Search results

It is suggested that the detrimental externalities of intensive livestock production can be reduced by manufacturing animal proteins with cellular agriculture technologies. This study explores consumer attitudes towards cultured proteins based on representative Finnish survey data (n = 1,452).

Sum variables from the principal component analysis were utilized in the cluster analysis to identify potential consumer groups of cultured proteins in Finland. A regression analysis was used to find out the explanatory factors of positive first reaction, willingness to taste, willingness to use and support for the establishment of a national cultured meat sector.

Most of the respondents (72%) would taste cultured products, but attitudes of optimists (n = 516), moderates (n = 479) and sceptics (n = 457) differ in terms of the environment, livestock farming and cultured proteins. Most optimists (77%), almost quarter (23%) of moderates and less than a fifth (18%) of sceptics support cultured proteins. The environmental concerns are shared by optimists and moderates, whereas moderates and sceptics tend to be more suspicious. Positive attitudes are significantly influenced by social norms and respondents' beliefs regarding their global and national benefits. Major concerns pertain to anticipated dictation force of big companies, negative effects on Finnish agriculture, product attributes, use of genetically modified organisms and experienced (un)naturalness of cultured foods.

This study contributes to the understanding of Finnish consumers' attitudes towards cultured proteins. The identification of potential consumer segments and the elucidation of their attitudes are relevant, given the anticipated acceleration in the development of cultured foods.

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.

Smart banking services (SBS) are critical for developing countries to achieve developmental goals. The success of SBS is dependent on the considerable perceived customer experience of provided services. Based on technology adoption studies, this study aims to model smart customer experience (SCE) outcomes by investigating the relationships between SCE, customer gratitude, continuance intentions and positive word-of-mouth (P-WOM).

The current research included 384 bank clients as participants. The data were analyzed using partial least squares structural equation modeling (PLS-SEM).

According to the findings, SCE directly increases customer gratitude, continuance intention to adopt smart services and P-WOM. Customer gratitude enhances continuance intentions and P-WOM. Additionally, customer gratitude mediates the relationship between SCE, continuance intention and P-WOM. Finally, the findings revealed that customer innovativeness and optimism play a substantial moderating impact among the variables studied.

This is the first research to include all of these variables. Furthermore, to the best of the authors' knowledge, this is the first empirical study of these linkages in the banking sector of emerging nations.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

Organic food consumption has received great attention due to the increase in consumer environmental and health concerns. This study intends to analyse how customers' green purchasing intentions for organic food are affected by internal factors of attitude and health consciousness and external factors of social norms and environmental concern, as well as how green trust operates as a moderator between green purchase intention and actual purchase.

A quantitative research methodology was employed in this study. The data (n = 323) were gathered via a self-administered questionnaire. The respondents, who were current purchasers of organic food, were chosen through a purposive sampling technique. Data were analysed using exploratory factor analysis and structural equation modelling with the aid of IBM SPSS 25.0 and AMOS 25.

The results reveal that customers' green purchase intention for organic products is positively influenced by internal factors (attitude and health consciousness) and external factors (social norms and environmental concern). This study also shows the moderating effect of green trust on intention and action, demonstrating the necessity of building green trust among customers to diminish green purchasing inconsistency.

The study's results have ramifications for producers of organic goods, merchants and market oversight organizations. Establishing a viable strategy while considering customers' concerns about health and the environment is necessary. The formulated strategy must target specific customer niches, therefore strengthening customers' trust in and understanding of organic food items, which will in turn diminish green purchasing inconsistency in the organic industry.

This study contributes to the existing literature by extending the Theory of Planned Behaviour model to organic food consumption and by visualizing how various factors (internal, external and green trust) affect a consumer's inclination to make organic food purchases. The authors added to the empirical evidence that green trust plays a crucial role in stimulating green buying intentions into behaviour and ultimately diminishing green purchasing inconsistency.

The Scottish Government hope to pilot judge only rape trials to increase the woefully low rape conviction rates in Scotland. The reasoning is that by removing jurors, the court will be attenuating the role that rape myths and other cognitive and social biases have on conviction rates. However, a plethora of research from cognitive and social psychology, legal literature and decision-making science has shown that experts, including judges and other legal professionals, may be no less biased than laypeople. This paper aims to outline the research highlighting that experts may also be biased, why biases in judges can be elicited, and potential alternative recommendations (i.e. deselecting jurors who score highly on rape myths and providing training/education for jurors). Furthermore, piloting with real judges, in real trials, may not be best practice. Therefore, the authors recommend that any piloting is preceded by experimental research.

N/A

Furthermore, piloting with real judges, in real trials, may not be best practice; therefore, the authors recommend that any piloting is preceded by experimental research.

To the best of the authors’ knowledge, this research is the first of its kind to directly compared the decision-making of jurors and judges within the current Scottish legal context.