Search results

This paper explains the mechanisms underlying the generation of two-way knowledge spillovers through the interaction of subsidiaries with differentiated local responsibilities and domestic firms.

The study is based on firm-level panel data from a census of Colombian manufacturing firms for the period 2003–2012. The estimation procedure involves two stages. In the first one, total factor productivity (TFP) of foreign and domestic firms is estimated. In a second step, we estimate conventional spillovers (from foreign-owned to local firms) and reverse spillovers (from local to foreign-owned firms) separately, using a random effect approach.

This study’s findings reveal that only locally creative subsidiaries enjoy positive and significant two-way knowledge spillover effects. The connectivity of subsidiaries to local and international networks is reinforced by reciprocal relationships among actors that enhance bidirectional knowledge flows, these being favored by the dynamics of clustering effects.

The paper contributes with new empirical evidence about the mechanism explaining how the technological heterogeneity of subsidiaries plays a determinant role in the generation of both knowledge flows from foreign to domestic firms and to the reverse, all integrated into the same framework.

This paper aims to investigate how purchasing organizations implement supplier diversity (SD) initiatives over time.

A multiple case study approach was conducted. Data were collected through in-depth interviews with participants from purchasing organizations, intermediary organizations and diverse suppliers.

The research suggests that the SD journey encompasses three different, but interrelated stages before full implementation is achieved: structuring, operation and adaptation. The findings also provide evidence that SD implementation in Brazil is highly influenced by the lack of a consistent knowledge base and the lack of legitimized intermediary organizations.

Using a temporal approach to understand how different practices suggested by the literature have been managed by practitioners over time, this study contributes to the understanding of the path to effective SD implementation and how intra- and interorganizational context influences this journey.

By identifying which practices should be adopted during different phases of SD implementation and proposing ways to overcome some of the inherent challenges, managers can better plan and allocate resources for the adoption of a successful SD initiative.

This research demonstrates how organizations can promote diversity and reduce social and economic inequalities by buying from diverse suppliers.

Using a temporal approach, the research empirically investigates how different purchasing organizations have implemented and managed the known practices and dealt with the challenges faced when trying to adopt SD.

Engaging suppliers in joint innovation can be an effective means for buyer firms to overcome internal resource/capability limitations. The purpose of this research is to investigate the impacts of cultural and trust congruences between the supplier and buyer firms on joint innovation. In addition, we examine the relationship commitment as an antecedent of cultural and trust congruences.

The study constructs a theoretical model based on social exchange theory (SET) and examines it using data from Chinese manufacturing firms.

The results suggest that cultural and trust congruences between suppliers and buyers positively influence joint processes and product innovations. Furthermore, we find that while normative relationship commitments of supplier firms promote cultural and trust congruences with buyers, instrumental relationship commitments only positively affect trust congruence.

This study enhances our understanding of social exchanges by adopting a dyadic view to examine the interconnectedness between relationship commitment, cultural and trust congruences, and joint innovation. These findings also offer practical managerial implications for managing collaborative innovation projects.

Against the background of the I50 paradigm, this paper queries in what ways blockchain and blockchain-based applications deployed in the smart city context facilitate the integration of the I50 paradigm in smart urban contexts.

A mixed methods approach is applied. First, by means of desk research and thematic literature review, a conceptual model integrating the I50 paradigm, smart city and blockchain-based solutions is built. Second, science mapping bibliometric analysis (SciMat) based on keywords’ co-occurrence is applied to a sample of 491 research articles to identify key domains of blockchain-based applications’ use in smart city. Third, a semi-systematic literature review complements insights gained through SciMat. Fourth, the findings are interpreted through the precepts of the conceptual model devised earlier.

The key blockchain-based applications in smart cities pertain to two domains, i.e. the foundational, service facilitation-oriented domain, including security (and safety), networks, computing, resource management and the service delivery-oriented domain, including mobility, energy and healthcare. Blockchain serves as the key building block for applications developed to deliver functions specific to each of the thus identified domains. A substantial layering of blockchain-based tools and applications is necessary to advance from the less to the more complex functional domains of the smart city.

At the conceptual level, the intricacies of the (making of the) I50 paradigm are discussed and a case for I50 – smart city – blockchain nexus is made. Easton’s input–output model as well as constructivism is referenced. At the empirical level, the key major domains of blockchain-based applications are discussed; those that bear the prospect of integrating the I50 paradigm in the smart city are highlighted. At the methodological level, a strategic move is made aimed at restoring the literature review’s role as subservient to the key line of exploration, to justify and ultimately support it, rather than to showcase the literature review as the ultimate purpose for itself.

Research has established that 38%, 56% and 66% of training is not transferred to work immediately, six months and 12 months after training, respectively. This has led scholars to advocate the continuous examination of factors that enhance training transfer to have a comprehensive understanding of the factors that enhance it. As a result, this study aims to examine transfer opportunity as a pretraining factor and its influence on assimilated training content (in-training factor); the influence of assimilated training content on motivation to transfer (post-training factor) and training transfer; the influence of motivation to transfer on training transfer; and the mediating role of motivation to transfer in the relationship between assimilated training content and training transfer.

A structural equation model is developed to test the five hypotheses formulated in this study using survey data obtained from 195 respondents who attended various training programs across different organizations. Following the assessment of the measurement model, the determination of the significance of the hypothesized paths is assessed based on the bias-corrected and accelerated confidence intervals obtained from the bootstrapping of 10,000 subsamples.

The findings of this study are that: transfer opportunity positively influences assimilated training content; assimilated training content positively influences motivation to transfer and training transfer; motivation to transfer positively influences training transfer; and motivation to transfer plays a complementary mediation role between assimilated training content and training transfer.

The nature of the work environment regarding the opportunity to transfer training influences trainees’ assimilation of the training content when they undergo training. Hence, organizations need to ensure that employees are always afforded the opportunity to transfer training content assimilated from previously attended training programs to assimilate the content of subsequent training programs. Furthermore, for training to culminate in training transfer, organizations and, more specifically, learning and development practitioners ought to pay attention to trainees’ assimilation of the content of training programs.

To the best of the authors’ knowledge, this is the first study to empirically consider transfer opportunity as a direct antecedent of assimilated training content. More so, it is one of few studies to empirically examine the influence of assimilated training content on training transfer.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

Digital technologies create myriad innovation opportunities and have inspired the establishment of many new start-ups in recent years. Despite the growing knowledge on digital entrepreneurship, few studies explore how start-ups exploit these opportunities to achieve entrepreneurial success. The purpose of this paper is to explore start-ups’ capabilities for successful delivery of digital artefacts in a cloud computing infrastructure.

Empirical data were collected during a qualitative case study of an established start-up in the Chinese market by interviewing 41 interviewees. Informed by the notion of dynamic capabilities and using the Gioia methodology, the case firm's life cycle was analysed in detail.

The study identifies start-ups’ ordinary and dynamic capabilities for successful development and delivery of digital services. The findings provide insights into a portfolio of start-ups’ capabilities, namely adaptation, networking, reengineering and refinement.

The study suggests that start-ups’ capabilities and underlying entrepreneurial actions determine the degree to which adoption of digital technologies create and transfer value to customers. The study offers specific insights into how start-ups successfully develop and deliver digital artefacts in a cloud infrastructure based on entrepreneurs' prior expertise, vision and accumulated experience.

Reverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance.

In this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis.

Nine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation.

The study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals.

Few studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs’ CE performance.

This study aims to analyse the intellectual property rights (INPR), foreign direct investment (FDI) inflows and technological exports of 32 developing and developed countries for the period of 2006–2020.

Diagnostic tests were used to confirm the panel least squares, fixed effect, random effect, feasible general least squares, dynamic ordinary least squares and fully modified ordinary least squares estimator results as well as to increase the robustness.

According to the findings for the developing countries, trademark, patent and industrial design applications, each had a significant positive long-run effect on FDI inflows. In addition, there was a significant positive long-run relationship between patent applications and medium- and high-technology exports. Meanwhile, trademark and industrial design applications had a significant negative long-term effect on medium- and high-technology exports. In developed countries, patent and industrial design applications each have a significant negative long-term on medium- and high-technology exports. Furthermore, patent and trademark applications each had a significant negative long-run effect on FDI inflows.

This study contributes significantly to the focus that host countries evaluate the technology gaps between domestic and foreign investors at different industry levels to select the best INPR rules and innovation process by increasing international cooperation. Furthermore, the host countries should follow the structure–conduct–performance paradigm based on analysis of the market structure, strategic firms and industrial dynamics systems.

To support the advancement of an underrepresented category of research in the field of entrepreneurial teams, this study proposes and tests a novel empirical model that connects two team emergent states, namely team entrepreneurial passion (TEP) and transactive memory systems (TMSs), and their influence on team performance.

The data were gathered using an online questionnaire distributed to undergraduate students who had formed entrepreneurial teams as part of a course assignment. Two methods were executed on the obtained data, namely partial least-square structural equation modelling (PLS-SEM) and necessary condition analysis (NCA).

The results uphold the hypothesised mediation role of TMSs between TEP and team performance. Of the two direct relations in the model, only the necessary conditions were present for the effect of TEP on TMSs.

The issue of the small sample size, a common feature in entrepreneurial team research, as discussed in the methodical section of the paper, is sidestepped with the use of PLS-SEM tools. Nonetheless, a larger sample size could have increased confidence in the results' validity. In addition, a longitudinal approach to data collection and analysis could have been used to augment that confidence further.

Three practical implications stem from the empirical findings. First, it lends support for implementing teaching approaches and task designs that are envisaged to improve team functioning in university classrooms. Making a business plan boosts students' desire to exploit the received knowledge and find a venture, so the teaching effort in entrepreneurship courses can have real-world consequences.

By testing the mediation model, new insights are made into the associations between team emerging states and, subsequently, team performance. In addition, this study responds to recent calls in the literature to incorporate NCA in an entrepreneurial setting.