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Sustainability has been on the executive agenda for years and it is now one of the fastest growing supply chain management trends. The purpose of this paper is to analyze the barriers for the adoption and implementation of the sustainable supply chain management (SSCM) concept.

This study has been divided into two phases such as identification of barriers and qualitative analysis. First, to identify the most influential barriers, the authors offer a systematic literature review, taking 188 papers published from 2010 to November 2016 into account. The investigation phase led to the selection of 15 barriers based on the literature in consultation with industrial experts and academicians. Second, the interpretive structural modeling qualitative analysis was used to find out the mutual influences between the 15 barriers by a survey.

Further, the authors propose and illustrate the cross-impact matrix multiplication applied to classification analysis to test a framework that extrapolates SSCM barriers and their relationships. “Inadequate information technology implementation” has been identified as the most important barrier that may force organizations to implement SSCM practices to ensure their business sustainability.

The authors presented some limitations in their research in some fields which could allow new researchers and practitioners to conduct the future research to grow in different dimensions.

Practitioners or policymakers usually are not familiar with these types of research works; that is why most of these surveys remain theoretical and conceptual. Future investigation needs to be done in practical application domain instead of merely giving opinions.

Based on the authors’ research, the researchers have more attention to work in conceptual analysis due to other fields, but the authors believe that even with the implementation of SSCM, many remarkable areas still exist for future research which could help in development. The authors also provide more details in this paper.

This paper aims to examine how presence (the social presence of live streaming platforms, of viewers, of live streamers and telepresence) affects consumer trust and flow state, thus inducing impulsive buying behaviors, personal sense of power as moderator.

Drawing on the Stimulus-Organism-Response (S-O-R) framework, the conceptual model covers social presence, telepresence, consumer trust, flow state, personal sense of power and impulsive buying behavior. An online survey was conducted from 405 consumers with the experience of live streaming shopping in China; structural equation modeling (SEM) was used for data analysis.

Results find that three dimensions of social presence (the social presence of live streaming platforms, of viewers, of live streamers) and telepresence have a positive and significant influence on consumer trust and flow state, thus triggering consumers’ impulsive buying behavior. Furthermore, consumers’ sense of power moderates the process from consumer trust, flow state to impulsive buying behavior.

This study will help live streamers and e-retailers to have a further understand on how to stimulate consumers’ buying behavior. Furthermore, it also provides reference for the development of live streaming commerce in other countries.

This research examines the effect of social presence and telepresence on impulsive buying behavior in live streaming commerce, which is inadequately examined in extant literature.

The purpose of this study is to investigate the initial corrosion behavior of pure Mg, AZ31 and AZ91 alloys in phosphate buffer solution (PBS) and to characterize the features in corrosion type and resistance of the corrosion product layer.

The scanning electron microscopy, equipped with energy-dispersive spectroscopy, X-ray photoelectron spectroscopy and electrochemical impedance spectroscopy have been used to characterize the as-corroded samples. Besides, the Mg²⁺ concentration in PBSs has been determined by inductively coupled plasma atomic emission spectrum.

It has been found that pure Mg suffers pit corrosion, and AZ31 initially undergoes pit corrosion and then uniform corrosion dominates with an extended immersion duration. However, AZ91 exhibits the uniform corrosion with the highest corrosion rate among the three materials. Besides, the corrosion product layer on AZ31 has the best compactness and corrosion resistance.

The findings add depth in understanding the corrosion of pure Mg and its alloys in PBS and also have guiding significance in exploring the effects by alloyed elements to develop new biomaterials with better performance.

This study aims to empirically examine the influence of management control systems (MCSs) on knowledge acquisition from innovation failure (KAFIF), which further impacts empowerment, creativity and organizational innovation. This study argues that enabling an MCS positively influences KAFIF, whereas controlling the use of an MCS negatively influences KAFIF. Further, KAFIF positively impacts empowerment, creativity and organizational innovation.

This study aims to create a comprehensive stimulus–organism–response (S-O-R) framework. This framework includes an MCS (belief, interactive, boundary and diagnostic) as a stimulus, KAFIF as an organism and creativity, empowerment and organizational innovation as responses. The data were gathered using an online survey administered to a sample of 321 employees working in India’s micro, small and medium enterprises and analyzed using partial least squares structural equation modeling.

The results indicate that there is no correlation between belief control and the acquisition of knowledge from the failure of innovation, interactive control has a positive association with KAFIF and boundary control has no relationship with KAFIF. Diagnostic control has a significant negative association with KAFIF. Further, this study found that KAFIF positively associates with empowerment, creativity and organizational innovation.

This study is among initial studies that examine the influence of MCSs on KAFIF, which impacts empowerment, creativity and organizational innovation. Further, it helps be one of the initial literature on studying KAFIF rather than innovation success.

This study aims to identify the essential elements impacting the adoption of blockchain technology (BCT) in supply chain management (SCM) by integrating the technology acceptance and information system success (ISS) models.

Questionnaire-based data was collected from 236 supply chain professionals from Beijing. The proposed research framework was evaluated using structural equation modeling (SEM) by using SPSS 23 and AMOS 24 software.

The empirical findings specify the positive influence of total quality on perceived usefulness and compatibility. Further, perceived ease of use positively influences perceived usefulness, compatibility and behavioral intention. Moreover, perceived usefulness positively impacts compatibility and behavioral intention. Compatibility positively influences behavioral intention. Finally, technology trust was found to be a significant moderator between perceived usefulness and behavioral intention and between perceived ease of use and adoption intention to use BCT in SCM.

This study empirically develops the second-order construct of total quality, representing the ISS model. Furthermore, this study established how the ISS and technology acceptance models influence behavioral intention through compatibility. Finally, this study confirmed the moderating role of technology trust among perceived ease of use, perceived usefulness and behavioral intention.

In the digital economy, as artificial intelligence applications increase, big data analytical capability (BDAC) plays a crucial role, and intellectual capital is growing in importance. This study aims to examine the possible benefits and effects of intellectual capital, BDAC and integrations on operational performance.

This study collected data from firms in Asia, and a total of 257 senior managers completed a questionnaire. Confirmatory factor analysis and structural equation modeling (SEM) is used for statistical analysis.

Intellectual capital positively correlates with BDAC, and BDAC positively relates to internal integration but not to external integration. Furthermore, both internal integration and external integration positively correlate with operational performance. This study supports that internal integration is a significant mediator in the influence of BDAC on operational performance.

First, the authors provide empirical evidence that intelligent capital in intangible resources helps firms to build BDAC. Second, this study stresses the importance of BDAC in business, which enhances the integration of the whole supply chain and results in superior operational performance.

This is a first attempt from the perspective of intelligent capital and uses SEM to emphasize the relationships among BDAC, supply chain integration and performance based on unique and irreplaceable intangible resources, thus providing a new perspective on the contributing factors of BDAC.

Enterprises are increasingly taking actionable steps to transform existing business models through digital technologies for service transformation such as big data analytics (BDA). BDA capabilities offer financial institutions to source financial data, analyse data, insight and store such data and information on collaborative platforms for a quick decision-making process. Accordingly, this study identifies how BDA capabilities can be deployed to provide significant improvement for financial services agility.

The study relied on survey data from 485 banking professionals' perspectives with BDA usage, IT capability development and financial service agility. The PLS-SEM technique was used to evaluate the underlying relationship and the applicability of the research framework proposed.

Based on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint proof that financial service agility could be enhanced provided enterprises develop technical capabilities alongside other relevant resources.

The study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering and market observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products.

This study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to improving enterprises operations.

Big data analytics capabilities are the driving force and deemed as an operational excellence approach to improving the green supply chain performance in the post COVID-19 situation. Motivated by the COVID-19 epidemic and the problems it poses to the supply chain's long-term viability, this study used dynamic capabilities theory as a foundation to assess the imperative role of big data analytics capabilities (management, talent and technological) toward green supply chain performance.

This study was quantitative and cross-sectional. Data were collected from 374 executives through a survey questionnaire method by applying an appropriate random sampling technique. The authors employed PLS-SEM to analyze the data.

The findings revealed that big data analytics capabilities play a significant role in boosting up sustainable supply chain performance. It was found that big data analytics capabilities significantly contributed to supply chain risk management and innovative green product development that ultimately enhanced innovation and learning performance. Moreover, innovation and green learning performance has a significant and positive relationship with sustainable supply chain performance. In the post COVID-19 situation, organizations can enhance their sustainable supply chain performance by giving extra attention to big data analytics capabilities and supply chain risk and innovativeness.

The paper specifically emphasizes on the factors that result in the sustainability in supply chain integrated with the big data analytics. Additionally, it offers the boundary condition for gaining the sustainable supply chain management.