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The proliferation of ride-hailing businesses brings significant considerations for improving the driver's operational performance. Informed by the literature on sharing economy, general deterrence theory and protection motivation theory this research investigates the behavioral factors impacting ride-hailing drivers' operational performance.

The authors empirically test the antecedents impacting a ride-hailing driver's operational performance using an online survey dataset comprising 513 ride-hailing drivers working for Uber and Lyft in the United States.

Ride-hailing drivers' intention to comply with the ride-hailing company guidelines results in better operational performance for the driver. Moreover, drivers believe that ride-hailing companies have effective penalties to deter drivers from violating company guidelines. However, drivers also believe that the chances of being caught while ignoring the company guidelines are low.

The results of this research support the decision-making processes of ride-hailing company managers and offer insights on how managers can enhance the operational performance of their drivers.

This study provides unique contributions to emerging research at the intersection of peer-to-peer asset sharing, behavioral studies and technology management. This research is one of the first to explore the role of behavioral factors such as coping mechanisms on the operational performance of sharing economy workers.

This paper aims to examine how interfirm transactional and relational assets drive firm performance (FP) in digitally integrated supply chains.

The authors combine the Transaction Cost Economics (TCE) and Relational Exchange Theory (RET) frameworks to hypothesize that FP will be a function of Asset Specificity (AS), Digital Technology Usage (DTU) and Collaborative Information Sharing (CIS). In addition, the authors hypothesize that Supply Chain Integration (SCI) will partially mediate the effect of DTU and fully mediate the impact of AS and CIS on FP. A cross-sectional survey of supply chain managers is used to test the hypotheses.

Findings indicate that specific investments in digitally integrated supply chains would increase FP. In addition, SCI fully mediates the relationships between AS and FP and CIS and FP, while SCI partially mediates the influence of DTU on FP.

Managers could strategically engage in the technologies that effectively fit within the firm’s supply chain strategies and seek to develop a pragmatic expertise that enables the effective use of technology in a comprehensive setting.

The study enriches the extant literature by incorporating TCE and RET as contradictory viewpoints on AS and investigating how transactional and relational assets affect FP in digitally integrated supply chains.

The purpose of this research is to investigate the contingent adoption of Additive Manufacturing (AM) and propose a typology to evaluate its adoption viability within a firm's supply chain.

By conducting semi-structured interviews of practitioners with deep knowledge of AM and supply chains from diverse industries, this research explores the contingent factors influencing AM adoption and their interaction.

While the AM literature is growing, there is a lack of research investigating how contingent factors influence AM adoption. By reviewing the extant literature on the benefits and barriers of AM, we explain the underlying contingencies that enact them. Further, we use an exploratory approach to validate and uncover underexplored contingent factors that influence AM adoption and group them into technological, organizational and strategic factors. By anchoring to a selected set of contingent factors, a typological framework is developed to explain when and how AM is a viable option.

This study focuses on specific industries such as automotive, machine manufacturing, aerospace and defense. Scholars are encouraged to explore the contextual factors affecting AM adoption in particular industries to expand our findings. The authors also acknowledge that the robustness of their framework can be enhanced by integrating the remaining contingent factors.

The developed typological framework provides a pathway for practitioners to see how and when AM can be useful in their supply chains.

This is the first paper in the supply chain management literature to synthesize contingent factors and identify some overlooked factors for AM adoption. The research is also unique in explaining the interaction among selected factors to provide a typological framework for AM adoption. This research provides novel insights for managers to understand when and where to adopt AM and the key contingent factors involved in AM adoption.

The purpose of this study is to better understand the effectiveness of buyers’ defensive measures to thwart bid protests in government procurements.

A sample of 240 sourcing professionals concerning government source selections is used to analyze a logistic regression model exploring 6 antecedents of bid protests.

This research implicates the importance of oral presentations of offers, the type of value procured (i.e. services), protest experience, the quantity of document revisions, transaction costs and cost reimbursement contracts in receiving a bid protest.

To the best of the authors’ knowledge, this research is the first to explore sourcing strategy decisions that can contribute to the receipt of a bid protest. It adds clarity to an understudied market of business – the public sector.

The purpose of this paper is to conduct an exploratory study that will assist supply chain firms in the development of partner satisfaction, flexibility, and supply chain performance. The authors examine how the interaction of information exchange, partner interaction, knowledge sharing and flexibility as mediated through partner satisfaction effectuates firm performance. The goal of this research is to answer the supply chain managers’ need to better understand where to invest their time and effort to get improved firm performance.

The model was tested with panel data from 105 experienced, US-based supply chain managers. Structural equation modeling using partial least squares approach was utilized to conduct the analysis.

The results provide crucial evidence that simple information exchange among supply chain partners does not result in improvements in firm performance or partner satisfaction, but, when mediated through the flexibility construct, it does. Further, the use of integration tools has a moderating effect on the relationship between flexibility and firm performance. The results suggest that working closely with supply chain partners helps ensure improved relationship satisfaction, and can reduce issues that can impact firm performance.

The empirical research presented requires additional validation though larger sample data from supply chain managers.

This study stresses on the importance of managers using information exchange, partner interaction, and knowledge sharing as a means of improving their firm’s indirect influence on firm performance through flexibility and integration tools.

This is one of the few studies in the supply chain literature that integrates flexibility as a mediator variable. Additionally, this study introduces the new construct of integration tools to the supply chain literature.

This study aims to provide a systematic review about the Internet of Things (IoT) and its impacts on happiness. It intends to serve as a platform for further research as it is sparse in in-depth analysis.

This systematic review initially observed 2,501 literary articles through the ScienceDirect and WorldCat search engines before narrowing it down to 72 articles based on subject matter relevance in the abstract and keywords. Accounting for duplicates between search engines, the count was reduced to 66 articles. To finally narrow down all the literature used in this systematic review, 66 articles were given a critical readthrough. The count was finally reduced to 53 total articles used in this systematic review.

This paper necessitates the claim that IoT will likely impact many aspects of our everyday lives. Through the literature observed, it was found that IoT will have some significant and positive impacts on people's welfare and lives. The unprecedented nature of IoTs impacts on society should warrant further research moving forward.

While the literature presented in this systematic review shows that IoT can positively impact the perceived or explicit happiness of people, the amount of literature found to supplement this argument is still on the lower end. They also necessitate the need for both greater depth and variety in this field of research.

Since technology is already a pervasive element of most people’s contemporary lives, it stands to reason that the most important factors to consider will be in how we might benefit from IoT or, more notably, how IoT can enhance our levels of happiness. A significant implication is its ability to reduce the gap in happiness levels between urban and rural areas.

Currently, the literature directly tackling the quantification of IoTs perceived influence on happiness has yet to be truly discussed broadly. This systematic review serves as a starting point for further discussion in the subject matter. In addition, this paper may lead to a better understanding of the IoT technology and how we can best advance and adapt it to the benefits of the society.

Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human–computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states.

This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model.

Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively.

This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.