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This study aims to investigate the influence of inventors’ abilities to acquire external knowledge, provide broad and professional knowledge and patenting output (i.e. different types of inventors) on the formation of structural holes.

The authors collected 59,798 patents applied for and granted in the USA by 33 of the largest firms worldwide in the pharmaceutical industry between 1975 and 2014. A random-effects tobit model was used to test the hypotheses.

The inventors’ ability to acquire external knowledge contributes to the formation of structural holes. While inventors’ ability to provide broad knowledge positively affects the formation of structural holes, their ability to provide professional knowledge works otherwise. In addition, key inventors and industrious inventors are more likely to form structural holes than talents.

The results identify individual factors that affect the formation of structural holes and improve the understanding of structural hole theory. This study is unique in that most scholars have studied the consequences of structural hole formation rather than their antecedents. Studies on the origin of structural holes neglect the effect of inventors’ knowledge abilities and patenting output. By addressing this gap, this study contributes to a more comprehensive theoretical understanding of structural holes. The results can guide managers in managing structural holes in accordance with inventors’ knowledge abilities and patenting outputs, which optimize the allocation of network resources.

Although research on how the downstream calculations of a patent’s profit potential influence invention renewal decisions is extensive, the impact of the upstream knowledge creation stages is overlooked. The purpose of this study is to address this theoretical vacuum by examining the intra-organizational configuration of knowledge networks and collaboration networks.

The data consist of 491 global pharmaceutical firms that patent in the USA. Drawing on patent records, the authors simultaneously construct intra-organizational knowledge networks and collaboration networks and identify network cohesion features (i.e. local and global). The authors employ panel fixed-effects models to test the hypotheses.

The results show that local knowledge cohesion and local social cohesion decrease invention renewals, while global knowledge cohesion and global social cohesion increase renewals. Moreover, the marginal effects of local and global social cohesion are stronger than those of local and global knowledge cohesion, respectively.

The hypotheses are tested using the pharmaceutical industry as a research setting, which limits the generalizability of our findings. In addition, potential formal and informal contingencies are not considered.

Despite its limitations, this study provides valuable implications. First, managers are cautioned against the adverse effects of local cohesion structures on invention renewal. Second, firms can dynamically adjust their local and global network configuration strategies to harmonize the generation of valuable inventions and the retention of good ideas.

Complementary to previous research that focused on inventions’ performance feedback, this study delves into upstream knowledge creation stages to understand invention renewals.

This paper aims to explore how psychological perceptions and family situations drive women into necessity- or opportunity-based female entrepreneurship (NBFE or OBFE) and the moderating role of gender equality.

This study adopts multilevel logistic regression analysis to examine relationships based on a sample of 6,843 women across eight developing countries drawn from the Global Entrepreneurship Monitor (GEM).

The findings suggest that capability and opportunity perceptions positively affect NBFE and OBFE. Family responsibility burden positively affects NBFE and has a U-shaped relationship with OBFE. Household income negatively affects NBFE but positively affects OBFE. Gender equality weakens the U-shaped relationship between family responsibility burden and OBFE but strengthens the positive relationship between capability perception and NBFE and between opportunity perception and NBFE.

The study highlights the need for targeted policies and support that consider the distinct antecedents and mechanisms of NBFE and OBFE, as well as the importance of promoting gender equality and entrepreneurial education to empower women in their entrepreneurial endeavors. A limitation of this study is the reliance on older data from the GEM, which may not fully capture the current dynamics of developing societies. While the study provides valuable insights, future research should incorporate more recent data to enhance the applicability of the results.

This study deepens the understanding of antecedents of NBFE and OBFE, breaking through the existing literature that neglects the heterogeneity of female entrepreneurship (FE).

The purpose of this paper is to explore the operating characteristics of gallium-based liquid metals (GLMs) by directly adding them as lubricants in real mechanical equipment.

This paper conducts an analysis of the rotor-bearing system under GLM lubrication using a constructed test rig, focusing on vibration signals, surface characteristics of the friction pair, contact resistance and temperature rise features.

The study reveals that GLM can effectively improve the lubrication condition of the tribo-pair, leading to a more stable vibration signal in the system. Surface analysis demonstrates that GLM can protect the sample surface from wear, and phase separation occurs during the experimental process. Test results of contact resistance indicate that, in addition to enhancing the interfacial conductivity, GLM also generates a fluid dynamic pressure effect. The high thermal conductivity and anti-wear effects of GLM can reduce the temperature rise of the tribo-pair, but precautions should be taken to prevent oxidation and the loss of its fluidity.

The overall operating characteristics of the rotor-bearing system under GLM lubrication were investigated to provide new ideas for the lubrication of the rotor-bearing system.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0067/

Although big data analytics managerial skills (BDAMS) offer opportunities for firms to foster organizational agility, existing studies present inconclusive indications of this impact, with an overlooking of the intermediate pathways involved. This study explored how BDAMS affect organizational agility by investigating the mediation effect of data-driven organizational learning (DDOL) and the moderating roles of technological and market turbulence.

This study employed mediation and moderated mediation analyses to test the hypotheses using data collected from listed Chinese firms. Furthermore, we performed a fuzzy set qualitative comparative analysis (fsQCA) as a supplementary approach to identify the configurations that lead to organizational agility.

This study shows that DDOL partially mediates the relationship between BDAMS and organizational agility. Besides, technological and market turbulence positively moderate the effect of DDOL on organizational agility and the mediation effect of DDOL. Our additional analyses also reveal several patterns of conditions that facilitate agility.

This study offers a comprehensive exploration of the relationship between BDAMS and organizational agility by verifying the mediating effect of DDOL and moderating effects of technological and market turbulence. In addition, the fsQCA results highlighted the combinatorial effects of key factors in this study, reinforcing and refining the moderated mediation results.

The rise of artificial intelligence (AI) and machine learning has largely promoted the emergence of “autonomous decision-making” (ADM). This paper aims to establish a personalized artificial intelligent housekeeper (AIH) that knows more about our hobbies, habits, personality traits, and shopping needs than ourselves and can replace us to do some habitual purchasing behavior.

We propose an AI decision-making method based on machine learning algorithm, a novel framework for personalized customer preference and purchase. First, the method uses interactive big data to predict a potential consumer’s decision possibility. Then, the method mines the correlation between consumer decision possibility and various factors affecting consumer behavior. Finally, the machine learning algorithm is used to estimate the consumer’s purchase decision according to the comprehensive influencing factors data of the target consumer.

The experimental results show that the method can predict the regular consumption behavior of consumers in advance and make accurate decision-making behavior. It can find correlations from a large amount of data to help predict many simple purchase decisions in our life, and become our AIH.

This study introduces a new approach that not only has the auxiliary decision-making function but also has the decision-making function. These findings contribute to the research on automated decision-making process of AI and on human–technology interaction by investigating how data attributes consumer purchase decision to AI.

This research aims to examine the factors that influence customers' product return intentions and proposes that YouTube product reviews can mitigate customers' desire to return a product.

The proposed theoretical research model and hypothesized relationship were investigated using a quantitative process. This study used 302 data from Indonesian young adult respondents to examine the structural model, which was analyzed using the SmartPLS 3.2 software package.

The results show that YouTube product reviews, product fit uncertainty and customer satisfaction are the key determinants of customers' product return intention. Furthermore, the results show that the credibility of YouTube product reviews has a major impact on customers' familiarity with a product, satisfaction and the likelihood of returning goods to sellers.

In the e-commerce industry, increasing the use of YouTube product reviews will help businesses eliminate unnecessary product returns. Sellers are also encouraged to collaborate with YouTube producers to review specific products, which can benefit companies by raising brand awareness and gaining customer feedback. Furthermore, YouTube online product reviews can help consumers avoid having an unpleasant shopping experience that causes emotional reactions and lowers satisfaction.

Most research has not considered antecedents in observing the product return phenomenon; this study observes a prerequisite of consumer product returns (i.e. information asymmetry and product familiarity) and investigates the relationships between YouTube product reviews, customer satisfaction and product return intention.

We phrase our analysis around the connectedness effects and portfolio allocation in the “Carbon-Energy-Green economy” system.

This paper utilizes the TVP-VAR method provided by Antonakakis et al. (2020) and Chatziantoniou et al. (2021), and portfolio back-testing models, including bivariate portfolios and multivariate portfolios.

Firstly, the connectedness within the “Carbon-Energy-Green economy” system is strong, and is mainly driven by short-term (weekly) connectedness. Notably, the COVID-19 pandemic leads to a vertical increase in the connectedness of this system. Secondly, in the “Carbon-Energy-Green economy” system, most of the sectors in the green economy stocks tend to be the transmitters of shocks to other markets (particularly the energy efficiency sector), while the carbon and energy markets are always the recipients of shocks from other markets (particularly the crude oil market). Thirdly, Green economy sector stocks have satisfactory hedging effects on the market risk of carbon and energy assets. Interestingly, hedging risks in relatively “dirty” assets requires more green economy stocks than in relatively “clean” assets. Finally, the results indicate that portfolios that include green economy stocks significantly outperform portfolios that do not contain green economy stocks, further demonstrating the crucial role of green economy stocks in this system.

Understanding the interactions and portfolio allocation in the “Carbon-Energy-Green economy” system, especially identifying the role of the green economy performance in this system, is important for investors and policymakers.

Users frequently target rival brands through direct criticism or indirect customer insults, yet the impact of such attacks on brand advocacy remains unexplored. The purpose of this study is to classify online attacks into brand-targeted attacks and consumer-targeted attacks and further investigate their differential impacts on brand advocacy and the underlying mechanism and a boundary condition of those impacts.

Three experimental studies using different types of brands (electronics, universities and sports footwear) are conducted to examine the effects of brand-related attack targets on brand advocacy.

This research shows that consumer-targeted attacks trigger higher brand advocacy through increasing perceived identity threat than brand-targeted attacks. Moreover, the effect of consumer-targeted attacks (versus brand-targeted attacks) on brand advocacy is mediated by perceived identity threat and mitigated when consumers’ identification with the attacked brand is strong.

The study’s findings yield practical applications for marketers and brand managers, assisting them in understanding consumers’ reactions to brand attacks. This study serves as a reference for firms to consider leveraging the association between brand identification and brand-related attack targets and uniting with loyal brand fans to manage online brand conflicts.

The present study extends prior literature on customer-brand relationships in the context of online attacks. Through investigating the impacts of brand-targeted and consumer-targeted attacks on brand advocacy, this research offers theoretical insights into consumers’ responses to online attacks with different targets.

With the development trend of China’s service-oriented manufacturing moving toward intelligence and personalization, the deep integration of manufacturing and service has become a synergistic challenge for enterprises.

An improved migratory bird optimization (IMBO) algorithm is proposed to solve the multiobjective FJSP model. First, this paper designs an integer encoding method based on job-machine. The algorithm adopts the greedy decoding method to obtain the optimal scheduling solution. Second, this paper combines three initialization rules to enhance the quality of the initial population. Third, three neighborhood search strategies are combined to improve the search capability and convergence of the solution space. Furthermore, the IMBO algorithm introduces the concepts of nondominated ranking and crowding degree to update the population better. Finally, the optimal solution is obtained after multiple iterations.

Through the simulation of 15 benchmark studies and a production example of a furniture enterprise, the IMBO algorithm is compared with three other algorithms: the improved particle swarm optimization algorithm, the global and local search with reinitialization-based genetic algorithm and the hybrid grey wolf optimization algorithm. The experiment results show the effectiveness of the IMBO algorithm in solving the multiobjective FJSP.

The study does not consider the influence of disturbance factors, such as emergency interventions and equipment failures, on scheduling in actual production processing. It is necessary to further study the dynamic FJSP problem.

The study proposes an IMBO algorithm to solve the multiobjective FJSP problem. It also uses three initialization rules to broaden the range of the solution space. The study applies multiple crossover strategies to avoid the algorithm falling into local optimality.