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The integration of Lean Six Sigma (LSS) and Industry 4.0 (I4.0) is in the nascent stage and promises to achieve new optimums in operational excellence. This study aims to empirically examine the enablers, barriers, benefits and application of I4.0 technologies in LSS and I4.0 integration.

A pilot survey was chosen as an appropriate methodology, as LSS and I4.0 integration is still budding. The survey targeted senior quality management professionals, quality managers, team leaders, LSS Black Belts and operations managers to collect the relevant research data. The questionnaire was sent to 200 respondents and received 53 valid responses.

This study reveals that “top management support” is an essential enabler for LSS and I4.0 integration. The most significant barrier was “poor understanding of data analysis” and “lack of top management support”. The findings further illustrated that LSS and I4.0 integration resulted in greater efficiency, lower operational costs, improved productivity, improved customer satisfaction and improved quality. Regarding I4.0 technology integration at different phases of LSS, the authors noticed that big data analytics and artificial intelligence (AI) are the most prominent technologies used in all phases of LSS implementation.

One of the limitations of this study is the sample size. LSS and I4.0 are emerging concepts; hence, obtaining a larger sample size is difficult. In addition, the study used non-parametric tests to analyse the data. Therefore, future studies should be conducted with large sample sizes across different continents and countries to understand differences in the key findings.

The outcomes of this study can be useful for organisational managers to understand the enablers and barriers before integrating LSS and I4.0 for adoption in their organisations. Secondly, it helps to convince top management and human resource personnel by providing a list of benefits of LSS and I4.0 integration. Finally, it can help decision-makers understand which I4.0 technologies can be used in different stages of LSS methodology.

LSS and I4.0 integration was studied at a conceptual level. This is the first empirical study targeted toward understanding the LSS and I4.0 integration. In addition, this study investigates the application of widely used I4.0 technologies in different phases of LSS.

The purpose of this paper is to explore the prospect of using neurophenomenology to understand, design and test phygital consumer experiences. It aims to clarify interpretivist approaches to consumer neuroscience, wherein theoretical models of individual phenomenology can be combined with modern neuroimaging techniques to detect and interpret the first-person accounts of phygital experiences.

The argument is conceptual in nature, building its position through synthesizing insights from phenomenology, phygital marketing, theoretical neuroscience and other related fields.

Ultimately, the paper presents the argument that interpretivist neuroscience in general, and neurophenomenology specifically, provides a valuable new perspective on phygital marketing experiences. In particular, we argue that the approach to studying first-personal experiences within the phygital domain can be significantly refined by adopting this perspective.

One of the primary goals of this paper is to stimulate a novel approach to interpretivist phygital research, and in doing so, provide a foundation by which the impact of phygital interventions can be empirically tested through neuroscience, and through which future research into this topic can be developed. As such, the success of such an approach is yet untested.

Phygital marketing is distinguished by its focus on the quality of subjective first-personal consumer experiences, but few papers to date have explored how neuroscience can be used as a tool for exploring these inner landscapes. This paper addresses this lacuna by providing a novel perspective on “interpretivist neuroscience” and proposes ways that current neuroscientific models can be used as a practical methodology for addressing these questions.

Exponential organizations (ExOs) are purpose-driven companies that leverage exponential technologies and exponential business practices to grow and scale rapidly, transform industries and create massive value and impact. In contrast, non-ExOs follow a linear approach to business and organizational strategy design and execution. This study aims to validate the hypothesis, based on financial metrics, that ExOs outperform their competitors and linear counterparts. Furthermore, it also brings a new understanding of the gap raised in the past eight years about how ExOs can achieve significantly better performance, measured with financial metrics.

For measuring how exponential an organization is, this study elaborated a completely new assessment tool called Exponential Quotient (ExQ). This study applied ExQ to the 100 largest US headquartered companies as ranked by Fortune magazine in 2014. Calculating the ExQ enabled this study to rank these Fortune 100 companies and identify the most and the least exponential firms. This study tracked these companies as to how they performed on different financial metrics over the eight years of 2014–2021 and analyzed the results.

Through the analysis, this study revealed that the top 10 ExOs have significantly outperformed their bottom 10 non-exponential peers, delivering 40x higher shareholder returns, 2.6x better revenue growth, 6.8x higher profitability and 11.7x better asset turnover. Furthermore, this study could identify commonalities and similarities between the two groups. This means that ExOs can thrive even in tough times and that accelerating technologies unlock abundance and allow every organization to become a disruptive innovator and stay ahead of the competition. These are novel results in the research focusing on the gap between exponential and traditional organizations.

Using the ExQ diagnostics tool, every organization can see how flexible, scalable and agile they are, which is the starting point for an exponential transformation program. Although this approach has already found its way into practice and is applied globally by thousands of organizations (startups, scaleups and incumbents), so far, the academic establishment is in its nascent phase. With this research, the authors wanted to extend this field of science. On the other hand, because of its novelty, no appropriate previous studies existed to compare the results.

The possible implications showed that there is a plannable way for significantly increasing an organization’s ExQ and advance it from a linear toward an exponential organizational model.

The results validated the robustness of the ExO framework and philosophy and shed light on the importance of exponential transformation – a proven method to increase an organization’s ExQ. This framework is not a “how to be successful” guide. Instead, it uncovered some of the previously unknown and universal mechanisms of scalability – which, in turbulent times, make companies successful (based on financial metrics). To the best of the authors’ knowledge, this study was among the first kind of in-depth analyses to validate the whole ExO model.

Emotional labour (EL) is a complex phenomenon that has received increasing attention in recent years due to its impact on employee’s well-being and job satisfaction. For a comprehensive understanding of the evolving field of EL, it is important to extract different research trends, new developments and research directions in this domain. The study aims to reveal 13 prominent research topics based on the topic modelling analysis.

Using latent Dirichlet allocation (LDA) method, topic modelling is done on 1,462 journal research papers published between 1999 and 2023, extracted from the Scopus database using the keyword “EL”.

The analysis identifies several emerging trends in EL research, including emotional regulation training and job redesign. Similarly, the topics like EL strategies, cultural differences and EL, EL in hospitality, organizational support and EL, EL and gender and psychological well-being of nursing workers are popular research topics in this domain.

The findings provide valuable insights into the current state of EL research and can provide a direction for future research as well as assist organizations to design practices aimed at improving working conditions for employees in various industries.

Topic modelling on emotional labor is done. The paper identifies specific topics or clusters related to emotional labor, quantifies these topics using topic modeling, adds empirical rigor, and allows for comparisons across different contexts.