Search results

Wine consumer behavior has long been a topic of discussion among scholars and industry professionals aiming to understand the underlying predictors of key behavioral outcomes. To help explain wine consumer behavior, concepts such as involvement, expertise, loyalty, satisfaction and perceived risk are often examined. The overarching objective of this study is to determine the relationship between these predictors and their impact on wine purchase intention utilizing a meta-analytical structural equation modeling (MASEM) technique.

As MASEM provides substantive evidence regarding the relationships between theoretical constructs through the combination of multiple studies, the researchers’ aim is to make definitive statements about the predictors of purchase intention.

Findings revealed several relationships that support previous research but also identified relationships that contradict previous literature. This study contributes valuable insights into consumer behavior that wine brands can utilize to improve their marketing efforts.

Wine marketers with a greater understanding of the stronger predictors of purchase intention should be able to create marketing plans that drive wine sales.

Despite the abundance of research that has utilized these theoretical constructs to demonstrate their propensity for determining behavioral outcomes such as purchase intention, no previous attempts have synthesized this body of literature through the use of meta-analysis.

The purpose of this paper is to examine the evolution of the literature on technology transfer and culture, identifying the main contents of the current body of knowledge encompassing culture and technology transfer (TT), thus contributing to a better understanding of the relationship between TT and culture based on bibliometric and multivariate statistical analyses of the relevant body of literature.

Data for this study were collected from the Web of Science (WoS) Core Collection database. Based on a bibliometric analysis and in-depth empirical review of major TT subjects, supported by multivariate statistical analyses, over 200 articles were systematically reviewed. The use of these methods decreases biases since it adds rigor to the subjective evaluation of the relevant literature base.

The exploratory analysis of the articles shows that first, culture is an important topic for TT in the literature; second, the publication data demonstrate a great dynamism regarding the different contexts in which culture is covered in the TT literature and third, in the last couple of years the interest of stimulating a TT culture in the context of universities has continuously grown.

This study focuses on culture in the context of TT and identifies the main contents of the body of knowledge in the area. Based on this first insight, obtained through more detailed bibliometric and multivariate analyses, it is now important to develop and validate a theory on TT culture, emphasizing the dimensions of organizational culture, entrepreneurial culture and a culture of openness that fosters economic and societal spillovers, and to link those dimensions to the performance of TT activities.

From the practical point of view, managers in companies and universities should be aware of the importance of identifying those dimensions of culture that contribute most to the success of their TT activities.

Despite several literature reviews on the TT topic, no studies focusing specifically on culture in the context of TT have been developed. Therefore, given the multifaceted nature of the research field, this study aims to expand and to deepen the analysis of the TT literature by focusing on culture as an important and commonly cited element influencing TT performance.

The purpose of this study is to provide an overview of artificial intelligence (AI) in the talent management sphere. The study seeks to contribute to the body of knowledge with respect to human resource management and AI by conducting a literature review on the integration of AI in talent management, synthesising existing approaches and frameworks, as well as emphasising potential benefits.

The study adopts desk research, computational literature review (CLR) and uses topic modelling [with bidirectional encoder representations from transformers (BERTopic)] to throw light on the diffusion of AI in talent management.

The study’s main finding is that the area of AI in talent management is on the verge of gradual development and is in tandem with the growth of AI. We deduced that there is a link between talent management practices (planning, recruitment, compensation and rewards, performance management, employee empowerment, employee engagement and organisational culture) and AI. Though there are some known fears with regards to using the innovation, the benefits outweigh the demerits.

The current study has some limitations. The scope and size of the sample are the primary limitations of this study. No form of qualitative analytics was used in this study; as a result, the information obtained was limited. The study provides a snapshot of AI in talent management and contributes to the lack of literature in the joint fields. Also, the study provides practitioners and experts an overview of where to target investments and resources if need be.

The originality of this study comes from the combination of CLR methods and the use topic modelling with BERTopic which has not been used by previous reviews. In addition, the salient machine learning algorithms are identified in the study, which other studies have not identified.

This paper aims to develop the building information modeling (BIM) investment decision model (BIDM) for Hong Kong architecture, engineering, construction and operation (AECO) industry utilization in early BIM investment decision-making. The developed BIDM is designed to assist company leaders in measuring and amending their investment decisions and BIM strategy by considering estimators [features and net positivity (NP)] and results based on BIDM.

This research is conducted using a mixed methodology of qualitative and quantitative analysis. The necessary indicators were collected from literature and interviews with relevant researchers, where 545 semistructured questionnaires were distributed to selected AECO company leaders and collected by the authors. The least absolute contraction and selection operator (LASSO)-based result was conducted to help company leaders. The results of the validation test validated the model based on the LASSO method and the outcomes of the p-value test also supported the significance of BIDM.

More than 80 determinators were processed to conduct 19 main indicators for generating BIDM, and 6 significant main indicators on final BIDM. The data set of this research included 483 samples, which are categorized into 7 groups according to their role in an infrastructure project.

To the best of the authors’ knowledge, this is the first LASSO-used investment decision-making model integrated with the proposal of NP in the AECO industry. The value of current knowledge is the development of BIDM, which benefits company leaders in BIM investment decision-making and commercially benefits consulting cooperators as an investment forecasting tool. BIDM will help future users make better, more dynamic investment strategies.