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Brand reputation (BR) is one of the most important factors that affect the consumer–brand relationship and give businesses a competitive advantage. Businesses with a strong BR can increase their market shares and product market prices, in addition to gaining a competitive advantage. In order for businesses to have these advantages, they need to know and analyze their consumers. This study aimed to develop an alternative analysis method by using classification algorithms and regression analysis to measure and evaluate the effect of consumers' BR perceptions on their willingness to pay premium prices (WPP).

The research data were collected from 483 participants by the online survey method due to the COVID-19 pandemic. The data were first analyzed with regression analysis, and the effect of BR on WPP was found to be significant. Then, using artificial intelligence (AI) methods that were not used in previous studies, consumers' perceptions of BR and WPP were clustered and classified.

The results revealed the highest and lowest customer groups with BR and WPP and empirically demonstrated that highly accurate practical classification models can be applied to determine strategies in line with these findings.

The model proposed in this study offers an integrated approach by using AI and regression analysis together and tries to fill the gap in the literature in this field. Therefore, the novelty of this study is to quantitatively reveal and evaluate the relationship between BR and WPP by using AI classification algorithms and regression analysis together.

This paper aims to investigate, upon taking into consideration both symmetric and asymmetric effects, how the economic and psychological impact of the coronavirus disease 2019 (COVID-19) pandemic, solidarity and future anxiety affect travel intention and the willingness to support a destination (WSD). Furthermore, the study sheds light on whether these relationships vary between domestic and international destinations.

The data are collected from 379 potential tourists. To detect and analyze the symmetrical and asymmetric effects, the covariance-based structural equation modeling (CB-SEM) and the fuzzy-set qualitative comparative analysis (fsQCA) are employed, respectively.

It is observed that, whereas only the effects of solidarity on travel intention and WSD differ in the CB-SEM, the fsQCA results include different recipes for the two groups.

There has not been much research done yet on the influence of future anxiety on tourists' decisions. Furthermore, it has not been thoroughly investigated whether solidarity has a different function for destinations within and outside of the country. In this respect, the study of both symmetric and asymmetric effects represents an important contribution to the literature.

The main aim of this study is to understand the role of smart technologies and show the rankings of various smart technologies in collection and classification of electronic waste (e-waste).

This study presents a framework integrating the concepts of collection and classification mechanisms and smart technologies. The criteria set includes three main, which are economic, social and environmental criteria, including a total of 15 subcriteria. Smart technologies identified in this study were robotics, multiagent systems, autonomous tools, smart vehicles, data-driven technologies, Internet of things (IOT), cloud computing and big data analytics. The weights of all criteria were found using fuzzy analytic network process (ANP), and the scores of smart technologies which were useful for collection and classification of e-waste were calculated using fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR).

The most important criterion was found as collection cost, followed by pollution prevention and control, storage/holding cost and greenhouse gas emissions in collection and classification of e-waste. Autonomous tools were found as the best smart technology for collection and classification of e-waste, followed by robotics and smart vehicles.

The originality of the study is to propose a framework, which integrates the collection and classification of e-waste and smart technologies.

Building on resource dependence theory and contingency theory (CT) and focusing on an emerging market setting, this study investigates how demographic board diversity (BD) influences the export intensity (EI) of firms listed on Borsa Istanbul (BIST), with the moderating effect of firm size, as a contingency factor, on this interaction.

Using a sample of 65 exporting firms listed on the BIST Industrials Index, this study explores how demographic attributes of board members, represented by the board diversity index (BDI), affects EI by employing panel data analysis over the period of 2016–2020.

The results suggest that there is a negative relationship between BD and EI, but firm size has a positive moderating effect on the association of BD and EI, indicating that large firms with diverse boards are more prone to access foreign markets and make export. The findings further indicate that board size and CEO duality have a negative and significant effect on EI, while marketing intensity has a positive and significant impact.

The sample covers only public companies listed on the BIST Industrials Index, and the impact of board characteristics on the EI is analyzed for a limited time frame, i.e. from 2016 to 2020.

The findings help business executives better understand the contribution of the firm size on the interaction of BD and EI and offers valuable insights to companies to gain a competitive edge in international markets.

The study provides evidence on the effects of board attributes on the EI from the perspective of emerging countries. It also helps to gain a deeper understanding of how board dynamics contribute to the internationalization of companies.

The COVID-19 pandemic, which appeared in China in late 2019, has affected the world psychologically, socially and economically in 2020. Tourism is one of the areas where the effects of COVID-19 have been felt most clearly. The study aims to determine the effect of negative problem orientation (NPO) and perceived risk related to the COVID-19 pandemic on travel and destination visit intention.

This study employed a convenience and probabilistic sampling method for collecting data from 531 respondents using an online questionnaire. Partial least square structural equation modeling (PLS-SEM) was used for testing research model.

According to the findings, NPO and perceived risk related to the pandemic were found to have direct and indirect effects on the travel behavior of tourists. The results of this research provide theoretical and practical implications for hospitality and travel businesses on topics such as the psychological effects of the pandemic and the travel behaviors of tourists.

It is estimated that the pandemic will also affect tourist behavior due to its effects on human psychology. For this reason, a study conducted in the context of tourist behavior theories is expected to contribute to the literature, managers and future of the tourism.

This study investigates the roles of technological development, artificial intelligence (AI), and robots in the catering business and their effects on the labor force and staff shortages.

The impact of AI, robots, and technological advancements on the labor force is outlined. Secondary sources and the results of interviews with two highly experienced experts were used to identify the transformation or metamorphosis that affects the staff situation during catering operations. This is an opinion paper that explains why it contains many of the authors’ observations and experiences.

Technological equipment and AI have been used frequently in catering operations, and robots are not grooved. In the near future, these tools could be part of a solution for the staff shortage because, with their help, less-skilled workers could be doing some of the job in the catering business.

The study is written from a limited perspective because of its qualitative nature and the fact that it is shaped by expert opinions and the author's views. It is expected that conducting the same study with a larger number of experts operating in various fields on similar topics in the future will lead to more effective results.

This research character is an opinion paper and qualitative. This research aims to explain the use of artificial intelligence, technological advancements, and robots and to explore their impacts on the labor force based on the opinions of experts and the author. The data obtained are expected to be useful for future labor force planning in catering businesses.

This study provides an exploratory framework regarding staff shortages in catering businesses and the integration of technology, AI and robots.

Based on a literature review, only a limited number of studies have been conducted on the use of technology, artificial intelligence, and robots in kitchens. None of these studies investigated their impact on the labor force. Therefore, this study is both original and valuable.

This study aims to discuss appropriate individual, organizational and macroleveled conditions when it is desired to foster intrapreneurship with nudge techniques.

Conceptual paper.

This paper aims to provide theoretical framework on the relationship between nudges approach and intrapreneurship and offer measurable propositions at different level of analysis for future research.

At the individual level it is theorized that nudges techniques on intrapreneurship will work when: “agents are aware of being nudged”, “choice architect is a well-recognized leader”, “perception of meaningful work is high” and “agents had former experience about being nudged”. At the organizational level it is claimed that the “number”, “type” and “frequency” of nudges plays the prominent role. Lastly, at the macrolevel it is postulated that “cultural adjustments”, “providing education” and “forming networks” sets the ground for pushing masses to intrapreneurial activities via nudges.

This study aims to analyse Türkiye’s industrial economic complexity index (ECI-IND) for comparison with the ECI-INDs of member countries of the Organization for Economic Co-operation and Development (OECD). It also explores the causal relationship between economic complexity and economic growth in Türkiye.

Empirical analysis was directed at industrial export baskets consisting of 760 product groups distributed by 130 countries. These data were used to calculate the product complexity index (PCI) and ECI-IND values of these countries. The calculations then served as the basis for examining Türkiye’s economic complexity in comparison with that of OECD countries. Finally, the short- and long-term relationships between the ECI-IND and GDP per capita in Türkiye were investigated using a time series analysis.

This study’s findings revealed that Türkiye ranked last in terms of economic complexity. The time series analysis showed unidirectional causality between Türkiye’s ECI-IND and its economic growth.

Türkiye should concentrate on ensuring the convergence of its ECI with those of developed countries. Based on the existing literature, it is important for Türkiye to implement policies that (1) increase human capital, (2) expand the share of R&D expenditures out of the GDP and (3) attract foreign direct investments, which advance technology transfer.

This study inquired into the ECI based on industrial products in Türkiye and accordingly provided new data on countries. It also compared Türkiye and OECD nations with respect to this index.

This paper aims to analyze the publication structure of academic research on organizational creativity between 1975 and 2022.

Bibliographic data on organizational creativity are extracted from the Scopus database and then analyzed through VOSviewer and R Statistical Software.

This paper analyzes 416 publications on organizational creativity from 1975 to 2022. Accordingly, the study identifies the most productive countries, universities, authors, journals and prolific organizational creativity publications. Also, the study uses VOSviewer and R Statistical Software Bibliometrix Package to visualize the mapping based on co-citation, bibliographic coupling and co-occurrence of keywords.

The study’s main contribution is that it provides an overview of the trends and trajectories of organizational creativity, which may help researchers and practitioners comprehend the trends and future research directions.

Despite South Africa’s ailing electrical grid, substantial renewable energy (RE) integration is planned for the country. As grid-integrated RE affects all grids differently, this study aims to develop an adaptable grid code-guided renewable power plant (RPP) control real-time simulation testbed, tailored to South African grid code requirements to study grid-integrated RE’s behaviour concerning South Africa’s unique conditions.

The testbed is designed using MATLAB’s Simulink and live script environments, to create an adaptable model where grid, RPP and RPP guiding grid codes are tailorable. This model is integrated with OPAL-RT’s RT-LAB and brought to real-time simulation using OPAL-RT’s OP4510 simulator. Voltage, frequency and short-circuit event case studies are performed through which the testbed’s abilities and performance are assessed.

Case study results show the following. The testbed accurately represents grid code voltage and frequency requirements. RPP point of connection (POC) conditions are consistently recognized and tracked, according to which the testbed then operates simulated RPPs, validating its design. Short-circuit event simulations show the simulated wind farm supports POC conditions relative to short-circuit intensity by curtailing active power in favour of reactive power, in line with local grid code requirements.

To the best of the authors’ knowledge, this is the first design of an adaptable grid code-guided RPP control testbed, tailored to South African grid code requirements in line with which RPP behavioural and grid integration studies can be performed.