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This research sought to examine the differential effect of chatbot banking artificial intelligence (AI) on consumer experience in the banking industry. A positivist paradigm was adopted to sample 389 consumers who were previously exposed to chatbot banking in Zimbabwe. A causal research design was employed whilst a quantitative approach was followed. In analysing data, the research study applied the structural equation modelling (SEM) technique. The authors found that chatbot banking significantly improves customer experience (CX) in the banking industry. Reliability and responsiveness of the chatbot need to be enhanced for effective improvements in CX. A need was also identified to enhance CX through the development of an ease-to-use chatbot which is embedded in everyday messaging applications of consumers. A significant association was also found between perceived benefits of chatbot banking and CX. This study informs the development of competitive advantage by banks and other related companies through AI-based CX management strategies. In times of pandemics and beyond, chatbot banking can be very instrumental in improving CX.

Theory of Constraints (TOC), though a well-established process improvement methodology in manufacturing, is still a novel philosophy for healthcare and an exhaustive review of literature is needed to summarize the key findings of various researchers. Such a review can provide a direction to the researchers and academicians interested in exploring the application of TOC in the healthcare sector. This paper aims to review the existing literature of TOC tools and techniques applied to the healthcare environment, and to investigate motivating factors, benefits and key gaps for identifying directions for future research in the domain of healthcare.

In this paper, different electronic repositories were searched using multiple keywords. The current study identified 36 articles published between January 1999 to mid-2021 to conceptualize and summarize the research questions used in the study. Descriptive analysis along with pictorial representations have been used for better visualization of work.

This paper presents a thorough literature review of TOC in healthcare and identifies the evolution, current trends, tools used, nature of services chosen for application and research gaps and recommends future direction for research. A variety of motivating factors and benefits of TOC in healthcare are identified. Another key finding of this study is that almost all implementations listed in literature reported positive outcomes and substantial improvements in the performance of the healthcare unit chosen for study.

This paper provides valuable insight to researchers, practitioners and policymakers on the potential of TOC to improve quality of services, flow of patients, revenues, process efficiency and cost reduction in different health care settings. A number of findings and suggestions compiled in the paper from literature study can be used for diagnosing, learning and making substantial changes in healthcare. The methodologies used by different researchers were analysed and combined to propose a generic step by step procedure to apply TOC. This methodology will guide the practising managers about the appropriate tools of TOC for their specific need.

Good health is always the first desire of all men and women around the globe. The global aim of healthcare is to quickly cure more patients and ensure healthier population both today and in future. This article will work as a foundation for future applications of TOC in healthcare and guide upcoming applications in the booming healthcare sector. The paper will help the healthcare managers in serving a greater number of patients with limited available resources.

This paper provides original collaborative work compiled by the authors. Since no comprehensive systematic review of TOC in healthcare has been reported earlier, this study would be a valuable asset for researchers in this field. A model has been presented that links various benefits with one another and clarifies the need to focus on process improvement which naturally results in these benefits. Similarly, a model has been presented to guide the users in implementation of TOC in healthcare.

The recent decade has described the role of HR practitioners as more strategic to advance in environmental management (EM), technology and change management competencies. The study aims to identify the HR professionals' changing strategies and challenges and barriers in sustainability performance (SP) through green HRM, which have become an emerging topic.

Data collection was conducted through six semi-structured face-to-face interviews with senior HR representatives through purposive sampling. The grounded theory (GT) method was applied, followed by an iterative process for codes and themes.

The results indicated the highly significant challenges and barriers (C&B) proposing a 5 × 4 framework in adopting GHRM practices. Examples of the challenges included (1) lack of knowledge, orientation and awareness; (2) corporate social responsibility as an integral part of the organization strategy; (3) environmental concerns internally required from top-bottom and bottom-top approach; (4) budget and cost that remain an issue for the top management, and; (5) HR department's responsibility to build competencies for their entire team.

The findings help the top management and policymakers maintain a balance between economic, environmental and social sustainability performance agendas. Furthermore, the environmental goals and values of the hotel are key ingredients in seeking the solution to environmental sustainability, which requires continuous training programs to enhance awareness at all levels.

The results are presented as future directions to enrich the literature and make significant contributions to the existing body of knowledge. Moreover, the research benefits the managers from the results intended in accomplishing sustainable development approaches.

The purpose of this study is to investigate how mock jurors’ experiences of deliberations are impacted by the defendant having a personality disorder.

This study used a qualitative approach to explore mock jurors’ experiences during the deliberations of a fictional defendant, Sarah Priest. Ten participants formed two mock juries, and each mock jury were given two case studies to deliberate. Case study one described Priest as having “Severe Personality Disorder, Borderline Pattern” whereas case study two described Priest as having “Complex Mental Health Problems”. There were no changes to the content of the case studies aside from the change in language used to describe the defendant.

An inductive thematic analysis identified two main themes relating to juror experience: “Interaction with Other Mock Jurors” and “Language as a Barrier to a Verdict”. Participants constructed that prosocial interactions with other mock jurors in the deliberations helped them make a verdict decision, but some of these interactions led to disagreements between participants due to a wide variation of opinion. Second, the different description of the defendant in each case study were constructed to have made the deliberations and decision-making difficult, but for different reasons. In case study one, a lack of knowledge surrounding BPD was the reason for this difficulty, and in case study two, participants thought that the applicability of diminished responsibility criteria were unclear, making it hard to reach a verdict.

The findings have key implications for the judicial system; common experiences can be identified and recorded to implement procedures to protect jurors from adverse experiences.

There is a lack of studies that have investigated juror experience in the UK, and the few studies available have used a quantitative methodology. The approach taken in the current study is, therefore, unique in a UK context. The findings have key implications for the judicial system; common experiences can be identified and recorded to implement procedures to protect jurors from adverse experiences.

The purpose of this experimental study is to examine the effects of climate change corporate social responsibility (CSR) and corporate social advocacy (CSA) messages on public perceptions of companies and collective action intentions.

This study employs a 2 (message type: CSA vs CSR) × 2 (environmental issue: single-use plastics vs renewable energy) × 2 (company: Target vs Walmart) plus control online experimental design.

There were no main effects of message type on outcomes; however, green consumer identity moderated the relationship between message type and green purchase intention as well as negative word-of-mouth.

This study responds to calls by scholars to empirically compare the effects of CSR and CSA messages. Additionally, we consider group-level processes, like ingroup identity, in influencing strategic communication outcomes.

The purpose of this paper is to present a comprehensive analysis of the carbon footprint of the Delft University of Technology (TU Delft), including direct and indirect emissions from utilities, logistics and purchases, as well as a discussion about the commonly used method. Emissions are presented in three scopes (scope 1 reports direct process emissions, scope 2 reports emissions from purchased energy and scope 3 reports indirect emissions from the value chain) to identify carbon emission hotspots within the university’s operations.

The carbon footprint was calculated using physical and monetary activity data, applying a process and economic input-output analysis.

TU Delft’s total carbon footprint in 2018 is calculated at 106 ktCO₂eq. About 80% are indirect (scope 3) emissions, which is in line with other studies. Emissions from Real estate and construction, Natural gas, Equipment, ICT and Facility services accounted for about 64% of the total footprint, whereas Electricity, Water and waste-related carbon emissions were negligible. These findings highlight the need to reduce universities’ supply chain emissions.

A better understanding of carbon footprint hotspots can facilitate strategies to reduce emissions and finally achieve carbon neutrality. In contrast to other work, it is argued that using economic input-output models to calculate universities’ carbon footprints is a questionable practice, as they can provide only an initial estimation. Therefore, the development of better-suited methods is called for.

In developing countries, construction organizations are seeking to effectively implement green innovation strategies. Thus, this study aims to assess the importance of green innovation practices and develop a measurement model for quantifying the green innovation degrees of construction firms.

A mixed-methods research approach is adopted. First, an extensive literature review is performed to identify potential green innovation items, which are then used to design a preliminary questionnaire. Next, expert interviews are conducted to pilot-test this questionnaire. Subsequently, by using a convenience non-probability sampling method, 88 valid responses are collected from construction firms in Vietnam. Then, one-sample and independent-samples t tests are employed to assess the importance of green innovation practices. Fuzzy synthetic evaluation (FSE) is also applied to quantitatively compare such practices. Finally, green innovation level (GIL) is proposed to measure the green innovation indexes and validated by a case study of seven construction firms.

This study identifies 13 green innovation variables, of which several key practices are highlighted for small/medium and large construction firms. The results of FSE analysis indicate that green process innovation is the most vital green category in construction firms, followed by green product and management innovations, respectively. As a quantitative measure, GIL could allow construction firms to frequently evaluate their green innovation indexes, thereby promoting green innovation practices comprehensively. Hence, construction firms would significantly enhance green competitive advantages and increasingly contribute to green and sustainable construction developments.

This research is one of the first attempts to integrate various green innovation practices into a comprehensive formulation. The established indexes offer detailed green innovation evaluations, which could be considered as valuable references for construction practitioners. Furthermore, a reliable and practical tool (i.e. GIL) is proposed to measure the GILs of construction firms in developing countries.

The literature on economic forecasting, is showing an increase in criticism, of the inaccuracy of forecasts, with major implications for economic, and fiscal policymaking. Forecasts are subject to the systemic uncertainty of human systems, considerable event-driven uncertainty, and show biases towards optimistic growth paths. The purpose of this study is to consider approaches to improve economic foresight.

This study describes the practice of economic foresight as evolving in two separate, non-overlapping branches, short-term economic forecasting, and long-term scenario analysis of development, the latter found in studies of climate change and sustainability. The unique case of Ireland is considered, a country that has experienced both steep growth and deep troughs, with uncertainty that has confounded forecasting. The challenges facing forecasts are discussed, with brief review of the drivers of growth, and of long-term economic scenarios in the global literature.

Economic forecasting seeks to manage uncertainty by improving the accuracy of quantitative point forecasts, and related models. Yet, systematic forecast failures remain, and the economy defies prediction, even in the near-term. In contrast, long-term scenario analysis eschews forecasts in favour of a set of plausible or possible alternative scenarios. Using alternative scenarios is a response to the irreducible uncertainty of complex systems, with sophisticated approaches employed to integrate qualitative and quantitative insights.

To support economic and fiscal policymaking, it is necessary support advancement in approaches to economic foresight, to improve handling of uncertainty and related risk.

While European Union Regulation (EC) 1466/97 mandates pursuit of improved accuracy, in short-term economic forecasts, there is now a case for implementing advanced foresight approaches, for improved analysis, and more robust decision-making.

Building economic resilience and adaptability, as part of a sustainable future, requires both long-term strategic planning, and short-term policy. A 21st century policymaking process can be better supported by analysis of alternative scenarios.

To the best of the authors’ knowledge, the article is original in considering the application of scenario foresight approaches, in economic forecasting. The study has value in improving the baseline forecast methods, that are fundamental to contemporary economics, and in bringing the field of economics into the heart of foresight.

The lack of a definite standard for determining the sample size in qualitative research leaves the research process to the initiative of the researcher, and this situation overshadows the scientificity of the research. The primary purpose of this research is to propose a model by questioning the problem of determining the sample size, which is one of the essential issues in qualitative research. The fuzzy logic model is proposed to determine the sample size in qualitative research.

Considering the structure of the problem in the present study, the proposed fuzzy logic model will benefit and contribute to the literature and practical applications. In this context, ten variables, namely scope of research, data quality, participant genuineness, duration of the interview, number of interviews, homogeneity, information strength, drilling ability, triangulation and research design, are used as inputs. A total of 20 different scenarios were created to demonstrate the applicability of the model proposed in the research and how the model works.

The authors reflected the results of each scenario in the table and showed the values for the sample size in qualitative studies in Table 4. The research results show that the proposed model's results are of a quality that will support the literature. The research findings show that it is possible to develop a model using the laws of fuzzy logic to determine the sample size in qualitative research.

The model developed in this research can contribute to the literature, and in any case, it can be argued that determining the sample volume is a much more effective and functional model than leaving it to the initiative of the researcher.