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This paper aims to examine the effect of R&D laboratories on the perceived performance of MNE subsidiaries during recession.

Employing resource-based view and knowledge-based theory, the authors investigate a unique sample of 171 technologically heterogenous foreign MNE subsidiaries located in Greece over the period of recession 2009–2016. The sample subsidiaries operate different types of R&D laboratories.

The authors find that MNE subsidiaries with advanced R&D laboratories such as locally integrated laboratories (LILs) and internationally interdependent laboratories (IILs) perform better in recession than subsidiaries with support laboratories (SLs) or subsidiaries without R&D laboratories. Overall, the authors find an asymmetric performance contribution of R&D laboratories at subsidiary level.

The study provides useful insights into the environmentally derived “knowledge-based - performance” context, so filling an important research gap, since little is known about the performance impact of the input-side of technological activity at MNE subsidiary level, especially as regards R&D facilities/infrastructure. Based on the findings the authors identify important managerial implications.

This study aims to identify demographic characteristics, personal attributes and attitudes and social support factors that adversely or favourably affect the likelihood of career persistence amongst women workers of the Indian IT sector.

The research, grounded in the social cognitive career theory, analyses primary data collected from 850 women working in IT via a survey. Based on an original definition of career persistence, the sample was segregated into 427 persistent and 423 non-persistent women. Logistic regression was performed to test for the effect of various determinants on the likelihood of women being career persistent versus non-persistent.

Being married, having children, as well as high levels of belief in gender disadvantage and work–family conflict lowered the likelihood of career persistence amongst women. While being a manager, possessing high career identity, high occupational culture fit, positive psychological capital and family support boost the likelihood.

The study examines women's actual continuance in an IT career vis-à-vis exit from the workforce/IT field, rather than women's stated intent to persist/quit as previously investigated. It uses logistic regression to identify both hurdles and aids on the path of women's career persistence. The findings can help recognize women more likely to struggle, thus be a first step in targeted organizational interventions to plug a leaky talent pipeline.

This study aims to investigation of the guar gum-manganese dioxide (GG/MnO₂) nanocomposite (NC) synthesized using an environment-friendly method and the degradation of reactive yellow (RY 145) dye in the UV system.

Characterization of the GG/MnO₂ NCs were conducted using field emission scanning electron microscopy, X-ray diffraction and Fourier-transform infrared spectroscopy. Experiments were conducted using a 1 L glass reactor coupled with Ultraviolet (UV-C) blue light bulb of wavelength 250 nm and power of 8 W.

The NC (2.25 g/L) displayed high RY 145 dye degradation (81%) with 10 mg/L of concentration at pH 3. The coefficient of determination (R² 0.99) also depicted that the model fits the experimental data. The analysis of variance (ANOVA) showed that the F-values of 464.75, 276.04 and 5.15 are related to the dose of GG/MnO₂ NCs, initial concentration of RY 145 dye and solution pH, respectively.

The GG/MnO₂ NCs followed by photo oxidation process (UV-process) could be used to degrade the RY 145 dye from synthetic wastewater.

There are two main innovations. One is that the novel process is performed successfully for RY 145 dye degradation. The other is that the optimized conditions are obtained by Box–Behnken design. Also, the effects of different variables on the RY 145 dye removal efficiency were investigated.

The purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.

This study uses a best–worst discrete choice experiment (BWDCE) and compares the outcome with that of the purchase intention scale, an established probabilistic measure of purchase intention. The total sample consists of 409 fans of three soccer teams sponsored by three different competing brands: Nike, Adidas and Puma.

With sports sponsorship, fans were willing to pay more for the sponsor’s product, with the sponsoring brand obtaining the highest market share. Prominent brands generally performed better than less prominent brands. The best–worst scaling method was also 35% more accurate in predicting brand choice than a purchase intention scale.

Future research could use the same method to study other types of sponsors, such as title sponsors or other product categories.

Sponsorship managers can use this methodology to assess the return on investment in sponsorship engagement.

Prior sponsorship studies on brand equity tend to ignore market share or fans’ willingness to pay a price premium for a sponsor’s goods and services. However, these two measures are crucial in assessing the effectiveness of sponsorship. This study demonstrates how to conduct such an assessment using the BWDCE method. It provides a clearer picture of sponsorship in terms of its economic value, which is more managerially useful.

This study aims to examine consumers’ responses to crowdsourcing campaigns in the request initiation stage using the signaling theory from economics. The purpose of the research is threefold. First, it provides a comprehensive classification of various task types within crowdsourcing. Second, it conceptualizes crowdsourcing announcements as signals of customer orientation and empirically tests the differential effects of the two most common crowdsourcing task types (product- and communication-related) on customer orientation perceptions. Third, it illuminates the downstream behavioral consequences of crowdsourcing campaign announcements.

The authors conducted secondary data analysis of 883 crowdsourcing campaigns (pilot study) to provide evidence on the differential effects of crowdsourcing task types. In addition, four laboratory experiments were conducted to test the theoretical arguments. To test the main effect of crowdsourcing task types, Study 1A (N = 252 MTurk workers) used a one-factor (product- vs communication-related crowdsourcing vs control) between-subject design, whereas Study 1B (N = 171 undergraduate students) used a 2 (task type: product- vs communication-related) by 2 (product category: restaurant vs fashion) between-subject design. Study 2 (N = 93 MTurk workers) explored the underlying mechanism using a one-factor (product- vs communication-related) between-subject design. Study 3 (N = 375 MTurk workers) investigated the boundary condition for the effect of task type with a 2 (task type: product- vs communication-related) by 3 (company credibility: low vs neutral vs high) between-subject design.

The pilot study provides evidence for the conceptualized typology and the differential effects of crowdsourcing task types. Study 1A reveals that product-related crowdsourcing tends to have a more substantial impact than communication-related crowdsourcing on how customer-oriented consumers perceive a company. Study 1B validates the results of Study 1A in a different product category and population sample. Study 2 shows that the differential customer-orientation effect is mediated by the perceived cost of implementing the crowdsourcing outcome and unravels the differences in consumers’ purchase and campaign participation intentions depending on task type. Study 3 highlights that the customer-orientation effect attenuates as company credibility increases.

This research contributes to the crowdsourcing literature by categorizing the various types of crowdsourcing campaigns companies undertake and revealing the differential impact of the different types of crowdsourcing campaigns on consumers’ perceptions and behavioral intentions. In doing so, this research converges two lines of consumer research on crowdsourcing, i.e. product- and communication-related crowdsourcing. The findings add to the debate over the returns from research and development (R&D) versus advertising and extend it from marketing strategy to crowdsourcing literature.

The findings highlight the importance of choosing specific task types for crowdsourcing and lead to practical recommendations on designing crowdsourcing campaigns to maximize their benefits to crowdsourcing brands.

To the best of the authors’ knowledge, this is the first study that differentiates crowdsourcing task types and compares their effectiveness from a consumer perspective.

This study aims to examine how three types of information technology (IT) capability (supplier technological capability, customer technology-sensing capability and relatedness of IT infrastructure) facilitate knowledge acquisition by the customer when the supplier is dominant in the supplier-customer relationship.

The unit of analysis was project. The authors designed two different questionnaires that were responded by the project manager of an enterprise resource planning (ERP) software supplier and the contact person of the customer organization in the same project, respectively. The two questionnaires were matched by means of project name. The final sample included a total of 136 projects. The authors used ordinary least squares to test the research hypotheses.

The authors found that supplier power advantage negatively influenced knowledge acquisition by the customer. The three types of IT capability did not have direct impacts on knowledge acquisition. The moderating effect of customer technology-sensing capability was not significant either. However, supplier technological capability and relatedness of IT infrastructure attenuated the negative effect of supplier power advantage on knowledge acquisition, indicating that both factors promoted knowledge acquisition.

Knowledge acquisition is important for the success of software implementation in the supplier-customer relationship. There is limited evidence in the literature on how to apply externally oriented IT capability to enhance knowledge management, improve knowledge acquisition and manage the business relationship that is typically dominated by the software supplier. The authors provide evidence to examine related issues.

The purpose of the study is applying and comparing models that predict optimal time for new product exit based on its demand pattern and survivability. This is to decide whether or not to continue investing in new product development (NPD).

The study investigates the optimal time for new product exit within the hi-tech sector by applying three models: the dynamic learning demand model (DLDM), the generalized Bass model (GBM) and the hazard model (HM). Further, for inter- and intra-model comparison, the authors conducted a simulation, considering Weiner and exponential price functions to enhance generalizability.

While higher price volatility signifies an unstable technology, greater investment into research and development (R&D) and marketing results in higher product adoption rates. Imitators have a more prominent role than innovators in determining the longevity of hi-tech products.

The study conducts a comparison of three different models considering time-varying parameters. There are four scenarios, considering variations in advertising intensity and content, word-of-mouth (WOM) effect, price volatility effect and sunk cost effect.

This paper aims to map the evolution of hydrogen-based technologies (HBTs) by examining the patenting activity associated to these technlogies from 1930 to 2020. In doing so, the study provides a novel perspective on the development of HBTs and offers implications for managers and policymakers.

We collected patent data at the level of patent families (PFs). Our sample includes 317,089 PFs related to hydrogen production and 62,496 PFs to hydrogen storage. We examined PF data to delineate the state of the art and major technical advancements of HBTs.

Our analysis provides evidence of an increasing patenting activity in the area of HBTs, hence suggesting relatively high levels of expectations on the economic potential of these technologies. US and Japan hold the largest proportion of PFs related to HBTs (about 60%), while European applicants hold the highest proportion of highly cited PFs (about 60%). While firms represent the applicant with the highest share of PFs, our analysis reveals that firms holding HBT PFs are primarily from the chemical sector.

While our analysis is limited to examining patent data which capture some aspects of the innovation activity around HBTs (namelly, patented inventions), our study enriches existing literature by performinng a patent analysis on a much larger sample of data when compared to previous studies.

Two main implications emerge from our study. Firstly, there seems to be an urgent need to support the emergence of a dominant design so as to facilitate the consolidation and diffusion of the HBTs, hence the transition to a more sustainable energy production. Secondly, the majority of HBT PFs are held by a small number of countries. This, in turn, suggests opportunities to develop cross-country cooperation (e.g. international agreements, research and technology offices) to support the development and adoption of HBTs globally.

Considering the results obtained in this study, from a social point of view, the attention that organizations have paid to hydrogen related technologies is evident. This suggests that the development HBTs can function as a social enabler for a sustianable energy transition.

Extant research has focused on the individual components of the hydrogen chain. As a result, we lack a comprehensive understanding of the progress made in the area of HBTs. To address this gap, this study examined HBTs by focusing on both production and storage technologies since their initial developments, hence adopting an observation period of about 70 years.

Information Systems (IS) research has built up a considerable understanding of digital platform ecosystems, while policymakers worldwide are aiming to introduce platform regulations that seek to erode fundamental mechanisms of digital platforms. This viewpoint article provides an introduction to how platform regulation affects our current understanding of digital platform ecosystems and suggests opportunities for future research.

A detailed analysis of the effects of the European Union (EU) Digital Markets Act (DMA) on current findings of organizational, technical and economic IS platform research.

Government regulations of digital platforms such as the DMA likely affect the central mode of operation of platforms in the scope of the regulation. The authors preconceive a major impact on platform openness, governance, steering the platform supply-side, modularity, nestedness, network effects, pricing and single-/multi-homing. In addition, the authors present opportunities for future research in each of these IS platform research streams.

Landmark regulations implemented in the past, such as the General Data Protection Regulation (GDPR), caused paradigm changes that fertilized research opportunities in IS and beyond. This viewpoint article aims to nudge studies that examine the changed mode of operation of platforms following platform regulation.

Academics and practitioners have long acknowledged the potential of multiagent systems (MAS) to automate and autonomize decision-making in logistics and supply chain networks. Despite the manifold promises of MAS, industry adoption is lagging behind, and the exact benefits of these systems remain unclear. This study aims to fill this knowledge gap by analyzing 11 specific MAS use cases, highlighting their benefits, clarifying how they can help enhance logistics network resilience and identifying existing barriers.

A three-stage Delphi study was conducted with 18 industry experts. In the first round, these experts identified 11 use cases of MAS and their potential benefits, as well as any barriers that could hinder their adoption. In the second round, they assessed the identified use cases with regard to their potential to enhance logistics network resilience and improve organizational productivity. Furthermore, they estimated the complexity of MAS implementation. In the third round, the experts reassessed their evaluations in light of the evaluations of the other study participants.

This study proposes 11 specific MAS use cases and illustrates their potential for increasing logistics network resilience and enhancing organizational performance due to autonomous decision-making in informational processes. Furthermore, this study discusses important barriers for MAS, such as lack of standardization, insufficient technological maturity, soaring costs, complex change management and a lack of existing use cases. From a theoretical perspective, it is shown how MAS can contribute to resilience research in supply chain management.

The identification and assessment of diverse MAS use cases informs managers about the potential of this technology and the barriers that need to be overcome.

This study fills a gap in the literature by providing a thorough and up-to-date assessment of the potential of MAS for logistics and supply chain management. To the best of the authors’ knowledge, this is the first study to investigate the relevance of MAS for logistics network resilience using the Delphi method.