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The purpose of this study was to comprehend the adoption of artificial intelligence (AI) technology-driven natural large language model (LLM)-based chatbots by customers.

A qualitative research study method was conducted. This was to explore managerial perspectives towards consumer centric technology adoption of AI plus LLM-based chatbots. This was specifically for AI-driven natural LLM-based chatbots services. The author conducted conducted in-depth personal interviews with 32 experts of digital content AI + LLM chatbot services. Thematic content analysis was undertaken to analyse the data.

The advent of natural language processing tools driven by AI technology chatbots has altered human-firm interaction. The research findings indicated that the push-pull-mooring (PPM) factors captured the phenomenon in the most comprehensive way. A total of 15 key factors influencing the adoption of AI technology-driven natural LLM-based chatbots by customers during firm customer interaction were identified in this study by the author. The thematic content analysis unraveled insights regarding transformed consumer adoptions towards AI-driven LLM-based chatbots by means of the PPM framework factors.

The empirical research investigation contributed to the literature on the PPM theoretical framework. This was specifically in the context of adoption of AI technology-driven natural LLM-based chatbots by customers during firm customer interaction.

The research study insights would help managers to restructure and reconfigure their organizational processes. This would neccessiated a shift in firm-customer interactions as demanded because of the availability of AI technology-driven natural LLM-based chatbots by customers.

This research study was based upon the PPM theoretical framework. This study provided a unique analysis of the altered firm customer interaction needs and requirements. This was one of the first studies that applied the framework of PPM theory regarding the adoption of AI technology-driven natural LLM-based chatbots by customers.

The ranking of universities and other research-intensive institutions in global ranking systems is based on numerous indicators, including number of articles with external collaboration, number of articles with international collaboration, number of articles with industry collaboration as well as co-patents with industry. The purpose of this paper is to examine university–industry research collaboration in Ghana, with the aim of exploring the relationship between the research output collaborations in the top four universities in Ghana and industry across different geographical scales.

This study’s data was obtained from the SciVal database, which drawn its data from the Scopus bibliographic and citation database. The bibliographic and citation data were extracted using a search of the publications affiliated to the University of Ghana, for the period 2011–2020.

Key findings demonstrate a constant rise in the number of research publications by the selected universities over time. Research collaboration intensity in the selected universities in terms of co-authored publications was higher as compared to single-authored publications. University–industry research co-authorships were, however, lower when compared to university–university research co-authorships. The university–industry research co-authorships occurred mostly with Europe, Asia-Pacific and North American-based institutions as opposed to African-based institutions. In Ghana, four industry-based institutions were engaged in intensive research with the selected universities.

This study demonstrates that, for each selected university, it is possible to measure the performance of individual universities in both intra-regional and international collaboration. Such results may be useful in informing policy as well as merit-based public funding of universities in Ghana.

To generate the measurement instrument seven steps were implemented. A total of 819 questionnaires were collected in Yunnan Province, China, where it has long tradition of eating insects.

This study aims to explore the influences of the multidimensional benefits of consuming insect-based food on its consequences. Food neophilia and hedonic motivation are used as moderating variables.

Health, nutritional value, taste and cultural domains effectively explained consumers’ attitudes toward insect-based food, food consumption value, satisfaction, subjective well-being, loyalty to the restaurant and community attachment. Food neophilia and hedonic motivation partially moderated the relationships between the proposed constructs.

This study provides a conceptual model for exploring insect-based food consumption experiences and offers a useful guideline for developing and designing marketing strategies for stakeholders in the food and restaurant industry.

This is one of the few studies examining the benefits of consuming insect-based food from the perspective of consumers.

Computer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.

The developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.

Three DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.

This study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

The purpose of this paper is to investigate relative portfolio performance between sin stock returns and faith‐based returns.

Similar to Hong and Kacperczyk, Jensen's alpha was utilized to conduct tests along with three asset‐pricing models and rolling regression technique to reveal that faith‐based and sin betas move in opposite directions during most of the sample period.

Norm‐neglect was found, in that Jensen's alpha is positive and significant for the sin portfolio. Further, evidence in favor of norm‐conforming investor behavior was found, where Jensen's alpha is negative and significant for the faith‐based portfolio. These findings provide evidence that the sin portfolio outperforms the faith‐based portfolio relative to the market. A rolling regression technique reveals that faith‐based and sin betas tend to move in opposite directions during most of the sample period. The evidence suggests that faith‐based beta has an average estimated beta of one, mimicking the market. The sin portfolio, however, has an average estimated beta of one‐half. Finally, the reward‐to‐risk measure, Sharpe ratio, is statistically higher for the sin portfolio relative to the faith‐based portfolio.

This paper contributes to the literature in the following distinct ways. First, three asset‐pricing models are estimated to examine Jensen's alpha for sin and faith‐based portfolios. Second, a rolling regression procedure is used to examine the dynamic behavior relative to the market of the sin and faith‐based portfolios. Third, use is made of the Jobson and Korkie test, which allows for statistical comparisons of Sharpe ratios. Lastly, daily instead of monthly data and a different sample period are used to examine the research questions posed in this study.

The current emphasis in anti‐money laundering (AML)/ counter terrorist financing (CTF) regulation on “risk‐based” strategies means that regulatory, law enforcement and reporting agencies need to respond to money laundering and terrorist‐financing threats in ways that are proportionate to the risks involved. However, the way that risk is conceptualized remains vague, and the requirements on agencies imposed by the risk‐based approach involve a significant element of uncertainty. The paper addresses these issues.

This paper examines the attributes of risk as it applies to AML/CTF strategy in the context of regulatory risk and related forms of risk assessment, and argues that there are a number of conditions that must be met if risk‐based decision‐making for AML/CTF is to work effectively.

This paper argues that there are a number of conditions that must be met if risk‐based decision‐making is to work effectively. Three of the most important conditions are that there has to be agreement about what risk is being decided on; there must be explicit, quantifiable models of risk, and those responsible for developing and refining risk‐based decision models must have access to knowledge about the outcomes of assessments.

The paper identifies the need for fundamental changes in the relationship between the regulators and the regulated.

The purpose of this paper is to describe a new tool that can be used to help in the design and evaluation of work-based elements within programmes or to evaluate whole programmes.

This paper takes a case study approach to describe the development of the mapping tool. The tool is based on a matrix which enables users to map four variables: teacher-centred delivery, employer-centred delivery and students outcomes in terms of knowledge and skills.

The mapping tool provides a useful approach to evaluating the outcomes for work-based learning activities.

The mapping tool provides tutors with a useful, easily used way to visualise the nature of their work-based learning activities.

This paper presents a novel, practical and useful tool that has wide applicability in the field of work-based learning.

We compare potential‐based (ø‐U‐P0) and displacement‐based finite element methods for static analysis of contained fluids. A general transient formulation may be specialized to static analysis in both cases. In the potential‐based method velocity potentials (ø) and a single pressure (P0) variable are the unknowns in the fluid region. Displacements are the unknowns in the fluid for displacement‐based methods. Higher‐order displace‐ment‐based elements may produce singular matrices for some static analyses, restricting us to four‐node elements for reliability. While both methods can yield excellent results when compared with experimental data, potential‐based methods appear to have computational advantages over displacement‐based methods.

In this paper, the organism model for knowledge‐based enterprise is proposed. A dynamic capacity grey set is defined and analyzed based on the definition of the growth and development for knowledge‐based enterprise organism. The structure of the capacity whiten core, a subset of the capacity grey set, is optimized for different periods of the organism's life cycle. The organism grey topological structure model of knowledge‐based enterprise is described to possess the essential capacity grey set.

The purpose of this paper is to develop and justify a theoretical framework for analyzing the relationship between manufacturing strategy, business strategy, time-based manufacturing competence, capability and competitiveness and their impact on firm performance for firms operating in the manufacturing sector. Many executives and scholars have argued that time is an important component for developing a brilliant strategy to achieve a sustainable competitive advantage for the firm.

This paper provides a theoretical framework primarily concerned with the relationship between time-based manufacturing competence, competitive priorities and firm performance. The framework suggests that firms focusing on time as a strategic factor at both strategic levels – business strategy and manufacturing strategy – can achieve a multi-competitive advantage, and, in turn, high performance.

To realize the level of performance associated with time-based manufacturing competence, it is essential for firms to identify the areas in which time can be reduced. These include reduction in design lead time, product concept to production; time-based competition for product-to-market firms; time-based manufacturing competence; product development activities; fast-to-product; and customer service.

This article provides a theoretical framework for linking manufacturing strategy to business strategy and performance to help expand the body of knowledge for other researchers to follow.