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Tactical capacity planning is crucial when hospitals must cope with substantial changes in patient requirements, as recently experienced during the Covid-19 pandemic. However, there is only little understanding of the nature of capacity limitations in a hospital, which is essential for effective tactical capacity planning.

We report a detailed analysis of capacity limitations at a Norwegian tertiary public hospital and conducted 22 in-depth interviews. The informants participated in capacity planning and decision-making during the Covid-19 pandemic. Data are clustered into categories of capacity limitations and a correspondence analysis provides additional insights.

Personnel and information were the most mentioned types of capacity limitations, and middle management and organizational functions providing specialized treatment felt most exposed to capacity limitations. Further analysis reveals that capacity limitations are dynamic and vary across hierarchical levels and organizational functions.

Future research on tactical capacity planning should take interdisciplinary patient pathways better into account as capacity limitations are dynamic and systematically different for organizational functions and hierarchical levels.

We argue that our study possesses common characteristics of tertiary public hospitals, including professional silos and fragmentation of responsibilities along patient pathways. Therefore, we recommend operations managers in hospitals to focus more on intra-organizational information flows to increase the agility of their organization.

Our detailed capacity limitation analysis at a tertiary public hospital in Norway during the Covid-19 pandemic provides novel insights into the nature of capacity limitations, which may enhance tactical capacity planning.

This research aims to conduct a literature review of Lean Six Sigma (LSS) in the Indian context and related research publications and apply bibliometric analysis and the author's visualization to map research trends in this area.

This article conducts a bibliometric analysis of LSS-related research in the Indian context using data gathered from Scopus and Web of Science databases from 2011 to 2022. The review provides information on LSS-related research in the Indian context and evaluates performance based on primary sources, authors, keywords, countries, affiliations, and documents. The analysis employs the Biblioshiny app and Bibliometrix R-tool for data analysis and scientific mapping.

The results of the bibliometric analysis indicate that the LSS culture has widely spread in India. The International Journal of Lean Six Sigma and Production Planning and Control were found to be the most productive sources for publishing LSS-related research articles. Antony J. was identified as the most active author in this field, contributing the most over the years. Among all organizations, NITs have conducted the most comprehensive research on LSS, indicating their significant investment of resources and efforts in studying this methodology and its applications in India. Additionally, the study examined the intellectual, social, and conceptual structures to identify implicit gaps and future research opportunities.

The findings of this study can inform academicians, researchers, practitioners, and policymakers about the state-of-the-art and the specifics of the most prolific studies. This study will facilitate their exploration of emerging research areas in LSS.

To the best of the authors knowledge, this is the first bibliometric analysis of LSS in the Indian context, providing an overview of relevant publications published between 2011 and 2022. This study analyzed 194 articles on LSS in India, which can help researchers and academics identify emerging research areas, suitable collaborators, and relevant journals for future publications.

The purpose of this study is to examine the role of artificial intelligence (AI) in transforming the healthcare sector, with a focus on how AI contributes to entrepreneurship and value creation. This study also aims to explore the potential of combining AI with other technologies, such as cloud computing, blockchain, IoMT, additive manufacturing and 5G, in the healthcare industry.

Exploratory qualitative methodology was chosen to analyze 22 case studies from the USA, EU, Asia and South America. The data source was public and specialized podcast platforms.

The findings show that combining technologies can create a competitive advantage for technology entrepreneurs and bring about transitions from simple consumer devices to actionable healthcare applications. The results of this research identified three main entrepreneurship areas: 1. Analytics, including staff reduction, patient prediction and decision support; 2. Security, including protection against cyberattacks and detection of atypical cases; 3. Performance optimization, which, in addition to reducing the time and costs of medical procedures, includes staff training, reducing capital costs and working with new markets.

This study demonstrates how AI can be used with other technologies to cocreate value in the healthcare industry. This study provides a conceptual framework, “AI facilitators – AI achievers,” based on the findings and offer several theoretical contributions to academic literature in technology entrepreneurship and technology management and industry recommendations for practical implication.

The aim of this study was to empirically examine how much traditional attributes and green attributes characterize products within design-oriented firms. Further, we explored how these attributes relate to the perceived level of innovation of the firms.

An exploratory research was carried out in 86 Italian manufacturing companies that are members of the Industrial Design Association. Using the questionnaire method, the entrepreneurs’ perceptions have been analyzed. Data have been treated with hierarchical cluster analysis.

The analysis shows that environmental sustainability is the least important attribute of a design product and four clusters of highly design-oriented firms differ by design-product attributes. Further, the least green firms are also the least innovative in terms of incremental and general innovation.

The small size of the sample and the provenance of firms from a single country imply limited generalizability, and further research on the topic is recommended.

Design-driven innovation based on traditional design attributes provides many competitive advantages to firms. However, given the growing concern about environmental challenges, investing in green attributes in design products allows for remaining competitive and more effective in innovation.

This study, for the first time, reveals the heterogeneity among design-oriented firms, particularly regarding the presence and assortment of traditional design attributes, as well as the incorporation of environmentally friendly attributes in their products. Moreover, the study uncovers the relationship between varying levels of green attributes in the offerings and the perception of the firm’s innovativeness.

This study aims to conduct a rigorous bibliometric analysis of impulse buying research trends and patterns, map the conceptual landscape of the field, identify significant themes and propose a roadmap for future studies in this domain.

This study used Snyder’s (2019) four-step systematic review approach. By using a specific search string, articles from the past 21 years (2003–2023) were retrieved from the Scopus and Web of Science databases. Performance analysis and science mapping were conducted using Biblioshiny to analyze the field’s knowledge base.

The volume and influence of impulse buying research have surged over the past two decades. The factorial analysis identified three main sub-themes within the impulse buying literature. In addition, Bradford’s law confirmed that the top five journals account for a significant portion of the relevant research. Despite the increased publications, author productivity diverges from Lotka’s law, indicating a few influential authors. This study provides a detailed conceptual map of the research landscape and proposes targeted future research questions by highlighting untapped opportunities.

This study offers insights for refining marketing strategies and highlights the importance of ethical considerations in marketing, especially during crises. It also strengthens the theoretical foundation of impulse buying by mapping core themes and identifying unexplored areas.

This study introduces an innovative approach by using factorial analysis with the multiple correspondence analysis technique to develop a detailed conceptual structure map of impulse buying research. It applies bibliometric laws such as Bradford’s law and Lotka’s law to explain the dispersion of research articles. By identifying core themes, this study charts a roadmap for future inquiry, addressing significant gaps and uncovering new research directions.

This paper aims to explore the perception of successful entrepreneurs and resource providers regarding the minimum operational time necessary to legitimize an enterprise as an established company, as well as the tactics used by the entrepreneurs to minimize the distrust of society during this initial period of the life cycle of the organization.

Data collection from two groups: (i) interviews with 62 entrepreneurs and founders of established companies; and (ii) a questionnaire completed by 77 sales managers of companies that provide businesses with resources (they practice business-to-business). Regarding analyses: (i) to analyze information, this study applied the content analysis technique; and (ii) for data, this study applied the one-sample t-test, mean comparison t-test, Pearson’s correlation test, chi-square test of association, Mann–Whitney U test and correspondence analysis technique.

In this study, a period of 42 months was identified for the legitimization of enterprises by society, in other words, the time necessary for enterprises to be recognized as an established company and a company of low risk by customers and resource providers. A set of managerial and behavioral actions practiced by successful entrepreneurs to face the difficulties they experience during the legitimization period was also identified.

In addition to establishing a period of 42 months for the legitimization of an enterprise in the eyes of society, a set of 15 strategies used by successful entrepreneurs was identified to combat the prejudice associated with the youthfulness of their companies. Of these, 12 are linked to managerial actions and 3 to the entrepreneur’s behavior. It should be highlighted that eight of these strategies are innovative and have yet to be addressed in the literature on mitigating risks associated with the liability of newness principle.

The authors discuss the legitimization of enterprises based on the perception of actors who play a fundamental role regarding entrepreneurial action: resource providers for companies, customers and successful entrepreneurs. This is a triangulation of sources, as well as a triangulation of collected data and qualitative and quantitative techniques, which sought to ensure the accuracy and reliability of the information that resulted from this analytical process.

Many organizations currently transition towards digitalized process design, execution, control, assurance and improvement, and the purpose of this research is to empirically demonstrate how data-based operational excellence techniques are useful in digitalized environments by means of the optimization of a robotic process automation deployment.

An interpretive mixed-method case study approach comprising both secondary Lean Six Sigma (LSS) project data together with participant-as-observer archival observations is applied. A case report, comprising per DMAIC phase (1) the objectives, (2) the main deliverables, (3) the results and (4) the key actions leading to achieving the presented results is presented.

Key findings comprise (1) the importance of understanding how to acquire and prepare large system generated data and (2) the need for better large system-generated database validation mechanisms. Finally (3) the importance of process contextual understanding of the LSS project lead is emphasized, together with (4) the need for LSS foundational curriculum developments in order to be effective in digitalized environments.

This study provides a rich prescriptive demonstration of LSS methodology implementation for RPA deployment improvement, and is one of the few empirical demonstrations of LSS based problem solving methodology in industry 4.0 contexts.

While the extant literature has explored issues related to the access, usage and availability of financial services, the ability of households to withstand financial adversities, particularly those living under economically vulnerable conditions, requires further attention. The paper presents a gendered analysis of financial resilience behaviour in South Africa.

Using a nationally representative sample of 4,880 households, this paper constructs a financial resilience behaviour index (FRBI) covering savings, credit, insurance, and retirement planning behaviours. The gendered effect of demographic characteristics on financial resilience is examined using the ordinary least square and seemingly unrelated regression techniques.

The results show that low levels of financial resilience were present across the sample with insurance observed to be the greatest driver of financial resilience, followed by retirement planning, savings and credit respectively. Furthermore, the analysis highlights that a gender gap in financial resilience exists as men are characterized with higher financial resilience behaviour compared to women. The results also suggest that employed women and women with higher levels of education are associated with greater financial resilience.

Based on these results, improving access to higher education and employment opportunities for women will enhance their financial resilience and contribute towards addressing SDG (5) on gender equality.

As far as the authors are aware, this paper presents the first empirical analysis of the gender gaps in socio-demographic characteristics that explain financial resilience in South Africa.

The purpose of this paper is to find out which research topics and methods in information science (IS) articles are used in other disciplines as indicated by citations.

The study analyzes citations to articles in IS published in 31 scholarly IS journals in 2015. The study employs content analysis of articles published in 2015 receiving citations from publication venues representing IS and other disciplines in the citation window 2015–2021. The unit of analysis is the article-citing discipline pair. The data set consists of 1178 IS articles cited altogether 25 K times through 5 K publication venues. Each citation is seen as a contribution to the citing document’s discipline by the cited article, which represents some IS subareas and methodologies, and the author team's disciplinary composition, which is inferred from the authors’ affiliations.

The results show that the citation profiles of disciplines vary depending on research topics, methods and author disciplines. Disciplines external to IS are typically cited in IS articles authored by scholars with the same background. Thus, the export of ideas from IS to other disciplines is evidently smaller than the earlier findings claim. IS should not be credited for contributions by other disciplines published in IS literature.

This study is the first to analyze which research topics and methods in the articles of IS are of use in other disciplines as indicated by citations.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.