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Training activities organized by maker-spaces have become the most important channel through which makers acquire new external knowledge related to problem solving and thus contribute significantly to makers' innovation performance. Is it true that the more training activities in which makers participate, the better their innovation performance? Following the logic of process analyses, this paper empirically verifies the effects of the scope and frequency of makers' participation in training activities on their innovation performance.

Based on a survey of 134 makers in China, this paper employed the procedure recommended by Preacher and Hayes (2008) to empirically test the total effects, direct effects and indirect effects of the scope and frequency of participation in training activities on makers' innovation performance via their ability to exploit new knowledge.

The research findings show that the scope (frequency) of participation in training activities has a positive (negative) effect on makers' innovation performance via the mediating effect of the makers' ability to exploit new knowledge. Time pressure acts as a negative moderator.

This paper sheds light on a considerably overlooked research area by investigating the effects of the scope and frequency of makers' participation in training activities on their innovation performance. The authors further identify and empirically test the theoretical mechanism by considering the mediating effect of makers' ability to exploit new knowledge. The research findings also provide a managerial training activity arrangement strategy for makerspaces and government.

Research on makers and innovation has been equivocal regarding whether maker innovation is driven by internal motivation or external incentives. The motivation view favors the intrinsic motives of makers, whereas the incentive view supports external economic incentives. The authors combine both views to explore how innovation tournaments promote the product innovation outcomes of different creative and entrepreneurial makers, using economic incentives (money) or social incentives (love).

The authors interviewed 42 makers and collected a panel dataset of 29,823 makers from the largest digital maker community in China using a Python crawling program. The authors analyzed the data using multiple methods, including cluster analysis, discriminant analysis, factor analysis and negative binomial regression.

Compared with entrepreneurial makers, the product productivity of creative makers is inferior, but their product popularity is greater. The social incentive of innovation tournaments promotes the product productivity and popularity of creative makers compared with that of entrepreneurial makers, but the economic incentive is contradictory. In addition, social and economic incentives interact to generate inconsistent influences.

The study identifies creative and entrepreneurial makers and contributes to user innovation and innovation tournaments by integrating motivation and incentive views.

This study examines the effect of employees' perceptions of political connections on performance measurement systems (PMS) design choice and firm performance. In addition, this study explores the moderating effect of social networking, a very common and widely used factor by domestic and foreign multinational firms operating in China, and its joint effect with political connections or PMS design choice on firm performance. We collected survey responses from a sample of 110 managers from manufacturing firms in China. Our results reveal that highly politically connected managers use nonfinancial measures, leading to improved firm performance. Our results suggest that social networking interacts significantly with political connections, and nonfinancial and financial measures on firm performance. The theoretical and practical implications of our findings are discussed.

This study employed self-determination theory (SDT) and the “Motivational affordance–Psychological outcomes–Behavioral outcomes” framework to investigate the relationship between gamification features and knowledge-sharing behavior in online communities.

A theoretical model was tested with 281 Chinese users from an online social question and answer (Q&A) community. Partial least square structural equation modeling was applied to analyze the data.

The empirical results revealed that competence mediated the effects of immersion and achievement-related gamification features on knowledge sharing. Moreover, relatedness mediated the effects of immersion, achievement and social-related gamification features on knowledge sharing.

This study was conducted on a Chinese Q&A platform, and the results may not be generalizable to other cultures or service providers with different goals.

The study's findings indicate that gamification could serve as an effective toolkit for incentivizing and promoting knowledge sharing in online communities. The findings thus provide strategic insights for administrators of online communities seeking to leverage gamification designs to encourage user participation in knowledge-sharing activities.

Research on the role of gamification in promoting knowledge sharing has been limited in scope and has focused on tourism comment communities. Little evidence exists on the effect of gamification within social Q&A communities. Further, the finding of gamification's positive role in motivating knowledge sharing indicates the need for the knowledge-sharing field to focus on contextual factors.

Drawing on Foa and Foa’s elaboration of social exchange theory, the authors propose that buyers reciprocate perceived commitment on the part of the salesperson and supplier with commitment on their own parts because of strengthening of the relationship’s tacit governance mechanism – cooperative norms.

This study uses data from 155 buyers doing business with a multinational supplier. The buyers were from firms generating less than $100,000 in billings. The salesforce of the supplier firm sponsoring the research is responsible for account management and communicating directly with buyers.

Buyers, who feel that their suppliers are providing a symbolic, long-term, particularistic benefit (commitment), respond with their own strengthened commitment to the relationship; this mutualism is explained entirely by the mediating effect of the relationship’s cooperative norms. Where buyers perceive generally favorable treatment (satisfaction), without these three qualities, their own reciprocal commitment increases directly and cooperative norms play no part. The results also demonstrate the transition of buyer perceptions of the salesperson as they develop into beliefs about the selling firm as a whole.

Drawing on the “reciprocation-in-kind” principle, supplier firms seeking long-term, open-ended commitment from their customers should cultivate it via similarly long-term and open-ended commitments of their own. Attention must be given to the unwritten, often unstated “rules of the road” for business relationships, as these rules represent the mechanism through which investments in long-term, profitable partnerships bear fruit.

The conceptual model draws on and empirically tests Foa and Foa’s framework within social exchange theory to predict what form of buyer reciprocation will result, based on the characteristics of perceived seller-provided benefits. This study illustrates that the tacit governance structure of a B2B relationship – its cooperative norms – plays a critical role in the strength of a buyer’s commitment to its supplier.

The purpose of this paper is to introduce and investigate social brokers who belong to and connect multiple groups in a social network. This paper also reveals the differential effects of innovative and following social brokers on content diffusion in terms of adoption timing, speed and size.

The paper collected field data related to 69,086 users on the largest social network platform in China and analysed their adoption behaviours of 2,492 pieces of content.

The analysis reveals that social brokers encourage content diffusion and accelerate the speed of content adoption in a social network. Specifically, following social brokers play a greater role than innovative social brokers in accelerating the speed of content adoption and expanding the size of content adoption. However, in the early stage of content diffusion within the social network, innovative social brokers could predict the success of content adoption more effectively than following social brokers.

This research extends the current diffusion literature by introducing the social broker and examining the effect of social brokers on the process of content adoption.

The findings provide suggestions to marketing managers on how to improve the diffusion of marketing-related content, such as by seeding specific people – that is, social brokers – with content, so they can serve as content transmitters in marketing campaigns. In addition, the findings suggest that to optimise content adoption in a social network, managers should strategically target innovative social brokers or following social brokers at various stages of content seeding-based marketing campaigns.

To the best of the authors’ knowledge, this research is the first to test the effects of social brokers on content adoption and identify innovative and following social brokers. The findings enrich the literature on content marketing by providing new perspectives on social structures in social networks.

This paper aims to numerically investigate the magnetohydrodynamic (MHD) convection heat transfer of nanofluid inside a differentially heated enclosure with various fin morphologies.

The fluid flow within the cavity was governed by N-S equations while it within porous medium was solved by the non-Darcy model, called the Darcy–Forchheimer model based on representative element-averaging method. Empirical correlations from experimental data are used to evaluate the effective thermal conductivity and dynamic viscosity. Relevant governing parameters, including thermal Rayleigh number (10⁵-10⁷), Hartmann number (0-50), Darcy number (10⁻⁶-10⁻¹), thermal conductivity ratio of porous matrix (1-10³), nanoparticles volume fraction (0-0.04) and topology designs of porous fins, are sensitively varied to identify their effects and roles on the fluid flow and heat transfer. Particularly, heatlines are used to investigate the mechanism of heat transport.

Numerical results demonstrate that the predictions of average Nusselt number are augmented by using more porous fins with high permeability, and this effect becomes opposite in tiny Darcy numbers. Particularly, for high Darcy and Rayleigh numbers, the shortest fins could achieve the best performance of heat transfer. In addition, the prediction of average Nusselt number reduces with an increase in Hartmann numbers. An optimal nanoparticles concentration also exists to maximize heat transfer enhancement. Finally, numerical correlations for the average Nusselt number were proposed as functions of these governing parameters.

Present work could benefit the thermal design of electronic cooling and thermal carriers in nanofluid engineering.

Present work could benefit the thermal design of electronic cooling and thermal carriers in nanofluid engineering. In addition, optimum thermal removals could enhance the lifetime of electronics, therefore reducing the cost of energy and materials.

To the best knowledge of authors, there are not any studies considering the synergetic effects of porous fins on MHD convection of nanofluids. Present work could benefit the thermal design of electronic cooling and thermal carriers in nanofluid engineering.

Acquisitions are complex and ambiguous events fraught with information asymmetries emphasizing market failure before an acquisition or organizational failure during integration. While often treated in isolation, market and organization failure are intertwined in acquisitions as integration planning starts before a deal is closed. Effective integration begins with a deep understanding of the target to be able to share assets and knowledge. However, acquiring firms currently have limited solutions to address information asymmetries. Most remedies primarily aim at market failure using due diligence and external advisors, leaving information asymmetry due to organizational failure primarily unattended. The authors develop a typology that leverages informal and formal social ties to address information asymmetries across the acquisition process that jointly considers market and organizational failure. The typology of this study combines existing research to develop how social ties with stakeholders influence acquisitions and can increase their success.

This chapter presents the survey of selected linear and mixed integer programming multi-objective portfolio optimization. The definitions of selected percentile risk measures are presented. Some contrasts and similarities of the different types of portfolio formulations are drawn out. The survey of multi-criteria methods devoted to portfolio optimization such as weighting approach, lexicographic approach, and reference point method is also presented. This survey presents the nature of the multi-objective portfolio problems focuses on a compromise between the construction of objectives, constraints, and decision variables in a portfolio and the problem complexity of the implemented mathematical models. There is always a trade-off between computational time and the size of an input data, as well as the type of mathematical programming formulation with linear and/or mixed integer variables.

This paper aims to propose a method to quickly set the heating zone temperatures and conveyor speed of the reflow oven. This novel approach intensely eases the trial and error in reflow profiling and is especially helpful when reflowing thick printed circuit boards (PCBs) with bulky components. Machine learning (ML) models can reduce the time required for profiling from at least half a day of trial and error to just 1 h.

A highly compact computational fluid dynamics (CFD) model was used to simulate the reflow process, exhibiting an error rate of less than 1.5%. Validated models were used to generate data for training regression models. By leveraging a set of experiment results, the unknown input factors (i.e. the heat capacities of the bulkiest component and PCB) can be determined inversely. The trained Gaussian process regression models are then used to perform virtual reflow optimization while allowing a 4°C tolerance for peak temperatures. Upon ensuring that the profiles are inside the safe zone, the corresponding reflow recipes can be implemented to set up the reflow oven.

ML algorithms can be used to interpolate sparse data and provide speedy responses to simulate the reflow profile. This proposed approach can effectively address optimization problems involving multiple factors.

The methodology used in this study can considerably reduce labor costs and time consumption associated with reflow profiling, which presently relies heavily on individual experience and skill. With the user interface and regression models used in this approach, reflow profiles can be swiftly simulated, facilitating iterative experiments and numerical modeling with great effectiveness. Smart reflow profiling has the potential to enhance quality control and increase throughput.

In this study, the employment of the ultimate compact CFD model eliminates the constraint of components’ configuration, as effective heat capacities are able to determine the temperature profiles of the component and PCB. The temperature profiles generated by the regression models are time-sequenced and in the same format as the CFD results. This approach considerably reduces the cost associated with training data, which is often a major challenge in the development of ML models.