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The purpose of this study is to develop a model of a starting situation for relationship initiation in turbulent business networks.

The study is designed as an extreme single case study that takes its point of departure in a company’s bankruptcy in the Swedish automotive industry.

This study illustrates how a new business relationship can start from a resource combination previously controlled by one actor (i.e. a single company) in a turbulent business network, thereby bringing nuances to the common understanding that new relationships start in stable business networks where resource combinations are developed between actors in established business relationships.

Previous studies have stated that the development of a mutual orientation between actors leads to the formation of a business relationship. The business relationship then leads to resource adaptations between the two companies. The developed model, however, illustrates that this pattern can be reversed in situations of turbulence. Hence, previously adapted resources might lead to the formations of a business relationship. Based on this observation, the authors argue that there are reasons to question if previous models of business relationship initiation and development in business networks are adequately equipped for analysis in turbulent business networks.

Polyurethane (PUR) foam parts are traditionally manufactured using metallic molds, an unsuitable approach for prototyping purposes. Thus, rapid tooling of disposable molds using fused filament fabrication (FFF) with polylactic acid (PLA) and glycol-modified polyethylene terephthalate (PETG) is proposed as an economical, simpler and faster solution compared to traditional metallic molds or three-dimensional (3D) printing with other difficult-to-print thermoplastics, which are prone to shrinkage and delamination (acrylonitrile butadiene styrene, polypropilene-PP) or high-cost due to both material and printing equipment expenses (PEEK, polyamides or polycarbonate-PC). The purpose of this study has been to evaluate the ease of release of PUR foam on these materials in combination with release agents to facilitate the mulding/demoulding process.

PETG, PLA and hardenable polylactic acid (PLA 3D870) have been evaluated as mold materials in combination with aqueous and solvent-based release agents within a full design of experiments by three consecutive molding/demolding cycles.

PLA 3D870 has shown the best demoldability. A mold expressly designed to manufacture a foam cushion has been printed and the prototyping has been successfully achieved. The demolding of the part has been easier using a solvent-based release agent, meanwhile the quality has been better when using a water-based one.

The combination of PLA 3D870 and FFF, along with solvent-free water-based release agents, presents a compelling low-cost and eco-friendly alternative to traditional metallic molds and other 3D printing thermoplastics. This innovative approach serves as a viable option for rapid tooling in PUR foam molding.

This paper aims to identify the combination of innovation determinants driving the creation of innovative products amongst market leaders and market followers in food and beverage (F&B) firms.

This research is based on the case study methodology by using two types of data sources: (1) semi-structured interviews with industry experts and (2) in-depth interviews with managers. In addition, a questionnaire adapted from prior research was used to consider market and firm types.

Suggesting an integrated theoretical framework based on firm-based factors and market-based factors, this study identified a combination of determinants significantly impacting innovative products in the market. Specifically, these determinants are competition intensity and innovation capability (a combination of research and development (R&D) investment and marketing capabilities). The study also examined how these determinants vary depending on whether the firms are market leaders or market followers.

This research provides practical insights for managers working in the F&B industry by using case studies and exploring the determinants of developing innovative products. In doing so, suitable strategies can be selected according to the market and firm situations.

The originality of the study is shown by focussing on how different combinations of market and firm factors could be applied in creating successful innovative products in the food sector.

The purpose of this study is to analyse the gender wage gap (GWG) in Spain adopting a labour market segmentation approach. Once we obtain the different labour segments (or idiosyncratic labour markets), we are able to decompose the GWG into its observed and unobserved heterogeneity components.

We use the data from the Continuous Sample of Working Lives for the year 2021 (matched employer–employee [EE] data). Contingency tables and clustering techniques are applied to employment data to identify idiosyncratic labour markets where men and/or women of different ages tend to match/associate with different sectors of activity and occupation groups. Once this “heatmap” of labour associations is known, we can analyse its hottest areas (the idiosyncratic labour markets) from the perspective of wage discrimination by gender (Oaxaca-Blinder model).

In Spain, in general, men are paid more than women, and this is not always justified by their respective attributes. Among our results, the fact stands out that women tend to move to those idiosyncratic markets (biclusters) where the GWG (in favour of men) is smaller.

It has not been possible to obtain remuneration data by job-placement, but an annual EE relationship is used. Future research should attempt to analyse the GWG across the wage distribution in the different idiosyncratic markets.

Our combination of methodologies can be adapted to other economies and variables and provides detailed information on the labour-matching process and gender wage discrimination in segmented labour markets.

Our contribution is very important for labour market policies, trying to reduce unfair inequalities.

The study of the GWG from a novel labour segmentation perspective can be interesting for other researchers, institutions and policy makers.

This paper explores the Adoption of Technological Innovation (ATI) in the healthcare industry. It investigates how the literature has evolved, and what are the emerging innovation dimensions in the healthcare industry adoption studies.

We followed a mixed-method approach combining bibliometric methods and topic modeling, with 57 papers being deeply analyzed.

Our results identify three latent topics. The first one is related to the digitalization in healthcare with a specific focus on the COVID-19 pandemic. The second one groups up the word combinations dealing with the research models and their constructs. The third one refers to the healthcare systems/professionals and their resistance to ATI.

The study’s sample selection focused on scientific journals included in the Academic Journal Guide and in the FT Research Rank. However, the paper identifies trends that offer managerial insights for stakeholders in the healthcare industry.

ATI has the potential to revolutionize the health service delivery system and to decentralize services traditionally provided in hospitals or medical centers. All this would contribute to a reduction in waiting lists and the provision of proximity services.

The originality of the paper lies in the combination of two methods: bibliometric analysis and topic modeling. This approach allowed us to understand the ATI evolutions in the healthcare industry.

This study investigates the impact of entrepreneurial orientation (EO) and knowledge management (KM) on firm performance (PERF), as well as the mediating role of KM in the EO–PERF (EO-PERF relationship). In particular, this study aims to explain the impact of KM on the relationship between the EO dimensions and PERF; dimensions are risk-taking (RT), innovativeness (IN) and proactiveness (PR).

This study uses structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA) methodologies to explore target relationships. The sample consists of 150 small furniture manufacturers operating in Poland (out of 1,480 in the population).

The study findings show that KM partially mediates the IN–PERF relationship. Furthermore, fsQCA reveals that KM accompanied by IN is a core condition that leads to PERF. Moreover, the absence of KM (accompanied by the absence of RT and IN) leads to the absence of PERF. In addition, the results show that all the variables examined (RT, IN, PR and KM) positively impact PERF.

This study explores the role of KM in the context of EO and its impact on PERF in the low-tech industry. The study uses simultaneously two methodologies that represent different approaches in the search for the expected relationships. The findings reveal that KM mediates the EO-PERF relationship.

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.

The purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.

This article focusses on the hybrid strategy, a simultaneous combination of cost leadership and differentiation strategy. The study aims to examine the impact of hybrid strategy on firm performance through its anticipated positive effects on process and product innovation. In addition, we study the moderating role of adaptive capacity in the direct relationships of hybrid strategy with process and product innovation.

Structural equation modelling was used to analyse 1,842 Spanish firms with fewer than 250 employees. We randomly selected small and medium-sized enterprises (SMEs) operating in Spain from the Spanish Central Business Directory (2021) database. The overall sample design was based on stratified sampling.

We found that hybrid strategy is positively related to firm performance and to process and product innovation. Additionally, in firms implementing hybrid strategies, process innovation fostered firm performance. Finally, adaptive capacity strengthened the relationships of hybrid strategy with process and product innovation. This sheds light on how and when hybrid strategy is most effective in fostering SME performance.

We highlight that SMEs need to establish strategies that use diverse resources and capabilities and not just generate competitive advantage using one strategy (cost leadership or differentiation strategy). This requires an agile and flexible systems and structures.

Our research provides novel results by proposing the adoption of hybrid strategies instead of pure strategies (cost leadership and differentiation strategy) as a way for SMEs to survive during crises. Unlike “stuck in the middle” strategies, our study demonstrates the importance of hybrid strategies in a comprehensive model that links them to innovation and firm performance, with adaptive capacity being a determining factor.

Worldwide academia is going through a major transformation because of Open Science and Recognition and Rewards movements that are linked to big societal challenges such as climate change, digitalization, growing inequality, migration, political instability, democracies under threat and combinations of these challenges. The transformations affect the human resource management (HRM) and talent management of universities. The main focus of this chapter is on collaborative innovation and the way universities participate in coalitions and strategic alliances on national and international levels. These platforms not only discuss the transformations and support the academic changes but also act as talent pools and talent exchange. This chapter provides an overview of the current state of affairs with respect to Open Science and Recognition and Rewards in academia. Next, a theoretical foundation is presented on the concepts of collaborative innovation, coopetition and HRM innovation in general. The leaders or leading organizations in the HRM innovation models often can’t make it happen on their own, in particular in highly institutionalized contexts such as academia. The legitimacy of transformations requires coalitions of the willing and therefore strategic alliances on different levels. The coalitions in academia can also contribute to academic talent management through sectoral transformations (see Recognition and Rewards) and through the way these coalitions operate.