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The organizational digital transformation (ODT) in companies presents small and medium-sized enterprises (SMEs) – who remain at the beginning of this transformation – with the challenge of offering digital services based on sensor technologies. Against this backdrop, the present paper identifies ways SMEs can enable digital servitization through sensor technology and defines the possible scope of the organizational transformation process.

Around 21 semi-structured interviews were conducted with experts from different hierarchical levels across the German manufacturing SME ecosystem. Using the Gioia methodology, fields of action were identified by focusing on influencing factors and opportunities for developing these digital services to offer them successfully in the future.

The complexity of existing sensor offerings must be mastered, and employees' (data) understanding of the technology has increased. Knowledge gaps, which mainly relate to technical and organizational capabilities, must be overcome. The potential of sensor technology was considered on an individual, technical and organizational level. To enable the successful implementation of service offerings based on sensor technology, all relevant stakeholders in the ecosystem must network to facilitate shared value creation. This requires standardized technical and procedural adaptations and is an essential prerequisite for data mining.

Based on this study, current problem areas were analyzed, and potentials that create opportunities for offering digital sensor services to manufacturing SMEs were identified. The identified influencing factors form a conceptual framework that supports SMEs' future development of such services in a structured manner.

Green practices are essential for sustainability. However, it is challenging due to the socioeconomic and environmental concerns. Similarly, after the induced SDG-12 and SDG-13 by United Nations, the pressure groups forced manufacturers to consider sustainability. Therefore, this research aims to examine the sustainability through multifaceted green functions in manufacturing is examined.

Data were collected from 293 supply chain professionals of manufacturers from a developing economy. Hypotheses were tested through a quantitative method using partial least squares-structural equation modeling with the help of SmartPLS version 4 to validate the measurement model.

The findings revealed that all six direct hypotheses were supported. However, out of four hypotheses of mediation, one was not supported. Besides, a sequential mediation of green supply chain environmental cooperation and green human resource management was supported. The findings illustrated that green supply chain practices positively influence all used variables.

This research provides practical insight to practitioners to implement green practices in their supply chain networks for social, economic and environmental sustainability and compliance with SDG-12 and SDG-13. The sustainability was validated in a higher-order construct (HOC) (formative), including sequential mediation in the model with the support of resource dependency theory. Therefore, this study adds substantial literature to the existing body of knowledge.

This research provides an interdisciplinary framework by adding knowledge to the Resource Dependency Theory to address Sustainable Development Goals-12 (SDGs) and SDG-13. Likewise, this research provides an extension towards the body of knowledge on the issue, which can be used in future research and critical examinations for cleaner and sustainable production. So far, in Pakistan, no research has looked at the function of these integrated variables in the manufacturing industry with a diligent focus on sustainability as it was validated in a higher-order construct (formative) with one sequential mediation, which makes this research unique.

Organizational resilience, defined by a firm’s speed in reaching a dynamic equilibrium after a shock and after the shocks are absorbed, and crisis management are critical in a global crisis. The concept of resilience is increasingly used in the economic press; nevertheless, few studies demonstrate empirically how firms became resilient and the lessons to be learned from it. Traditionally, the concept of resilience is approached as resistance in the face of a crisis. The authors go further by showing three-loop learning, which is part of a logic of innovation and regeneration. This study aims to examine how a business can regenerate itself by effectively managing the external threats and disruptions caused by a crisis. Also, this study deepens knowledge on learning process. The double-loop learning process is known in the literature as enabling firms to learn from unexpected events and react accordingly. The findings point out a third loop implying the co-invention of a new business model and a collective mindfulness of changes made.

Using longitudinal data, the authors investigate how the global crisis affects merger negotiations between two companies. This study analyzes the period of dialogue (negotiation) between the two entities with a view to carrying out a merger and then their withdrawal from the project during the pandemic, reshuffling the cards for each company. The negotiation period is not normally disclosed because of its highly confidential and strategic nature and it is therefore difficult for researchers to access merger operations at the negotiation stage. From this viewpoint, this case study was chosen because of the availability of generally inaccessible documentation.

This in-depth case study provides new insights on organizational resilience and the recovery capacity of a firm. The results underline four main triggers that a firm should develop in facing a major crisis: skills; credits; previous and historical relationships; and corporate culture. Recovery capacity depends on reactivity, flexibility, learning and regeneration. Finally, this study points out a three-loop learning experience that can be understood as a learning process in two steps to generate lasting and adaptive changes.

The limitations are those concerning a single case study.

This study highlights the ability to deal with unexpected events. First, this work identifies concrete items that can be perceived by managers as elements enabling a firm to develop resilience. Second, the results show main elements enabling this capacity as reactivity – both companies react quickly and effectively to disturbances to limit the impact on their performance; or flexibility – firms adapt their business model to deal with disruptions. Third, this work underlines a learning capacity process in three steps to recover capacity. This process stimulates creativity and innovation by the teams and stakeholders by placing them at the heart of the change.

This case provides a vivid illustration of firms’ adaptation to a rapidly evolving context because of a global crisis. Theoretical concepts and empirical findings from the literature are combined to present a single consistent picture.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

The purpose of this study is to identify and synthesise the best evidence on health-care leaders’ and professionals’ experiences and perceptions of compassionate leadership.

A mixed-methods systematic review was conducted in accordance with the Joanna Briggs Institute methodology for mixed-methods systematic reviews using a convergent integrated approach. A systematic search was done in January 2023 in PubMed, CINAHL, Scopus, Medic and MedNar databases. The results were reported based on Preferred Reporting Items for Systematic Reviews and Meta-analyses. The data was analysed using thematic analysis.

Ten studies were included in the review (five qualitative and five quantitative). The thematic analysis identified seven analytical themes as follows: treating professionals as individuals with an empathetic and understanding approach; building a culture for open and safe communication; being there for professionals; giving all-encompassing support; showing the way as a leader and as a strong professional; building circumstances for efficient work and better well-being; and growing into a compassionate leader.

Compassionate leadership can possibly address human resource-related challenges, such as health-care professionals’ burnout, turnover and the lack of patient safety. It should be taken into consideration by health-care leaders, their education and health-care organisations when developing their effectiveness.

This review synthesised the knowledge of compassionate leadership in health care and its benefits by providing seven core elements of health-care leaders’ and professionals’ experiences and perceptions of compassionate leadership.

The purpose of this study is to conduct a systematic literature review of the adoption of immersive marketing technology (IMT) in terms of strategic planning of its adoption, resource requirements and its implications and challenges.

This study categorizes and contextualizes qualitative approaches to evaluate the literature, with Scopus databases serving as the primary source of 90 selected articles in the areas of information technology, business and marketing strands. Theme analysis was carried out using thematic techniques and grounded approach principles to facilitate thematic coding and generate theme analysis.

The analysis was supported by the three concepts of business flexibility, agility and adaptability, which were drawn as a strategy for IMT adoption. The findings presented three main themes: proactive flexibility, responsive agility and reactive adaptability that enable business owner–managers to craft a strategy for IMT adoption.

The novel contribution of this study is the inclusion of key implications related to IMT as a starting point of the next level of innovative marketing for all academics, practitioners and business owner–managers.

The Astroworld concert tragedy is used as an example of crisis (mis)management and the potential utility of the 4R model. Although the 4R model has been implemented in high-risk emergency management situations, it is useful in the PR field because of its actionable approach, creating a way for practitioners to prepare for and manage crisis situations.

This is an analysis of the crisis that occurred at Astroworld, spanning preparation, day-of events, casualties and enduring reputational impact. The paper applies the 4R method to the Astroworld tragedy to show how it could have lessened or even prevented the tragedy. Finally, the SCCT model is used to explain why the official post-crisis statements were ineffective.

Social media has heightened the importance of a quick and effective organizational response to risk and crisis situations because poor responses can go viral quickly. However, social media also provides intelligence and crowd sourced information that can inform PR practitioners of emerging crisis scenarios. It is also an underutilized tool for two-way communication during crises.

The 4R approach is beneficial to general practitioners as it simplifies crisis best-practices, something essential for quick action. As our world changes and becomes less predictable, practitioners must have a clear plan to protect their organizations and the public surrounding them. This approach includes reduction, readiness, response and recovery, which are all essential in crisis communication.

The 4R method has not been explored or applied in the PR field. This paper highlights how the model has been utilized in the emergency management field and illustrates the way 4R can serve the PR field.

A better understanding of the characteristics and capabilities of women entrepreneurs can significantly improve their chances of success. Therefore, three studies were conducted for this exploratory paper. We have discovered the characteristics of entrepreneurial intelligence among female entrepreneurs through semi-structured interviews based on conventional content analysis. According to the second study, qualitative meta-synthesis was utilized to identify characteristics of women's entrepreneurial intelligence at the international level. As a third study, we examined the evolutionary relationships of entrepreneurs' intelligence components following the discovery and creation of opportunities.

The present paper was based on three studies. In the first study, 15 female entrepreneurs were interviewed using purposive sampling in the Guilan province of Iran to identify the characteristics of entrepreneurial intelligence at the national level. An inductive content analysis was performed on the data collected through interviews. Using Shannon entropy and qualitative validation, their validity was assessed. In the second study, using a qualitative meta-synthesis, the characteristics of women's entrepreneurial intelligence were identified. Then the results of these two studies were compared with each other. In the third study, according to the results obtained from the first and second studies, the emergence, priority and evolution of entrepreneurial intelligence components in two approaches to discovering and creating entrepreneurial opportunities were determined. For this purpose, interviews were conducted with 12 selected experts using the purposeful sampling method using the fuzzy total interpretive structural modeling (TISM) method.

In the first research, this article identified the components of entrepreneurial intelligence of women entrepreneurs in six categories: entrepreneurial insights, cognitive intelligence, social intelligence, intuitive intelligence, presumptuous intelligence and provocative intelligence. In the second study, the components of entrepreneurial intelligence were compared according to the study at the national level and international literature. Finally, in the third study, the evolution of the components of entrepreneurial intelligence was determined. In the first level, social intelligence, presumptuous intelligence and provocative intelligence are formed first and social intelligence and provocative intelligence have an interactive relationship. In the second level, entrepreneurial insight and cognitive intelligence appear, which, in addition to their interactive relationship, take precedence over the entrepreneur's intuitive intelligence in discovering entrepreneurial opportunities. With the evolution of the components of entrepreneurial intelligence in the opportunity creation approach, it is clear that intuitive intelligence is formed first at the first level and takes precedence. At the second level, there is cognitive intelligence is created. At the third level, motivational intelligence and finally, at the last level, entrepreneurial insight, social intelligence and bold intelligence.

This study has the potential to discover credible and robust approaches for further examining the contextualization of women's entrepreneurial intelligence at both national and international levels, thereby advancing new insights. By conceptualizing various components of entrepreneurial intelligence for the first time and exploring how contextual factors differ across nations and internationally for women's entrepreneurship, this paper challenges the assumption that the characteristics of women's entrepreneurial intelligence are uniform worldwide. It also depicts the evolution of the components of entrepreneurial intelligence.

The purpose of this paper is to model a process for moving toward sustainable ecosystem service decisions in a Coastal Biodiversity and discuss the directions of the process for decision-makers to apply in ocean ecosystem services.

After the development of theoretical approaches to understand their prospects for the future development of ecosystem services, the authors worked on a process for developing factors for sustainable decision-making. It uses the Delphi method to develop all the factors supported by six dimensions in two specific moments: deductive-inductive and inductive-deductive.

This process of modeling the factors expands the possibility of adaptive governance to make prior and subsequent decisions using factors related to dimensions, stakeholders and benefits, risks, opportunities and costs.

Considering the limitations, future studies could use another database to widen the view in terms of the studies, factors, dimensions and other additional information to maintain the evolution of this process in ocean ecosystem services decision-making. Another limitation arose in the number of projects and experts defining the factors. This may prevent the opportunity to have more impact in terms of future decisions if more sources are used in the market. In addition, time and the access to experts during this modeling process demonstrate a limitation, as does the time for feedback.

This set of factors developed for adaptive governance decision-making can be applied to develop a prior alignment of stakeholder interests with sustainable practices.

This set of factors developed with the intervention of experts reinforces the importance of sustainable collective decisions on ocean ecosystem services. This is a joint approach with participants in the NextOcean project, sponsored by the European Commissions (EC)’s Horizon 2020 program. An Earth Observation-based Consortia aims to create sustainable value for Space, Land and Oceans.

This modeling process generated dimensions and factors to support adaptive governance stakeholders in making sustainable decisions in a coastal biodiversity zone.

Cotton fibre lots are graded and selected for yarn spinning based on their quality value which is a function of certain fibre properties. Cotton grading and selection has created a domain of emerging interest among the researchers. Several researchers have addressed the said issue using a few exponents of multi-criteria decision-making (MCDM) technique. The purpose of this study is to demonstrate a cotton selection problem using a recently developed measurement of alternatives and ranking according to compromise solution (MARCOS) method which can handle almost any decision problem involving a finite number of alternatives and multiple conflicting decision criteria.

The MARCOS method of the MCDM technique was deployed in this study to rank 17 cotton fibre lots based on their quality values. Six apposite fibre properties, namely, fibre bundle strength, elongation, fineness, upper half mean length, uniformity index and short fibre content are considered as the six decision criteria assigning weights previously determined by an earlier researcher using analytic hierarchy process.

Among the 17 alternatives, C9 secured rank 1 (the best lot) with the highest utility function (0.704) and C7 occupied rank 17 (the worst lot) with the lowest utility function (0.596). Ranking given by MARCOS method showed high degree of congruence with the earlier approaches, as evidenced by high rank correlation coefficients (Rs > 0.814). During sensitivity analyses, no occurrence of rank reversal is observed. The correlations between the quality value-based ranking and the yarn tenacity-based rankings are better than many of the traditional methods. The results can be improved further by adopting other efficient method of weighting the criteria.

The properties of raw cotton have significant impact on the quality of final yarn. Compared to the traditional methods, MCDM is reported as the most viable solution in which fibre parameters are given their due importance while formulating a single index known as quality value. The present study demonstrates the application of a recently developed exponent of MCDM in the name of MARCOS for the first time to address a cotton fibre selection problem for textile spinning mills. The same approach can also be extended to solve other decision problems of the textile industry, in general.

Novelty of the present study lies in the fact that the MARCOS is a very recently developed MCDM method, and this is a maiden application of the MARCOS method in the domain of textile, in general, and cotton industry, in particular. The approach is very simple, highly effective and quite flexible in terms of number of alternatives and decision criteria, although highly robust and stable.