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Driven by motivation of quality enhancement and brand reputation promotion, automotive industries try to improve product quality and customer satisfaction by performing quality pilot programs continuously. The purpose of this paper is to develop a dynamic model to select the improvement quality pilot program from competitive candidates based on dynamic customers’ feedback.

An extended dynamic multi-criteria decision-making method is developed by embedding dynamic triangular fuzzy weighting average operators into fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, and the novel evaluation indicator “ζ” is introduced to reflect prioritization performance.

The two evaluation indicators (Q and “ζ” ) assist quality managers to identify the best program with respect to multiple conflicting criteria and the best choice based on these two indexes shows high conformity. Besides, ranking sequences obtained by “ζ” can avoid the dilemma that there are several candidates with top priority calculated by comprehensive group utility value Q.

The dynamic MCDM method has been applied into the quality improvement procedure in Chinese domestic auto factories and contributes to highly efficient promotion.

Few dynamic models on pilot program selection for quality improvement based on dynamic customers’ feedback, this study deals with the dynamic promotion by an extended fuzzy VIKOR method and presents a case application.

The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.

A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.

The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.

The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.

Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

This paper aims to study the vehicle routing problem with dynamic customers considering dual service (including home delivery [HD] and customer pickup [CP]) in the last mile delivery in which three decisions have to be made: determine routes that lie along the HD points and CP facilities; optimize routes in real time, which mode is better between simultaneous dual service (SDS, HD points and CP facilities are served simultaneously by the same vehicle); and respective dual service (RDS, HD points and CP facilities are served by different vehicles)?

This paper establishes a mixed integer linear programing model for the dynamic vehicle routing problem considering simultaneous dual services (DVRP-SDS). To increase the practical usefulness and solve large instances, the authors designed a two-phase matheuristic including construction-improvement heuristics to solve the deterministic model and dynamic programing to adjust routes to dynamic customers.

The computational experiments show that the CP facilities offer greater flexibility for adjusting routes to dynamic customers and that the SDS delivery system outperforms the RDS delivery system in terms of cost and number of vehicles used.

The results provide managerial insights for express enterprises from the perspective of operation research to make decisions.

This paper is among the first papers to study the DVRP-SDS. Moreover, this paper guides the managers to select better delivery mode in the last mile delivery.

The purpose of this paper is to investigate the factors influencing the behavioral intention to adopt payments banks services by Indian underbanked and unbanked population.

The proposed model has assimilated factors from the Unified Theory of Acceptance and Use of Technology (UTAUT) along with perceived credibility. The factors of UTAUT include performance expectancy, effort expectancy, facilitation of conditions and social influence. Apart from testing the direct relationships of the model constructs with the behavioral intention to adopt payments banks services, the study has also explored mediating and moderating effects of certain constructs. The research model has been empirically tested using 660 responses from a field survey conducted in New Delhi – the capital city of India – by using the structured equation modeling (SEM) technique. The target respondents of the study are small businessmen and migrant laborers who are either underbanked or unbanked.

The findings of the study reveal that the model is able to explain 67.5 per cent of the variance in behavioral intention. The results indicate that all the factors are direct determinants of behavioral intention. Perceived credibility is found to be the strongest influencer of behavioral intention. The findings also indicate that perceived credibility partially mediates the relationships between “social influence and behavioral intention” and “performance expectancy and behavioral intention.” The relationship between performance expectancy and behavioral intention is also found to be moderated by facilitating conditions and effort expectancy.

As this study is based on a convenience sample of respondents of only one city of India, this could negatively reflect on the generalizability of results across other cities. Moreover, the study has only focused on the perceptions of small businessmen and migrant laborers. This raises concerns regarding the applicability of the results for other segments of the current population that have different demographic characteristics (e.g. occupation, income, education level and technology experience). Modifying the conceptual model presented in this research to include “experience” and “age” as moderators can also be worth considering in future. Although this study has extended the UTAUT to include perceived credibility, the results of the explanatory power of the model indicate that there is still room for improvement. Therefore, including other constructs, e.g. hedonic motivation, perceived risks and trialability, could be a fruitful path forward. Future studies may also examine the factors influencing the actual use behavior of payments banks, rather than just behavioral intention.

The study looks forward to providing the payments banks service providers in India with suitable guidelines for effectively implementing and designing payments banks services. Specifically, the results of this study have provided clues for Indian payments banks service providers about the crucial role of perceived credibility in influencing the behavioral intention to adopt payments banks. Therefore, service providers have to initially be sure that payments banks are able to conduct financial transactions efficiently, securely and within less time, along with the availability of information required by customers to successfully use the services. Service providers should enhance customer confidence and trust by providing secure and reliable services. They should also emphasize on the positive safety measures of the payments banks during any marketing campaign rather than just creating brand awareness.

The study represents a substantial contribution to the existing knowledge regarding mobile payment channels in particular and technology acceptance area in general. In fact, this study presents a worthwhile direction by examining payments banks services, which, so far, have not been well evaluated in the Indian context. To the best of the authors’ knowledge, this is an early attempt toward a holistic and integrative approach to explain adoption of payments banks in India. Although prior studies have addressed mobile banking and mobile payment adoption, the strength of this research lies in combining the UTAUT constructs with perceived credibility. This is evidenced by the high explanatory power (67.5 per cent) of the research model adopted in this study.

The study uses machine learning techniques to cluster regional retail egg prices after 2000 in China. Furthermore, it combines machine learning results with econometric models to study determinants of cluster affiliation. Eggs are an inexpensiv, nutritious and sustainable animal food. Contextually, China is the largest country in the world in terms of both egg production and consumption. Regional clustering can help governments to imporve the precision of price policies and help producers make better investment decisions. The results are purely driven by data.

The study introduces dynamic time warping (DTW) algorithm which takes into account time series properties to analyze provincial egg prices in China. The results are compared with several other algorithms, such as TADPole. DTW is superior, though it is computationally expensive. After the clustering, a multinomial logit model is run to study the determinants of cluster affiliation.

The study identified three clusters. The first cluster including 12 provinces and the second cluster including 2 provinces are the main egg production provinces and their neighboring provinces in China. The third cluster is mainly egg importing regions. Clusters 1 and 2 have higher price volatility. The authors confirm that due to transaction costs, the importing areas may have less price volatility.

The machine learning techniques could help governments make more precise policies and help producers make better investment decisions.

This is the first paper to use machine learning techniques to cluster food prices. It also combines machine learning and econometric models to better study price dynamics.

This paper aims to investigate the effects of deposition time on the structure and anti-corrosion properties of a micro-arc oxidation (MAO)/Al coating on AZ31B Mg alloy.

The study describes the fabrication of the coating via a combined process of MAO with multi-arc ion plating. The structure, composition and corrosion resistance of the coatings were evaluated using scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction and electrochemical methods.

The Al-layer is tightly deposited with a good mechanical interlock along the rough interface due to the Al diffusion. However, the Al layer reduces the anti-corrosion of MAO-coated Mg alloy because of structural defects such as droplets and cavities, which act as channels for corrosive media infiltration towards the substrate. Fortunately, the Al layer improves the substrate corrosion resistance owing to its passive behaviour, and the corrosion resistance can be enhanced with increasing deposition time. All results indicate that a buffer layer fabricated through the duplex process improves the interfacial compatibility between the hard coating and soft Mg alloys.

An MAO/Al duplex coating was fabricated via a combined process of MAO and physical vapour deposition. MAO/Al duplex coatings exhibit obviously passive behaviours on AZ31 Mg alloy. The structure and corrosion resistance of MAO/Al coatings were investigated.

This paper aims to prepare a residual rust epoxy coating by adding different quantities of phytic acid (PA) on the surface of the rusty steel and investigate the corrosion protection of PA and its action mechanisms.

A residual rust epoxy coating by adding different quantities of PA was prepared on the surface of the rusty steel. The influence of PA on the corrosion resistance of epoxy-coated rusty steel was investigated by means of electrochemical impedance spectroscopy and adhesion testing.

Results indicated that PA can substantially improve the corrosion resistance of epoxy-coated rusty steel. This improvement is due to the reaction of PA with residual rust and generation of new compounds with protection properties and increased adhesive strength effects on the coating/metal interface. The coating showed better protection performance when 2 per cent PA was added.

Considering the structure of the active groups, PA has strong chelating capability with many metal ions and can form stable complex compounds on the surface of a metal substrate, thereby improving corrosion resistance. In recent years, PA has been reported to be useful in the conversion of coatings or as green corrosion inhibitor. To the best of the authors’ knowledge, few studies have reported the use of PA as a rust converter or residual rust coating. The present work aims to improve the corrosion resistance of residual rust epoxy coating by adding PA.

This paper aim to investigate the influence of PA on the corrosion behavior of carbon steel with blast cleaned or pre-rusted treatments, and interpret the inhibition mechanism of PA on the steel with different surface treatments.

The influence of PA on the corrosion behavior of blast cleaned or rusty steel was investigated by means of electrochemical impedance spectroscopy (EIS). The EIS data were analyzed using the @ZsimpWin commercial software. The morphology and component of steel after immersion were characterized by scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), Fourier transformation infrared (FTIR) and X-ray diffractometer (XRD).

EIS analysis results indicated that PA had good corrosion inhibition for blast cleaned or rusty steel. SEM, EDS, FTIR and XRD further indicated that PA had two main corrosion inhibition processes for the corrosion inhibition of blast cleaned or rusty steel: corrosion dissolution and formation of protective barrier layers.

Most published works focus the attention only toward the effect of corrosion inhibitor for the clean metal surfaces. However, the surface condition of metal sometimes is unsatisfactory in the practical application of corrosion inhibitor, such as existing residual rust. Some studies also have shown that several corrosion inhibitors could be applied on partially rusted substrates. These inhibitors mainly include tannins and phosphoric acid, but not PA. Therefore, the authors investigated the influence of PA on the corrosion behavior of carbon steel with blast cleaned or pre-rusted treatments in this paper.

Supply chain risk (SCR) has increasingly attracted academic and corporate interest; however, the SCR debate in academic literature is rather limited to case- and location-specific studies. Hence, the purpose of this paper is to utilize a systematic literature review (SLR) to explore the SCR research trends and gaps within the management literature.

To achieve the research objective an SLR, looking into 25 years since 1990, into supply chain risk management (SCRM) was conducted, which resulted in 114 papers.

While the SCRM literature is growing, results from the SLR identified limited organized understanding of what constitutes a holistic SCR process, and high reliance of particular categories for SCR, such as the high reliance on specific country settings (the USA and the UK); limited presence of cross competitive SCR process analysis and challenges in developing conceptual SCR frameworks.

The SCR embeds categories of location, scope of supply chain, risk management tools, and the industry sectors involved. The search for related publications was mainly used from a wide range of coverage from accountancy to design in SCR; hence, although there is indication to specific industries, and foci of risk, this could be explored further.

This review of SCRM identifies various research gaps and directions for future research to develop theory and a practical understanding of SCR.

The current literature on SCR has been assessed based on its definition and utilization. The current paper bridges this gap by synthesizing the diverse academic journal papers into the categories based on the design continuum, relationship continuum, process continuum and economic continuum. In addition, it highlights the gaps in industry context, theoretical contribution, geographic location, and research methods applied and addresses the scope for further research.

In recent years, some peer-to-peer (P2P) service sharing platforms have improved their service quality by setting an entry quality threshold for service providers. Considering consumers' heterogeneous preferences for service quality and commission rate, it is worth studying how to select the commission rate contract for a P2P platform under a predetermined entry quality threshold for service providers.

In this study, the platform's profit-maximizing model is constructed under two proposed contracts: unilateral commission rate contract and bilateral commission rate contract. The optimal entry quality threshold and the optimal commission rate are obtained. This study also explores the impacts of cross-side network externality and service price on a platform's optimal decisions and social welfare.

Results show that it is always advantageous for the platform to adopt the bilateral commission rate contract, which is closely related to the strength of cross-side network externality, service price and quality sensitivity coefficient. Under certain conditions, adopting the unilateral commission rate contract can reduce platform profit and service provider surplus, and improve consumer surplus and social welfare.

This study analyzes the unilateral commission rate contract and the bilateral commission rate contract of the platform, and discusses which contract is beneficial to the platform, consumers and service providers. In addition, this study provides a basis for the operation decision of a P2P service sharing platform and the pricing decision of service providers.