Search results

This study aims to examine the impact of communication style focusing on the family business brand’s roots and virtues on inferences of manipulative intent and willingness to pay a price premium, applying the persuasion knowledge model.

This study collects data involving 337 participants from France and applies partial least squares structural equation modeling (PLS-SEM) to test the hypotheses.

The results demonstrate that communication style plays an important role in inferences of manipulative intent: focusing on the family business brand’s virtues has a positive effect while focusing on the family business brand’s roots has a nonsignificant effect. In turn, inferences of manipulative intent have a negative and significant effect on willingness to pay a price premium. Furthermore, age does not moderate the effect of communication style focusing on the family restaurant brand’s roots and inferences of manipulative intent but positively moderates the effect of communication style focusing on the family restaurant brand’s virtues on inferences of manipulative intent.

To the best of the authors’ knowledge, this study is the first to try to unpack the differing effects of communication styles in the context of family business brands. In this vein, it has insightful theoretical and managerial implications for family business brands.

This paper aims to examine the convergence and divergence of business and production practices in the global wine industry, particularly focusing on Pinot Noir producers in Burgundy, New Zealand and South Africa (SA). This study explores the interplay between firm-specific factors and regional contexts to identify competitive advantage drivers among Pinot Noir producers.

This research uses a comparative analysis approach, using data from a comprehensive winery level survey. This study applies methodologies akin to value chain analysis to unravel the configuration of productive and technology/knowledge creation activities within wineries across three regions.

This analysis reveals both convergence and divergence in business and production practices among Pinot Noir producers in Burgundy, New Zealand and South Africa. Although there is a degree of convergence in marketing, distribution and competition strategies, differences exist in production practices and firms’ capabilities. Burgundy emphasizes tradition and terroir expression, contrasting with the modernization and innovation focus observed in New Zealand and South Africa. However, all regions share a commitment to quality as a competitive advantage.

This study acknowledges limitations such as the focus on a specific grape variety and regions, the absence of performance impact analysis and the need for additional variables like environmental, institutional and cultural factors and consumer preferences to provide a comprehensive understanding of industry dynamics.

The insights from this study offer practical implications for winemakers, industry stakeholders and policymakers. Producers can optimize production and marketing strategies based on regional contexts and market segments, whereas stakeholders can identify emerging trends and opportunities in the global wine market. Policymakers can develop targeted policies supporting innovation, sustainability and competitiveness.

This paper provides a unique contribution by conducting a comparative firm-level analysis across distinct wine-producing regions, shedding light on the nuanced interplay of factors shaping competitive advantage among Pinot Noir producers. This study’s comprehensive data set and methodological approach enhance understanding and offer valuable insights for industry stakeholders and policymakers.

Our result of this paper aims to indicate that the beta pricing formula could be applied in a long-term model setting as well.

In this paper, we show that the capital asset pricing model can be derived from a three-period general equilibrium model.

We show that our extended model yields a Pareto efficient outcome.

The capital asset pricing model (CAPM) model can be used for pricing long-lived assets.

Long-term modelling and sustainability can be modelled in our setting.

Our results were only known for two periods. The extension to 3 periods opens up a large scope of applicational possibilities in asset pricing, behavioural analysis and long-term efficiency.

This study combines the TAM and UGT frameworks to investigate how Vietnamese students' views of ChatGPT and intrinsic needs affect their intentions to use it for education (via variables including perceived ease of use, perceived usefulness, novelty, information seeking and academic content creation). We will employ TAM theory (Davis, 1989) and UGT theory to elucidate university students' motivations for utilising ChatGPT in Vietnam. Simultaneously, we aim to address the limitation stemming from data uniformity. Our research will make a substantial contribution to the understanding of researchers regarding the use of ChatGPT and its varied consequences as it grows and develops.

This study was conducted at a private university in Vietnam with an estimated population of 15,000 students. One of Vietnam’s top private information technology institutions requires its students to use a variety of information and communication technologies (ICTs) on a regular basis to facilitate and enjoy their academic pursuits (Ngo, 2024; Nguyen). Students who are familiar with ChatGPT and have access to it for educational purposes are the ones that were chosen. This research is a quantitative study that utilises primary data through a survey method. Participants answered a questionnaire online through the Google Form platform sharing via social media platforms from October to December 2023. The questionnaire was divided into two sections: the first contained screening questions and demographic information and the second had five-point Likert-scale questions that measured the study’s components. Two screening questions are used to separate out the intended responders. (i.e. “I have heard the name ChatGPT” and “I know about ChatGPT”) were set to find whether the participants had any knowledge of ChatGPT. If participants were unaware of ChatGPT, their responses were not included in the study. A total of 283 responses were received. The participant’s demographic information is shown in Table 1. It is believed that a sample size of more than 200 provides adequate statistical power for data analysis in structural equation modelling. It is evident that the 283-sample size in this study is adequate to evaluate the research hypothesis and the fitting model. 42.9% of the 283 research samples were made up of men, while 57.1% were women. Business administration accounted for 40.1% of survey respondents, followed by information technology (25.2%) and English language (14.5%). The average ChatGPT usage time of respondents was 56 min in a single use. The study sample’s average age is 20–72 years old.

The present study contributes to the existing AI chatbot literature in the educational industry in several ways. First, this study addresses a gap in the literature by investigating the factors that influence students’ ITU ChatGPT for educational purposes in Vietnam. Using the extended model, we investigated factors influencing students’ intentions to use ChatGPT. It integrates three motive factors of the UGT (ACT, IS and N) with the core factors of TAM (PeoU and PU). The integrated framework’s findings indicate that in a Vietnamese educational setting, ChatGPT is a novel technology that should be considered in conjunction with PU and PEoU.

First, only Vietnamese students make up the research sample. To increase the relevance of the findings, it is advised that future research look at the study model in various geographic regions. Second, the present investigation’s constraints arise from the lack of clarity about the Chat GPT version utilised by the respondents, specifically whether it was the free or premium edition. Furthermore, the limited duration of the survey presents an obstacle to gathering thorough data. Due to their reliance on the particular features and functionalities of the Chat GPT version used, these restrictions may have an effect on the researcher’s objectivity. Furthermore, the limited survey time may curtail the coverage of the collected answers, particularly considering that our survey predominantly focuses on business administration students, thus limiting the diversity and richness of the gathered data. We recommend that future studies should conduct comparative research between different versions of GPT Chat, including free and paid variants that can provide valuable insights into potential differences in performance and results. Such analysis can provide a deeper understanding of the strengths and limitations of different GPT Chat versions. Third, because the study focused on behavioural intention, actual usage and post-usage behaviour may not be covered by the findings. To gain a deeper understanding of users' actual behaviour, we suggest doing research on their usage and post-usage behaviours.

The findings will assist service providers and legislators in determining critical variables and influencing students' incentives to use ChatGPT in educational settings that use constructivist teaching methods. As a result, the information will assist service providers in creating AI chatbots that are more user-friendly, visually appealing, efficient, safe and convenient for education. Governments, in conjunction with service providers, have the potential to significantly accelerate the adoption of AI-based chatbots by highlighting their ethical and sustainable use. The findings demonstrate that students' ITU towards ChatGPT is substantially impacted by PU and PEoU. It is recommended that service providers emphasize the advantages and ease of use of AI chatbots in order to draw new clients. Additionally, in order to promote ChatGPT or related technologies, marketers should concentrate on raising the technology’s perceived novelty value. This is because people are open to new technologies as long as they believe they are interesting and innovative.

ChatGPT is an advanced AI-powered chatbot that has the potential to advance and revolutionize the learning and teaching process. This study attempted to look at the elements that lead students to want to use ChatGPT from an academic standpoint by combining the UGT and TAM. For practitioners, academics and educators, the findings provide a solid knowledge of and encouragement for the sustainable use of such AI tools. Despite having important practical consequences, the study contains a number of limitations that indicate possible research gaps that should be filled by further investigation.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

In recent years, investing with robo-advisors has gained momentum and is seen as a simplifying approach for individual investors to participate in financial markets. This chapter contributes to a better understanding of the concept of a robo-advisory and its implications for private investors by discussing its past, present, and future. It explores key issues, like cost-efficiency, historical performance, and automation levels, based on research and industry insights. Moreover, this chapter examines a robo-advisor's benefits, limitations, and challenges, like behavioral biases, regulation, and risk profiling. Finally, the importance of the ongoing megatrends of AI and green investing is examined concerning a robo-advisory.

Using the analogy of “If you build it, they will come” from the movie Field of Dreams, this study aims to draw on relational signaling and commitment–trust theories to examine the role of different types of trust (cognitive, affective and relational) in the relationship between relational-encounter quality and customer loyalty to service employees and to the organization in the case of an integrated casino resort. The study confirms that building it (trust), they (customers) will come to the casino exhibited in their loyalty to casino hosts and their affiliated casinos.

The study was conducted with very important person (VIP) customers who have a designated VIP host to provide them with personal services at an integrated casino resort in Macau. The questionnaire was distributed to the respondents by VIP hosts using WeChat and Tencent QQ.

The study shows that different types of trust (cognitive, affective and relational) play a significant mediation role in the relationship between relational-encounter quality and the customers’ loyalty to the hosts and their affiliated casinos.

To the best of the authors’ knowledge, this study is the first to investigate the roles of different types of trust (cognitive, affective and relational) in the relational encounter between casino hosts and VIP customers and provides insights into the link between service employees and their firm through the nurturing of the service encounter with the firm’s key accounts.

There are calls for irradiated food labelling due to a significant need for food safety and extensive procedures to safeguard consumer health. Additionally, there is a strong push from producers for mandatory Country of Origin (COO) labelling. The study examines how the COO and Radura labels influence consumer behaviour and shows the interplay between these influences. Using Attitude-Behavior-Context theory, a conceptual model is proposed and tested to evaluate these factors. The use of multiple labels allows for more choices. How the information presented on labels corresponds to consumers' pre-purchase information search is tested on regular label users.

Structural Equation Modelling (SEM), Generalised Linear Modelling and Cluster Analysis were used to analyse the data on a sample of 322 Australian respondents.

The study revealed that COO labelling had a significant positive relationship with attitudes but a negative relationship with WTP, acting as a suppressed mediator between attitudes and WTP. Interestingly, while knowledge was not found significant, label confidence emerged as a significant factor. Furthermore, the research suggests that regular users may prioritize COO labels over Radura labels.

This research contributes novelty by being the first to address the interplay between COO and irradiated labels, complementing the growing body of literature on irradiation labelling. It also offers valuable insights for retail practitioners, providing an understanding that can facilitate the delivery of high-value multiple labels at the point of purchase.

This research addresses the critical challenge of fostering customer loyalty within the highly competitive landscape of the insurance industry. The study investigates the interplay between customer satisfaction, loyalty, and the influence of technology and service quality in the context of insurance services and in periods of crisis.

A quantitative research approach was employed, utilizing a structured questionnaire distributed among diverse insurance customers in Lebanon during crisis conditions. The data were analyzed using SPSS-Amos, incorporating descriptive statistics, correlation analysis, and structural equation modeling (SEM).

This research emphasizes the crucial role of customer satisfaction in fostering loyalty in the insurance sector, especially during crises. High satisfaction levels, influenced by user-friendly online platforms, positively correlate with increased customer loyalty. Technology plays a vital role in maintaining and improving satisfaction, making it a key driver during challenging times. Positive interactions between service quality and satisfaction further highlight the multifaceted impact of technology on shaping customer loyalty.

The research findings provide valuable insights with practical implications for insurers aiming to boost customer loyalty. The study recommends strategic investments in critical areas like claims processing, customer service, communication strategies, digitalization initiatives, and employee training. The study provides insights applicable particularly to insurance companies navigating crisis conditions.

This research contributes both to academic understanding and practical applications by shedding light on the distinctive challenges and opportunities faced by insurers in cultivating customer loyalty within the insurance industry during crisis. The elucidations provided serve as a foundation for developing targeted strategies to address these challenges and to leverage opportunities for enhanced customer loyalty.

The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders.

This study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further.

The results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy.

One limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics.

The study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools.

Utilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision.