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Music has a priming effect on product selection. The purpose of this paper is to extend the current understanding on this issue using an experimental design incorporating behavioural and brainwave data.

An experiment with 40 participants was conducted to explore how and why wine tasting preferences would be primed by different genres of musical stimuli. Electroencephalographic measurement was adopted to measure participant brainwave activity in two experiments, each involving two rounds of wine tasting, and the treatment was administered between the two rounds.

Significant associations between the musical stimulus genre and participant change in wine selection were found, and the musical stimuli resulted in different brainwave activities because participant β and γ wave activities significantly differed in the first and second wine tasting rounds. Correlational analyses indicated that α, β and γ wave activities generated by the musical stimuli were significantly but negatively correlated with α wave activity. α wave activity in the musical stimulus phases was significantly negatively correlated with β wave activity in the second round of wine tasting, and the other associations were significant and positive.

This study highlighted the priming effect of musical stimuli in wine tasting. Empirical evidence derived from experimental research was analysed with behavioural and brainwave data. This study’s original contribution is that it explored wine tasting preferences from a neuromarketing perspective. The results of this study can provide empirical evidence on how to effectively use music in marketing strategies.

The purpose of this paper is to focus on three fundamental human-related behaviour factors associated with Lean Six Sigma (LSS) projects in German manufacturing small-and medium-sized enterprises (SMEs) on the readiness of people (managers and their staff) to commence LSS projects. These are core personal competence, strategic vision of the people and the organisational culture of the specific organisation.

Based on a thorough review of the academic literature a set of hypotheses were constructed to examine the level of association between people’s competence, vision and culture with LSS readiness within SMEs. This was done using correlation and regression analysis. Data collection were carried out in seven different German manufacturing SMEs involved in aerospace supply and agro-food manufacturing using a survey instrument.

It was found that there is a strong positive association between the core competence of people and organisational culture with readiness for commencing LSS in the manufacturing SMEs studied. The core values of people, education level and the vision of making continuous quality improvement were identified as key variables in promoting LSS readiness in these manufacturing SMEs. This study indicates that these “softer” variables can be essential to successful LSS implementation and need to be explored further before undertaking the process.

From the perspective of the implementers of LSS the results of this research could be of interest to different manufacturing SMEs intending to embark upon an LSS journey as it highlights the significance of human-related behavioural factors in the process. SME organisations may consider carrying out development or training with their managers and employees around personal and organisational values, addressing core competence and strengthening organisational culture in order to facilitate LSS readiness and enhance the prospect of its success.

It would appear that this LSS research has not been carried out within the German manufacturing SME context before and although discrete in nature has surfaced the “softer” variables of core competence of staff and organisational culture as important readiness issues to address when undertaking LSS. This integrated approach of human behavioural factors, organisational culture, LSS and manufacturing SMEs demonstrates the originality of the research.

The purpose of this paper is to investigate if country development indicators, i.e. gross domestic product per capita (GDPPC), literacy rate, internet penetration and urban population, influence the generation of e-waste on a global level. The moderation effect due to differences between countries in terms of absence or presence of e-waste policy and level of development is also checked.

This is an archival study that builds upon data from United Nations (UN), World Bank and Global E-waste Statistics Partnership. The authors did a path analysis comprising mediation and multigroup analyses to decipher the proposed rese arch model containing data from 172 countries.

The results indicate that GDPPC, literacy rate, internet penetration and urban population do not directly influence the generation of e-waste. However, higher internet penetration in developing countries leads to higher e-waste, while higher literacy rates in developed countries suppress e-waste generation. When it comes to e-waste policy, a higher urban population without a regulatory legal framework boosts higher e-waste. The authors observed that higher internet penetration leads to higher e-waste in the presence of e-waste policy as well.

This is the first study to include economic well-being indicators in elaborating e-waste generation, on a global scale. No previous study has observed differences between countries nested in e-waste policy and level of development.

This study intends to explore the connection between Facebook-based social media marketing (FSMM) and Facebook-based online purchase order (FOPO) for 20 popular online fashion retail brands across three South Asian countries: India, Pakistan and Bangladesh. FSMM was further divided into four components: Perceived trust (PT), Perceived informativeness (PInf), Perceived interactivity (PInt) and Perceived benefit (PB). 

The authors selected 20 popular Facebook-based online fashion brands involved in clothing and fashion accessories businesses in those three countries. Later, the authors purposively selected 114 region-based Facebook page administrators (admins) responsible for operating those brands' Facebook pages and taking Facebook-based online orders. The authors collected primary data from those admins as respondents through a structured survey instrument. The authors applied SPSS 25 for descriptive analysis and a covariance-based structural equation modeling (CB-SEM) (through AMOS 25) for testing the hypothesized relations.

Based on the valid responses and application of proper statistical measures, it was revealed that three FSMM components: PT, PInf and PB have significant positive relationships with FOPO, while PInt has an insignificant relationship with FOPO. 

South Asia is a growing business hub and the largest consumer market in terms of population. This study was conducted to identify the relationship between FSMM and FOPO in the three most prominent South Asian countries. As the first study was undertaken ever on customer perceptions of FSMM in a multi-country South Asian context, this paper is expected to be helpful for academics in conducting further empirical investigations on Facebook-based marketing as well as practitioners and policymakers in formulating and implementing Facebook-based marketing strategies.

In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.

The pandemic has reinforced the need for revamping the healthcare service delivery systems around the world to meet the increased challenges of modern-day illnesses. The use of medical cyber–physical system (MCPS) in the healthcare is one of the means of transforming the landscape of the traditional healthcare service delivery system. The purpose of this study is to critically examine the impact of MCPS on the quality of healthcare service delivery.

This paper uses an evidence-based approach, the authors have conducted a systematic literature review to study the impact of MCPS on healthcare service delivery. Fifty-four articles were thematically examined to study the impact of MCPS on eight characteristics of the healthcare service delivery proposed by the world health organisation.

The study proposes support that MCPS will positively impact (1) comprehensiveness, (2) accessibility, (3) coverage, (4) continuity, (5) quality, (6) person-centredness, (7) coordination, (8) accountability and (9) efficiency dimension of the healthcare service delivery. The study further draws nine propositions to support the impact of MCPS on the healthcare service delivery.

This study can be used by stakeholders as a guide point while using MCPS in healthcare service delivery systems. Besides, healthcare managers can use this study to understand the performance of their healthcare system. This study can further be used for designing effective strategies for deploying MCPS to be effective and efficient in each of the dimensions of healthcare service delivery.

The previous studies have focussed on technology aspects of MCPS and none of them critically analysed the impact on healthcare service delivery. This is the first literature review carried out to understand the impact of MCPS on the nine dimensions of healthcare service delivery proposed by WHO. This study provides improved thematic awareness of the resulting body of knowledge, allowing the field of MCPS and healthcare service delivery to progress in a more informed and multidisciplinary manner.

The purpose of this study is to identify the key data categories and characteristics defined by asset information requirements (AIR) and how this affects the development and maintenance of an asset information model (AIM) for a blockchain-based digital twin (DT).

A mixed-method approach involving qualitative and quantitative analysis was used to gather empirical data through semistructured interviews and a digital questionnaire survey with an emphasis on AIR for blockchain-based DTs from a data-driven predictive analytics perspective.

Based on the analysis of results three key data categories were identified, core data, static operation and maintenance (OM) data, and dynamic OM data, along with the data characteristics required to perform data-driven predictive analytics through artificial intelligence (AI) in a blockchain-based DT platform. The findings also include how the creation and maintenance of an AIM is affected in this context.

The key data categories and characteristics specified through AIR to support predictive data-driven analytics through AI in a blockchain-based DT will contribute to the development and maintenance of an AIM.

The research explores the process of defining, delivering and maintaining the AIM and the potential use of blockchain technology (BCT) as a facilitator for data trust, integrity and security.

The purpose of this paper is to investigate the potential integration of deep learning (DL) and digital twins (DT), referred to as (DDT), to facilitate Construction 4.0 through an exploratory analysis.

A mixed approach involving qualitative and quantitative analysis was applied to collect data from global industry experts via interviews, focus groups and a questionnaire survey, with an emphasis on the practicality and interoperability of DDT with decision-support capabilities for process optimization.

Based on the analysis of results, a conceptual model of the framework has been developed. The research findings validate that DL integrated DT model facilitating Construction 4.0 will incorporate cognitive abilities to detect complex and unpredictable actions and reasoning about dynamic process optimization strategies to support decision-making.

The DL integrated DT model will establish an interoperable functionality and develop typologies of models described for autonomous real-time interpretation and decision-making support of complex building systems development based on cognitive capabilities of DT.

The research explores how the technologies work collaboratively to integrate data from different environments in real-time through the interplay of the optimization and simulation during planning and construction. The framework model is a step for the next level of DT involving process automation and control towards Construction 4.0 to be implemented for different phases of the project lifecycle (design–planning–construction).

Blockchain technology is one of the emerging innovative technologies making waves globally, and it has been adjudged to have the capability to transform businesses. With the different capabilities of the technology, such as immutability of information and decentralisation of authority attributes, the built environment is slow in its adoption. This study aims to explore the barriers to the implementation of blockchain technology in the construction using a principal component analysis (PCA) approach.

This research took a post-positivist philosophical stance, which informed a quantitative research approach through a questionnaire survey. From the South African built environment and information technology sector, 79 respondents were drawn through a snowballing sampling technique. The built environment professionals include architect, construction project manager, construction manager, quantity surveyor and engineer. Retrieved data were screened and analysed by adopting the descriptive analysis and PCA while Cronbach alpha evaluated the reliability. Also, Kruskal–Wallis H non-parametric test was used to determine the differences in the opinion of the respondent groups.

The analysis revealed that all the identified barriers ranked above the average mean with lack of clarity, scalability risks and lack of skills or knowledge ranking top three. PCA clustered the identified barriers into three components: organisational barriers, social barriers and technological barriers.

This study was carried out in the Gauteng province of South Africa, leaving out other provinces due to accessibility, cost and time constraints.

Built environment organisations need to be kept abreast of the capabilities of blockchain technology as the major barrier observed was the lack of clarity of blockchain technology. Also, the technological barriers identified from this study need to be addressed by information technology experts to give consumers the desired value for money in implementing blockchain technology for the built environment.

The blockchain technology capabilities are incomparable to any other invention thus far. Therefore, it is very important that the numerous stakeholders in the built environment be made aware of the blockchain technology capabilities while formulating a solution to the identified barriers. This will aid its implementation in the built environment and help the industry measure up with its counterparts.

The purpose of this study is to focus on structured data provision and asset information model maintenance and develop a data provenance model on a blockchain-based digital twin smart and sustainable built environment (DT) for predictive asset management (PAM) in building facilities.

Qualitative research data were collected through a comprehensive scoping review of secondary sources. Additionally, primary data were gathered through interviews with industry specialists. The analysis of the data served as the basis for developing blockchain-based DT data provenance models and scenarios. A case study involving a conference room in an office building in Stockholm was conducted to assess the proposed data provenance model. The implementation utilized the Remix Ethereum platform and Sepolia testnet.

Based on the analysis of results, a data provenance model on blockchain-based DT which ensures the reliability and trustworthiness of data used in PAM processes was developed. This was achieved by providing a transparent and immutable record of data origin, ownership and lineage.

The proposed model enables decentralized applications (DApps) to publish real-time data obtained from dynamic operations and maintenance processes, enhancing the reliability and effectiveness of data for PAM.

The research presents a data provenance model on a blockchain-based DT, specifically tailored to PAM in building facilities. The proposed model enhances decision-making processes related to PAM by ensuring data reliability and trustworthiness and providing valuable insights for specialists and stakeholders interested in the application of blockchain technology in asset management and data provenance.