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Work context transformed. Employees increasingly interact with enterprise social media, wherein employees may feel disconnected from workplace. Drawing on social affiliation theory, we develop and examine a moderated mediation model to explore the indirect effect of enterprise social media usage on counterproductive work behavior (CWB) via workplace loneliness and the moderating role of information and communication technology hassle (ICT hassle). We test hypotheses by conducting a three-wave survey of 345 knowledge workers. Results indicate that enterprise social media usage has a positive effect on workplace loneliness, and workplace loneliness mediated the indirect effect of enterprise social media usage on CWB. The moderated mediation analysis indicated that ICT hassle positively moderates the above mediation effect. We discuss the theoretical and practical implications of our findings.

Abstract Scholarly attention to social innovation has increased rapidly in recent years, but a broad picture to illustrate the structure of the field, tracing its evolution and identifying new research areas has been lacking. Thus, we conduct a three-step method to explore the trajectory of academic research on social innovation and identify potential research opportunities. In the first step, the bibliometric mapping software VOSviewer is used to visualize the network of authors and keyword co-occurrences. Next, SciMAT is applied to illustrate the evolution and importance of the themes. Then, content analysis is used to identify how specific research topics in social innovation in higher education have evolved. The author-keyword occurrence analysis and evolution map results reveal that innovation and design thinking were the most prominent keywords, and the citation analysis indicates that the works of Swyngedouw, Moulaert, and Westley as the leading research works in this field. The most influential countries during the study period from 1996 to July 2021 were the UK, the US, and Italy. Furthermore, the research collaboration network is more active among developed countries than among developing countries. Three research streams in social innovation in higher education have been identified: curriculum transformation, community-university partnership, and helix partnerships. This study supports understanding how higher education is shaped through social innovation. The results from this study can serve as a research reference on the state of the art and point to new research opportunities, notably on the need to engage developing countries in collaborating on social innovation in higher education through design, engagement, and partnership.

Abstract As online P2P loans in automotive financing grows, there is a need to manage and control the credit risk of the personal auto loans. In this paper, the personal auto loans data sets on the Kaggle platform are used on a machine learning based credit risk assessment mechanism for personal auto loans. An integrated Smote-Tomek Link algorithm is proposed to convert the data set into a balanced data set. Then, an improved Filter-Wrapper feature selection method is presented to select credit risk assessment indexes for the loans. Combining Particle Swarm Optimization (PSO) with the eXtreme Gradient Boosting (XGBoost) model, a PSO-XGBoost model is formed to assess the credit risk of the loans. The PSO-XGBoost model is compared against the XGBoost, Random Forest, and Logistic Regression models on the standard performance evaluation indexes of accuracy, precision, ROC curve, and AUC value. The PSO-XGBoost model is found to be superior on classification performance and classification effect.

This paper proposes a new time-varying integer-valued autoregressive (TV-INAR) model with a state vector following a logistic regression structure. Since the autoregressive coefficient in the model is time-dependent, the Kalman-smoothed method is applicable. Some statistical properties of the model are established. To estimate the parameters of the model, a two-step estimation method is proposed. In the first step, the Kalman-smoothed estimation method, which is suitable for handling time-dependent systems and nonstationary stochastic processes, is utilized to estimate the time-varying parameters. In the second step, conditional least squares is used to estimate the parameter in the error term. This proposed method allows estimating the parameters in the nonlinear model and deriving the analytical solutions. The performance of the estimation method is evaluated through simulation studies. The model is then validated using actual time series data.

Abstract This study examines the role of social connections and network centrality in attracting funders to crowdfunding campaigns. We classify social connections as either external (e.g., Facebook) or internal (e.g., investing in online platforms through resource exchange). Drawing from the 108,463 crowdfunding campaigns on the online platform Kickstarter from April 21, 2009, to July 24, 2019, we apply external linkages and online followers to estimate the effect of external social connections. We construct a digraph network for the internal social connections and use PageRank, HITS, and centrality to obtain the weights of the nodes. Next, we compare the performance change of several prediction algorithms by feeding social connection-related variables. This study has several findings. First, for external social connections, having more online followers improves the funding success rate of a campaign. Second, for internal social connections, only authority and degree in centrality positively affect the number of funders and the campaign’s financing progress among the weights of the nodes. Third, using social connection variables improves the prediction algorithms for funding outcomes. Fourth, external social connections exert greater funding outcomes than internal social connections. Fourth, entrepreneurs should extend their external social connections to their internal social connections, and network centrality expedites project financing. Fifth, the effect of social connections on fundraising outcomes varies among the campaign categories. Fundraisers who are online influencers should leverage their online social connections, notably for the project categories that matter.

Smart data selection can quickly sieve valuable information from initial data. Doing so improves the efficiency of analyzing situations to aid in better decision-making. Past methods have mostly been based on expert experience, which may be subjective and inefficient when dealing with large, complex datasets. Recently, the system analysis method has been exploited to find the key data. However, few studies address the indirect effects and heterogeneity of time series data. In this study, a data selection method, the modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method based on the objective data grey relational analysis (GRA), is used to enhance the ability to analyze time-series data. GRA was first applied to assess the direct impact in the raw data indicators. Then, a modified DEMATEL was adopted to find the overall impact by including the indirect impact and data heterogeneity. We applied the method to analyze the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and perform the remaining useful life (RUL) prediction of aircraft engines. The results suggest that our method predicts well. Our work offers a nuanced approach of identifying key information in time series data and has potential applications.

The product development knowledge stored in manufacturing firms can be obtained and re-used to assist the development engineers in new product development and facilitate the use of knowledge resources, which in turn enhances the quality and reduces the time-cost of product innovation. This study presents a knowledge service integrated framework for product development, which is refined from the literature and supported by practice needs. The proposed integrated framework is supported by four pillars of work, namely, how to represent knowledge exactly, the model of knowledge integration, the method of knowledge recommendation, and the mechanism and performance evaluation of knowledge service. A demonstration of a gantry crane design is provided to show the effectiveness and benefit of the proposed integrated framework and technology solutions. Finally, we offer some pointers for future research.

Recently, Yang et al. (Computers & Industrial Engineering, 131: 157–171, 2019) proposed a solution approach to the two-stage fuzzy location problem under the Value-at-Risk (VaR) criterion, which significantly reduced the computational complexity compared with the other approximation treatments. However, in their work, the approach was developed for cases with regular fuzzy variables, which renders it useless when dealing with other types of fuzzy parameters such as discrete fuzzy variables and trapezoidal fuzzy numbers. In this note, for the general case involving arbitrary types of fuzzy parameters, we show that the VaR of a location decision can be determined exactly by solving a corresponding deterministic linear programming. Consequently, a similar approach is developed for the generalized case. Utilizing the extended approach, we show that the discrete case presented by Yang et al. can be solved directly rather than by enumerating all possible scenarios. Furthermore, a numerical example with regular fuzzy variables studied in their work is extended to the continuous but irregular case to illustrate our extension.

Many Automatic Identification (Auto–ID) technologies such as bar codes, magnetic stripes, Optical Character Recognition (OCR) and Electronic Article Surveillance (EAS) security tags are in existence. However, they are limited by a variety of constraints on them such as limited object rate of scan, need for Line-of-Sight (LOS) operation, very small interrogation range and poor accuracy in complex environments. Research has accordingly grown rapidly in recent years into the development of robust identification or tracking mechanisms. Radio Frequency Identification (RFID) technology using readers and RFID tags, whether passive, semi-passive or active in nature, has been deemed to be a promising candidate. Silicon based IC tags are quite popular. However, some alternative RFID technologies have also been on the rise. In particular, another special type of tag based on Surface Acoustic Wave (SAW) design also has a great potential for deployment in future identification fields due to its ruggedness against harsh conditions, metallic environments, interference, small and low cost and is being thoroughly reviewed, which is ignored in other survey papers. While other chipless RFID systems based on conductive ink, ink-tattoo and others do exist, negative aspects such as inability to survive in extreme weather, larger tag size and limited data storage capacity have severely impacted the penetration of such tags in the RFID market. Therefore, the scope of this survey article does not include chipless RFID tags. This article instead provides a comparative survey of silicon based IC and SAW based tags, which has attracted much attention from both academia and industry. The underlying characteristics, principles, advantages, and limitations of such tags are thoroughly discussed, and relevant research work followed by frequency of operation and other parameters are elaborated. This article undertakes a thorough investigation into the evolution of RFID technology and comparison into the current trend in silicon based IC and SAW based RFID tags and provides a comparison across many metrics, such as read range, tag size, tag power, availability of tag power, lifespan and more importantly, cost and security. It discusses active and passive tags under silicon based IC and SAW technology. Finally, the article reviews recent advances and provides potentials, open challenges and future research directions in such RFID tags.

Most carbon Emission Trading Systems (ETS) rely on a centralized system to manage the transactional tasks, and are vulnerable to security threats. This article proposes a Blockchain-enabled Distributed ETS (BD-ETS) to improve the security and efficiency of the system. The BD-ETS transforms the centralized Carbon Emissions Permit (CEP) trading mode to a distributed trading system in which the trading mode is based on a smart contract performed in Hyperledger Fabric. In a smart contract, every transaction considers both the offer price and reputation value of the emitting enterprises. The voting power of the emitting enterprise is determined by its reputation value, which stems from their contributions to carbon emission reduction. To achieve consistency of every node in the CEP transactions, we propose a Delegated Proof of Reputation (DPoR) consensus mechanism. Compared to the enhanced Delegated Proof of Stake, the DPoR decreases the attack intention of malicious enterprises and performs better in finding malicious miners faster, thus improving the security of the BD-ETS. A case study and numerical simulations are developed to illustrate how the CEP trading functions, and to validate the DPoR mechanism.

Purpose – Trust is essential for any team working together. In humanitarian logistics operations, relief organizations often have to work collaboratively in hastily formed networks. Trust in such a context, termed as “swift trust” in the literature, is an important but less explored topic. The purpose of this paper is to empirically explore the antecedents of swift trust as well as its impact on the coordination among the humanitarian workers. Design/methodology/approach – The authors choose the humanitarian workers in Southeast Asia as the research sample. An exploratory survey study is conducted in three Southeast Asian countries, namely, Singapore, Indonesia, and the Philippines, with 89 usable responses. Findings – The empirical results have shown support for most hypotheses. Third-party certification, competency, similarity in procedure standards and organizational values, can all generate swift trust. Moreover, swift trust can lead to greater openness in information sharing for coordination, though not to active assistance. Both coordination activities would lead to greater coordination effectiveness. Research limitations/implications – Future studies could examine the four antecedent conditions for swift trust with better proxies. The connection between swift trust and coordination effectiveness can be explored in depth. Practical implications – NGOs and governments could use these means effectively to build swift trust among the humanitarian players. For example, organizing field-oriented training activities would be beneficial for humanitarian workers in both network building and enhancing personal competency. Originality/value – The findings point to the importance of swift trust in humanitarian operations and identify several means to enhance this trust. It has filled a research gap on the empirical investigation of the antecedents and impact of swift trust on inter-organizational coordination in humanitarian logistics operations.

Purpose – The purpose of this paper is to provide a decision support framework for locations identification to address network design in the domain of disaster relief supply chains. The solution approach is then applied to a real-life case about Indonesia. Design/methodology/approach – An approach integrating geographic information system technology and fuzzy analytical hierarchy process has been used. Findings – For the Indonesian case, distribution centers should be located in Pekanbaru, Surabaya, Banjarmasin, Ambon, Timika, and Manado. Research limitations/implications – The main limitation of this work is that facilities being sited are incapacitated. Inclusion of constraints over capacity would elevate the framework to a further level of sophistication, enabling virtual pool of inventory that can be used to adsorb fluctuation in the demand due to disasters. Practical implications – The use case provided in this paper shows a practical example of applicability for the proposed framework. This study is able to support worldwide decision makers facing challenges related with disaster relief chains resilience. In order to achieve efficiency and effectiveness in relief operations, strategic logistics planning in preparedness is key. Hence, initiatives in disaster preparedness should be enhanced. Originality/value – It adds value to the previous literature on humanitarian logistics by providing a real-life case study as use case for the proposed methodology. It can guide decision makers in designing resilient humanitarian response, worldwide. Moreover, a combination of recommendations from humanitarian logistics practitioners with established models in facility location sciences provides an interdisciplinary solution to this complex exercise.

Flight delays are widespread and growing around the world. Using public domain data of the airlines in Australia's domestic aviation market, this study investigates the antecedents of flight delays, and how airline operations contribute to such an important issue in the aviation industry. Applying the Hausman-Taylor regression method, the estimations suggest that the variables of the airline capacity decision are meaningful determinants of flight delays. Our findings inform that more frequent flights and larger aircraft are associated with more flight delays. A higher level of route congestion leads to more flight delays, especially for the low-cost carriers. Jet fuel price is identified as a driver, leading to more flight delays for the legacy airlines compared to the low-cost carriers. Keen competition between the airlines actually helps to improve the flight service quality, through fewer flight delays.

The major challenges of deregulation are lax market entry, sudden surge in new market entrants, and the intense price wars that ensue, thus causing major losses for any industry. This paper investigates whether deregulation can be structured through a controlled Mergers and Acquisitions (M&As) process by means of government intervention, and how this promotes the performance of the players in the industry. We study this in the context of the Chinese aviation industry as an ideal microcosm of our problem statement. This is because China’s civil aviation industry has witnessed many of the above challenges since its deregulation and economic reforms in 1979, which saw the beginning of a transformation from a fully state-owned machinery to a rent-seeking private sector. The post controlled deregulation process through M&As led to three dominant carriers: Air China Limited (AC), China Southern Airlines (CS), and China Eastern Airlines (CE). Using a 3-player non-cooperative perfect information Cournot oligopoly game model, the strategic efficacy of the government intervention to consolidate the industry based on operating expenses, air passenger revenue, and profit data are investigated respectively. All three airlines are better off after the exercise, with the industry facing a more sustainable growth by the intervention.

Purpose – – In humanitarian logistics operations, performance measurement is crucial for effective operation. The purpose of this paper is to develop a set of indicators for humanitarian relief organizations (HROs) for their organizational-level logistics operations. Design/methodology/approach – – The authors applied the supply chain operations reference (SCOR) framework to the context of humanitarian supply chains. By taking a bottom-up approach with the support of a large HRO, the authors identified the most important metrics through examining its supply chain processes. The initial metrics are then validated by seven HROs to ensure their applicability in humanitarian logistics operations. Findings – – A hierarchical benchmarking framework is proposed, and a set of 26 metrics is identified. The validation of these metrics supports the initial work with all metrics deemed important. It also highlights the implementation difficulty as only five indicators are readily available. The authors further suggested the automation of key logistics processes, which would significantly increase the number of implementable metrics to 14. Research limitations/implications – – The sample size of the validation is small, and the last mile delivery is not covered by the metrics. Practical implications – – With these performance metrics, HROs are able to monitor their logistics performance better with processed-based measures, which may lead to their policy and process adjustments for performance improvement. Originality/value – – The work contributes to performance measurement in humanitarian logistics with a framework of a generic metrics set. The validation result is also original to reveal the state of performance measurement on the ground.

A multi-criteria decision-making (MCDM) method with single-valued neutrosophic information is developed based on the Partitioned Heronian Mean (PHM) operator and the Shapley fuzzy measure, which recognizes correlation among the selection criteria. Motivated by the PHM operator and Shapley fuzzy measure, two new aggregation operators, namely the single-valued neutrosophic PHM operator and the weighted single-valued neutrosophic Shapley PHM operator, are defined, and their corresponding properties and some special cases are investigated. An MCDM model is applied to solve the single-valued neutrosophic problem where weight information is not completely known. An example is provided to validate the proposed method.

The busiest container ports in the world are mostly situated in the Asian region, thus intensifying inter-port competition. Particularly unique in this region, is the shared history between Malaysia and Singapore, which was once ruled-under the British empire, has since become economic rivals for the Far East – Europe trade route. This provides a suitable context to investigate whether competition or a strategic alliance should be adopted for ports in terms of its benefit to the industry in the region as a whole. Specifically, this paper analyses the ocean freight traffic demand for the Far East-Europe route among three main transshipment ports located in Malaysia and Singapore: Port of Singapore (PSA), Port Klang (PKL), and Port of Tanjung Pelepas (PTP). The paper provides a scenario analysis of the 3-way interaction through a game theoretic model. The results suggest that a strategic alliance between PSA and PTP generates greater profitability to the current hub and spoke network, while PKL should not commit to any cooperative strategy with either PSA or PTP.

To stimulate the development of the Internet of Health Things and the construction of a robotic automation system in the healthcare industry, the mobile robot selection problem for a hospital pharmacy is studied. We target the mobile robot selection as a multi-criteria decision-making problem. The VIKOR-based implementation steps integrating fuzzy extended analytic hierarchy process technique are developed. To avoid any loss of information and ensure the veracity of the VIKOR-based calculations, we employ the fuzzy ranking technique based on the degree of possibility to select the best mobile robot alternative by deriving the minimum fuzzy comprehensive utility value. The results of a case study and the accompanying sensitivity analysis show the effectiveness and robustness of the fuzzy extended VIKOR approach for the mobile robot selection problem.

Purpose – – As humanitarian organizations often operate in highly volatile environments, a good understanding of their learning mechanisms would improve the knowledge acquisition and retention in these organizations, and complement the formal logistics education and training for their relief staff. The paper explores the following research question: what learning mechanisms are used by humanitarian organizations to acquire sufficient knowledge for their logistics operations. Design/methodology/approach – – The paper opts for a theoretical study based on the existing literature. We apply organizational learning theory to examine the learning process and mechanisms of humanitarian organizations. An actual case is used to validate the findings. Findings – – The study develops a theoretical framework for the learning mechanism of humanitarian organizations, and reports four learning mechanisms: learning by hiring, learning by doing, learning by observing, and learning by searching. Five propositions are proposed for empirical validation. Research limitations/implications – – Because of the theoretical nature of the study, the research is still an exploratory one. The organization learning literature employed is not comprehensive. Researchers are encouraged to test the proposed propositions further. Practical implications – – The paper includes implications for humanitarian organization to better manage their learning processes, improve the effectiveness of their knowledge management, and benefit more disaster-affected people. Originality/value – – This paper fills a gap in the learning processes and knowledge management of humanitarian organizations.

This paper studies a class of multi-objective n-person non-zero sum games through a robust weighted approach where each player has more than one competing objective. This robust weighted multi-objective game model assumes that each player attaches a set of weights to its objectives instead of accessing accurate weights. Each player wishes to minimize its maximum weighted sum objective where the maximization is pointing to the set of weights. To address this new model, a new equilibrium concept-robust weighted Nash equilibrium is obtained. The existence of this new concept is proven on suitable assumptions about the multi-objective payoffs.