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Given the growing emphasis on environmental consciousness and sustainability as core principles within most companies, product-service systems are recognized as strategic approaches to achieving sustainability objectives. Consequently, understanding consumer acceptance of these systems is of paramount importance. This study seeks to explore users' perspectives on the barriers that impede the adoption of product-service systems, intending to prioritize these obstacles.

This study utilizes a social media-based approach, specifically analyzing tweets related to Zipcar, an American car rental company that exemplifies a usage-oriented product-service system. The analysis identifies the factors influencing the acceptance of this system. The study utilizes topic modeling and sentiment analysis techniques to analyze the tweets. The opportunity value of each topic is determined, aiding in the identification of topics that require improvement. Furthermore, the interrelation between topics is explored, followed by correlation analysis to assess their significance.

Eight topics strongly related to the keywords are identified. Among them, “responsiveness”, “responsibility”, and “trust” hold the highest opportunity values. The findings emphasize the importance of service providers proactively addressing the obstacles that impede consumers' willingness to adopt product-service systems. Prioritization should be given to topics with higher opportunity values.

This research uncovers the primary obstacles to adopting the product-service system by directly considering consumer opinions and providing a prioritized list of these obstacles.

The success of e-commerce websites depends on their effective communication and influence on their users. At first glance, the users are impressed by the website design and, if inspired, they would continue their operations on the website. This paper aims to evaluate the effects of visual aesthetic of the Web pages on users’ behavior in online shopping environment. In particular, the paper aims to evaluate the elements of visual aesthetic on the organism variables (i.e. “satisfaction,” “arousal,” “perceived on-line service quality” and “trust”) and measure them on the users’ response (i.e. purchase, comparison and re-visit).

Using the stimulus–organism–response (S-O-R) framework, the authors first assessed direct and indirect effects of visual aesthetics of e-commerce websites on customer responses. Then, the Visual Aesthetics of Websites Inventory (VisAWI) method was used to examine the effects of four dimensions (i.e. craftsmanship, simplicity, diversity and colorfulness) on users’ perceived website aesthetics. To do so, DigiKala.com, a famous Iranian e-commerce website was selected and the questionnaires were distributed among its users.

The study results revealed that the website aesthetics in the S-O-R evaluation had the greatest direct impact on “perceived quality of online services,” “trust,” “satisfaction” and “arousal,” respectively. These variables also indirectly affected “shopping,” “revisit” and “comparison to similar products on other websites.” Regarding the evaluations based on the VisAWI, the component “craftsmanship” played the most central role in expressing the website aesthetics, followed by the variables “simplicity,” “diversity” and “colorfulness,” respectively.

Although the considerable effect of Web aesthetics on customers’ purchase behavior has been identified in previous research, it has not been accurately measured. Furthermore, studies on Web aesthetics are mostly limited to information systems’ users and do not concern consumers. Therefore, considering the increasing growth in online shopping and the significance of Web aesthetics to online consumers, investigating how consumers respond to Web aesthetics is of vital importance.

The purpose of this paper is to develop a theoretical model to explain the impact of big data analytics capabilities (BDAC) on company’s supply chain sustainability (CSCS). The secondary objective of the study is to assess the relationship between different dimensions of supply chain sustainability and companies’ BDAC.

This research was carried out by conducting a survey among 234 pharmaceutical companies in Iran (a case study of Iran), using a standard questionnaire of BDAC and United Nations (UN) online self-assessment on supply chain sustainability. However, the respond of managers of 188 companies were usable in this research. Smart PLS3 was used to employ partial least squares method to examine the validity and reliability of the measurement and structural model.

The results of this study demonstrate that BDAC have a strong impact on both pharmaceutical supply chain sustainability, and the dimensions including vision, engage and internal. It is found that the relationships between BDAC and the other dimensions of supply chain sustainability including expect, scope and goals are not significant but positive.

Research on the relationship between BDAC and CSCS, especially in the pharmaceutical supply chain, is scanty, and this gap is highlighted in developing countries and the pharmaceutical supply chain that plays a prominent role in public health. This paper discusses several important barriers to forming a sustainable supply chain and strong BDA capabilities.

This paper could be a guide to managers and consultants who are involved in big data analytics and sustainable development. Since UN urges companies do the online self-assessment, the results of this paper would be attractive and useful for UN global compact specialists.

No study has directly measured the relation between BDAC and CSCS and different dimensions of CSCS, using a comprehensive survey throughout all pharmaceutical companies in Iran. Moreover, this research assesses the different dimensions of BDA capabilities and supply chain sustainability. This paper represents the facts about situation of sustainability of pharmaceutical supply chain and BDAC in these companies, and discloses several related issues that are serious barriers to forming a sustainable supply chain and strong BDAC. In addition, this paper provided academic support for UN questionnaire about CSCS and used it in the survey.

The main purpose of the present study was to improve and develop previously proposed models for Iran telecommunication networks. It should be noted that the six-dimensional (6D) sustainability model used in this study will be a useful and comprehensive model for industries. Since, the new dimension of IT along with the five well-known economic, social, environmental, technical and institutional aspects of organizations is considered to have great impacts on supply chain sustainability; the proposed framework can be practical.

According to the related literature review, there are two research streams in supply chain management. The first stream is exploratory research, seeking out conceptual discussions in this area. The second one is associated with mathematical models and techniques, aiming to set decision-making rules in this regard (Agrell et al., 2004). In this study, evaluation was performed using the FMEA method as an analytical technique based on the principle of pre-occurrence prevention to identify potential failure factors in sensitive systems (Mohammadfam and Kianfari, 2008).

After identifying the risks and causes of the incidence and effects and consequences of risks, preventive and risk control measures and advisory strategies were presented. Customers with 45.76% share in critical risks are threatening to maintain supply chain in these companies. During this study, it was found that 33.9% of the main source of supply chain critical risks was customers, constituting 45.76% of such risks, accompanied by organization, having a 38.88% share of critical risk generation. The study findings also revealed that 33.9% of critical risks were mainly (equally) related to economic and technical aspects of supply chain sustainability in telecommunication networks. Moreover, as a newly-introduced sixth dimension, IT represented 10.17% of critical risks threatening supply chain sustainability in such networks. Critical risks are mainly related to the economic and technical aspects (equally) with the sustainability of the telecommunication networks supply chain. Also, as a new finding and the sixth dimension, 10.17% of the critical risks that threaten the sustainability of the telecommunication networks supply chain have the information technology dimension.

The internet and fixed and mobile data services are provided by several private companies in Iran, which are relatively similar in terms of their supply chains. In order to manage the sustainability of Iran's telecommunication supply chain, telecommunication networks affiliated to Iran Telecommunication Company (ITC), operating in the field of data and internet services and fixed telephone were selected in 31 provinces. The intended networks were also providing an important part of the country's needs including Mobile Telecommunication Company of Iran, a subsidiary of Iran's telecommunication networks, as one of the top companies in this industry. Accordingly, all the networks studied in this study needed to be identified with regard to communication sustainability risks, since they provide management solutions to each other by segregating risks. In this study, 68 managers and 72 experts participated in different work teams of telecommunication networks.

This paper aims to present a smart, sustainable supply chain practices structure on the basis of the relational view.

A method based on fuzzy cognitive map was applied to construct a relational map to introduce and implement such relational methods. Considering this relational map as a guideline, observations into particular methods and ways of applying relational methods to attain sustainable development goals across organizations has been introduced.

Primary outcomes provided a series of relational methods for the purpose of giving advice to those organizations and their suppliers for smart, sustainable supply chain. Reliance between relational methods were examined and assessed under seven meaningful groups: economic internet of things (IoT), green internet of things, social internet of things, economic supply chain, green supply chain, social supply chain and other variables.

This study guides managers toward an improved perception of the connection among IoT instances and sustainable supply to modeling smart, sustainable supply chain. Managers can determine the practices that need more focus along with the practices that are less relevant. Thus, this will help managers in the decision-making process and to organize their decisions by planning and calculating the relative importance and influence of smart, sustainable practices on each other and on the company’s smart, sustainable program.

To the best of the authors’ knowledge, this is the first approach that promptly examines and determines the interdependencies between relational methods and constructs a relational map for the purpose to introduce and analyze smart, sustainable supply chain.

This study explores the sustainable supply chain trade-offs in the electronics industry.

The study employs a social media analytics approach and analyses Twitter posts from August 2017 to December 2021. Thematic analysis is applied to discover the pattern in sustainable supply chain trade-offs based on the consumers' perceptions. In addition, a chi-square test was used to measure whether a relationship exists between product groups and sustainable supply chain perceptions.

The results indicate that environmental practices are the most frequent topic among consumers on social media. Further, although basic sustainable supply chain practices are prioritised in the environmental aspect, advanced sustainable supply chain practices take precedence over basic ones in the social dimension. The result from the chi-square independence test reveals that there is no significant relationship between different products and perceptions of consumers except for economically advanced sustainable supply chain practices.

The main implications of the present study are to offer a fast and efficient method to marketers and companies for discovering customer perceptions. In a way, they can identify where the quality of practices needs to improve in their supply chains to gain customer satisfaction. Additionally, the authors suggest industries declare their trade-off preferences between sustainable supply chain practices transparently.

The findings extend the abundance of sustainable supply chain literature by identifying the sustainable supply chain trade-offs among consumer electronics. Also, the reason for customers' dissatisfaction is provided. In the end, six propositions are presented based on the explorations.

Given the competitive environment and complicated relationships in supply chains in the modern era, it is important to take into account internal and external risks. In addition, proper methods must be designed to evaluate these risks correctly. The purpose of this paper is to provide a suitable map based on the artificial neural network technique to assess and classify the risk levels of retailers who have interconnected rules in the downstream of the supply chain.

In this research, a model for risk assessment with a hexagonal grid and 2D self-organizing map was applied.

According to the results, the model used in the study can provide a basis for classification of retailers based on the specified risk levels defined by the experts and risk managers of the company. Also with the model’s visual output, managers can have a better understanding of the distribution of the risk level of retailers.

The proposed methodology can be adopted by managers to assess the risk of members involved in the supply chain, helping them to formulate the risk mitigation strategies based on the risk levels.

As a part of the risk management process, organizations can use this developed method to reduce the existing risks imposed by the members or customers on the company.

Recently, reverse logistics (RL) has become more prominent due to growing environmental concerns, social responsibility, competitive advantages and high efficiency by customers because of expansion of product selection and shorter product life cycle. However, effective implementation of RL results in some direct advantages, the most important of which is winning customer satisfaction that is vital to a firm's success. Therefore, paying attention to customer feedback in supply chain (SC) and logistics processes has recently increased, so manufacturers have decided to transform their RL into customer-centric RL. Hence, this paper aims to identify the features of a mobile phone which affect consumers’ purchasing behavior and to analyze the causality and prominence relations among them that can help decision-makers, policy planners and managers of organizations to develop a framework for customer-centric RL. These features are studied based on analysis of product review sites. This paper's special focus is on social media (SM) data (Twitter) in an attempt to help the decision-making process in RL through a big data analysis approach.

This paper deals with identifying mobile phone features that affect consumer's mobile phone purchasing decisions. Using the DEMATEL approach and using experts' insights, a cause and effect relationship diagram was generated through which the effect of features was analyzed.

Eighteen features were categorized in terms of cause and effect, and the interrelationships of features were also analyzed. The threshold value is calculated as 0.023, and the values lower than that were eliminated to obtain the digraph. F6 (camera), F13 (price) and F5 (chip) are the most prominent features based on their prominent score. It was also found that the F5 (chip) has the highest driving power (1.228) and acts as a causal feature to influence other features.

The focus of this article is on SM data (Twitter), so that experts can understand the interaction between mobile phone features that affect consumer's decision on mobile phone purchasing by using the results. This study investigates the degree of influence of features on each other and categorizes the features into cause and effect groups. This study is also intended to help organizational decision-makers move toward a reverse customer SC.

Warranty service plays a critical role in sustainability and service continuity and influences customer satisfaction. Considering the role of social networks in customer feedback channels, one of the essential sources to examine the reflection of a product/service is social media mining. This paper aims to identify the frequent product failures through social network mining. Focusing on social media data as a comprehensive and online source to detect warranty issues reveals opportunities for improvement, such as user problems and necessities. This model will detect the causes of defects and prioritize improving components in a product-service system based on FMEA results.

Ontology-based methods, text mining and sentiment analysis with machine learning methods are performed on social media data to investigate product defects, symptoms and the relationship between warranty plans and customer behaviour. Also, the authors have incorporated multi-source data collection to cover all the possibilities. Then the authors promote a decision support system to help the decision-makers using the FMEA process have a more comprehensive insight through customer feedback. Finally, to validate the accuracy and reliability of the results, the authors used the operational data of a LENOVO laptop from a warranty service centre and classifier performance metrics to compare the authors’ results.

This study confirms the validity of social media data in detecting customer sentiments and discovering the most defective components and failures of the products/services. In other words, the informative threads are derived through a data preparation process and then are based on analyzing the different features of a failure (issues, symptoms, causes, components, solutions). Using social media data helps gain more accurate online information due to the limitation of warranty periods. In other words, using social media data broadens the scope of data gathering and lets in all feedback from different sources to recognize improvement opportunities.

This work contributes a DSS model using multi-channel social media mining through supervised machine learning for warranty-service improvement based on defect-related discovery to unravel the potential aspects of social networks analysis to predict the most vulnerable components of a product and the main causes of failures that lead to the inputs for the FMEA process and then, a cost optimization. The authors have used social media channels like Twitter, Facebook, Reddit, LENOVO Forums, GitHub, Quora and XDA-Developers to gather data about the LENOVO laptop failures as a case study.

In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.