CAN) theory, seeks to systematically explore the factors influencing Gen Z shoppers' willingness to interact with customer-service robots.

A hybrid approach combining Structural Equation Modeling (SEM) and Necessary Condition Analysis (NCA) was employed to analyze survey data collected from 945 Gen Zs in the Czech Republic.

The results from SEM highlight significant cognitive, normative, and affective factors that influence the intention of Gen Z shoppers to interact with a customer-service robot. Specifically, cognitive factors such as effort and performance expectancy, along with normative factors like subjective norms, emerged as critical determinants. Furthermore, affective factors such as technology anxiety and positive emotions significantly influence users' readiness to use customer-service robots for service requests. The study also underscores that positive emotions, effort expectancy, performance expectancy, and subjective norms are vital prerequisites for interacting with customer-service robots.

The originality of this work lies in its two significant contributions to the burgeoning field of SRs in retail literature. First, it extends the CAN theory to the context of SRs among Gen Z shoppers in Czechia, thereby enriching the existing literature on SRs in retail. Second, by employing a hybrid analytical approach, our research offers both empirical and methodological advancements, providing rigorous insights crucial for enhancing the understanding of the pivotal factors influencing shoppers' interactions with SRs in physical store environments.