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Purpose: The present study investigates how the performance of Greek bank branching varies when the external environment causes dramatic changes that are reflected in recession and capital control effects.

Design/Methodology: A unique dataset of accounting Profit and Loss statements of retail branches of a systemic Greek commercial bank, closely supervised by the European Central Bank (ECB), is utilized. A profit bootstrap Data Envelopment Analysis (DEA) model is selected to measure the bank branch efficiency. The derived efficiency estimates are analyzed through a second-stage panel data regression analysis against a set of efficiency drivers related to branch profitability, diversification of income, branch size, and branch activity.

Findings: The results indicate that recession negatively affects branch efficiency in the short and long run. The occurrence of recession significantly intensifies the efficiency premium of branch profitability, reduces the efficiency premium of diversification of income (i.e., a negative efficiency effect is recorded during the early recession period), while mitigating the generally negative efficiency effect of branch size. The analysis of efficiency effects from the deep recession period that encompasses capital controls reveals the importance of diversification of income for the improvement of profit efficiency at bank branch level.

Originality/Value: This is the first branch banking study that explores branch efficiency alteration and the dynamic of branch efficiency drivers when the economy suddenly enters recession and afterwards when conditions are becoming extremely difficult and consequently capital controls are imposed on the economy.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

The purpose of this study is to address the concept and the step-by-step procedure of a high-precision optical alignment test for spacecrafts using digital theodolites. The proposed scheme focuses on the non-contact alignment qualification of spacecraft components during the integration and test phases until the launch event.

The proposed approach is based on the exploitation of the auto-collimation feature of theodolites and several prisms attached to the requested component and satellite configuration. As soon as the misalignment measurement including the difference between the real and desired attitude or position aberration of an instrument is made, the results must be transformed from the component level to the system level for misalignment error identification in the spacecraft dynamic model.

The paper introduces the main instruments, the defined coordinate systems and the architecture of the optical spacecraft misalignment test. Moreover, the guideline of the test implementation and the resulting data process have been presented carefully.

There is no limitation associated with this method because the procedure is applicable for high-precision typical missions.

This paper describes a fully implementable scheme to examine any possible inaccuracy in mounting of the spacecraft components both in position and orientation. The test can be performed without the need for a huge budget or complicated hardwares.

The contribution of this work revolves around illustrating the context and procedure of the spacecraft misalignment test which has remained unknown in literature despite the frequent implementation in the different satellite projects.

To advance the learning of professional practices in teacher education and medical education, this conceptual paper aims to introduce the idea of representational scaffolding for digital simulations in higher education.

This study outlines the ideas of core practices in two important fields of higher education, namely, teacher and medical education. To facilitate future professionals’ learning of relevant practices, using digital simulations for the approximation of practice offers multiple options for selecting and adjusting representations of practice situations. Adjusting the demands of the learning task in simulations by selecting and modifying representations of practice to match relevant learner characteristics can be characterized as representational scaffolding. Building on research on problem-solving and scientific reasoning, this article identifies leverage points for employing representational scaffolding.

The four suggested sets of representational scaffolds that target relevant features of practice situations in simulations are: informational complexity, typicality, required agency and situation dynamics. Representational scaffolds might be implemented in a strategy for approximating practice that involves the media design, sequencing and adaptation of representational scaffolding.

The outlined conceptualization of representational scaffolding can systematize the design and adaptation of digital simulations in higher education and might contribute to the advancement of future professionals’ learning to further engage in professional practices. This conceptual paper offers a necessary foundation and terminology for approaching related future research.

This study aims to explore whether Chat Generative Pre-training Transformer (ChatGPT) can produce quantitative data sets for researchers who could behave unethically through data fabrication.

A two-stage case study related to the field of tourism was conducted, and ChatGPT (v.3.5.) was asked to respond to the first questionnaire on behalf of 400 participants and the second on behalf of 800 participants. The artificial intelligence (AI)-generated data sets’ quality was statistically tested via descriptive statistics, correlation analysis, exploratory factor analysis, confirmatory factor analysis and Harman's single-factor test.

The results revealed that ChatGPT could respond to the questionnaires as the number of participants at the desired sample size level and could present the generated data sets in a table format ready for analysis. It was also observed that ChatGPT's responses were systematical, and it created a statistically ideal data set. However, it was noted that the data produced high correlations among the observed variables, the measurement model did not achieve sufficient goodness of fit and the issue of common method bias emerged. The conclusion reached is that ChatGPT does not or cannot yet generate data of suitable quality for advanced-level statistical analyses.

This study shows that ChatGPT can provide quantitative data to researchers attempting to fabricate data sets unethically. Therefore, it offers a new and significant argument to the ongoing debates about the unethical use of ChatGPT. Besides, a quantitative data set generated by AI was statistically examined for the first time in this study. The results proved that the data produced by ChatGPT is problematic in certain aspects, shedding light on several points that journal editors should consider during the editorial processes.

本研究旨在探讨ChatGPT是否能够为那些可能通过数据伪造行为不道德的研究人员生成定量数据集。

本研究进行了与旅游领域相关的两阶段案例研究, 并要求ChatGPT（v.3.5.）代表400名参与者回答第一个问卷, 以及代表800名参与者回答第二个问卷。通过描述统计、相关分析、探索性因子分析、验证性因子分析和哈曼的单因素测试对人工智能生成的数据集的质量进行了统计测试。

结果显示, ChatGPT能够按照所需的样本大小水平回答问卷, 并以表格格式呈现生成的数据集, 以便进行分析。还观察到ChatGPT的回答是系统性的, 并且它创建了一个在统计上理想的数据集。然而, 本研究注意到所产生的数据在观察变量之间存在较高的相关性, 测量模型未能达到足够的拟合度, 并出现了共同方法偏差的问题。本研究得出的结论是, ChatGPT目前不能生成适用于高级统计分析的数据, 或者说不适合这样做。

本研究表明, ChatGPT可以为试图不道德地伪造数据集的研究人员提供定量数据。因此, 它为关于ChatGPT不道德使用的持续争论提供了一个新而重要的论点。此外, 在本研究中首次对由人工智能生成的定量数据集进行了统计检验。结果表明, ChatGPT生成的数据在某些方面存在问题, 为期刊编辑在编辑过程中考虑的几个要点提供了启示。

Changes in eating habits and food preferences caused by circadian rhythm disruptions in shift workers increase the risk for chronic diseases. Several studies demonstrated that shift work exerts adverse effects on various risk factors for cardiometabolic disease and cancer. Moreover, shift work leads to altered eating patterns, including diets low in alkaline foods (such as vegetables, fruit and legumes) and high in acidogenic foods (such as meat, fish and cheese). Previously not examined, this imbalance could result in a high dietary acid load (DAL). DAL has been linked to low-grade metabolic acidosis, tissue inflammation and other metabolic alterations. The aim of this study was to investigate the hypothesis that shift work is positively associated with an increased DAL.

The authors used cross-sectional data from the National Health and Nutrition Examination Surveys (2007–2010). The sample for this analysis was drawn from a representative multistage-probability sample of US civilians. DAL estimations included potential renal acid load (PRAL) and net endogenous acid production (NEAP). Acid load scores and nutrient intake were compared between regular day workers (n = 3,814; mean age, 43.7 ± 0.3; mean body mass index (BMI), 28.6 ± 0.1; females 46.9%) and shift workers (n = 881; age, 37.4 ± 0.6; BMI, 28.4 ± 0.3; females 48.1%). Evening/night shift workers combined with rotating shift workers were contrasted to participants on a regular daytime schedule.

Potassium and magnesium intake were significantly lower in shift workers, whereas no significant differences with regard to total energy intake were found. The authors found significantly higher crude NEAP_F scores in shift workers (mean: 61.8 ± 1.2 mEq/day) compared to regular day workers (mean: 58.7 ± 0.5 mEq/day, P = 0.023). After adjustment for confounders, however, this association remained no longer significant. There were no significant intergroup differences regarding NEAP_R and PRAL_R.

This study revealed significant differences in crude NEAP_F scores between shift workers and day workers. After adjustment for confounders, this association was no longer significant. Such discrepancy is probably because of the scarce homogeneity in the analyzed sample which represents the main limitation of this study. Thus, the results did not confirm the hypothesis that shift work is associated with elevated DAL scores. Importantly, the data suggest significant intergroup differences in nutrient intakes without confirming our initial hypothesis with regard to DAL.

In light of the detrimental health outcomes associated with an increased DAL, this study reinforces the call for evidence-based dietary guidelines in shift workers. Studies in other shift working populations are thus warranted.

In the intellectual property (IP) and management literature, the question of how external patent attorneys impact patent filings has been understudied. The purpose of this paper is to advance this area of research by examining how the use of external patent attorneys influences the patent filing strategies of firms and what impact firms’ level of experience with the exclusive use of in-house resources has on filing strategies. This study, thus, provides insights into the strategic dimension behind patent filing, a process which is affected by patent attorneys’ work and decision-making processes.

The econometric analysis is based on a patent database of 922,553 patents which is combined with an EPO patent database covering applications from 1990 to 2010. The authors test the hypotheses for this study using patent indicators addressing the impact of in-house firm experience vs the use of external patent attorneys on firm’s filing strategy.

This research finds empirical evidence that external patent attorneys’ work has an effect on patent scope, international scope, and patenting speed. Moreover, it can be shown that external patent attorneys have a positive impact on most filing dimensions, such as patent scope, international scope and the Patent Cooperation Treaty option, whereas the level of in-house firm experience has a negative impact on most filing dimensions. This implies that external patent attorneys seem to pursue a “maximization approach” while experienced firms seem to pursue a more differentiated approach to filing patents, for instance, drafting narrower and more focused patents.

The study suggests that effective filing strategies require an integrated approach between diverse IP stakeholders. More particularly, filing strategies should be communicated and aligned between all actors, including external patent attorneys in order to achieve the targeted patenting output.

The current study develops a patent filing typology, which accounts for patent attorneys’ decision options. In providing insights into patent attorneys’ work and their impacts on intellectual property rights management, the study is a useful complement to prior research, which has predominantly focused on applicants or examiners.

In an exploratory approach, the authors conducted a structured literature review to extract candidate process digitalization project (PDP) success factors (SFs) from the literature on business process management (BPM), project management (PM) and digitalization. After that, the authors validated, refined and extended these intermediate results through interviews with 21 members of diverse PDP teams. Finally, the authors proposed the PDP success model by linking the candidate SFs with relevant success criteria.

Digitalization substantially impacts organizations, which increasingly use digital technologies (DTs) to improve and innovate their business processes. While there are methods and tools for identifying process digitalization ideas and related projects (PDPs), guidance on the successful implementation of PDPs is missing. Hence, the authors set out to explore PDP SFs.

The PDP success model covers 38 PDP success factor candidates, whereof 28 are already backed by the literature and ten have emerged during the interviews. Furthermore, the SFs are structured according to seven categories from the literature covering a broad range of sociotechnical topics (i.e. strategy, structure, culture, people, process, project and technology) as well as equipped with preliminary success rationales.

The work is the first to systematically explore PDP SFs. The PDP success model shows that PDPs require a unique set of SFs, which combine established and hitherto underrepresented knowledge. It extends the knowledge on BPM and serves as foundation for future (confirmatory) research on business process digitalization and the successful implementation of PDPs.

Researchers often stress the predictive goals of their partial least squares structural equation modeling (PLS-SEM) analyses. However, the method has long lacked a statistical test to compare different models in terms of their predictive accuracy and to establish whether a proposed model offers a significantly better out-of-sample predictive accuracy than a naïve benchmark. This paper aims to address this methodological research gap in predictive model assessment and selection in composite-based modeling.

Recent research has proposed the cross-validated predictive ability test (CVPAT) to compare theoretically established models. This paper proposes several extensions that broaden the scope of CVPAT and explains the key choices researchers must make when using them. A popular marketing model is used to illustrate the CVPAT extensions’ use and to make recommendations for the interpretation and benchmarking of the results.

This research asserts that prediction-oriented model assessments and comparisons are essential for theory development and validation. It recommends that researchers routinely consider the application of CVPAT and its extensions when analyzing their theoretical models.

The findings offer several avenues for future research to extend and strengthen prediction-oriented model assessment and comparison in PLS-SEM.

Guidelines are provided for applying CVPAT extensions and reporting the results to help researchers substantiate their models’ predictive capabilities.

This research contributes to strengthening the predictive model validation practice in PLS-SEM, which is essential to derive managerial implications that are typically predictive in nature.