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As e-commerce has expanded rapidly, online shopping platforms have become widespread in India and throughout the world. Product return, which has a negative effect on the E-Commerce Industry's economic and ecological sustainability, is one of the E-Commerce Industry's greatest challenges in light of the substantial increase in online transactions. The authors have analyzed the purchasing patterns of the customers to better comprehend their product purchase and return patterns.

The authors utilized digital transformation techniques-based recency, frequency and monetary models to better understand and segment potential customers in order to address personalized strategies to increase sales, and the authors performed seller clustering using k-means and hierarchical clustering to determine why some sellers have the most sales and what products they offer that entice customers to purchase.

The authors discovered, through the application of digital transformation models to customer segmentation, that over 61.15% of consumers are likely to purchase, loyal customers and utilize firm service, whereas approximately 35% of customers have either stopped purchasing or have relatively low spending. To retain these consumer segments, special consideration and an enticing offer are required. As the authors dug deeper into the seller clustering, we discovered that the maximum number of clusters is six, while certain clusters indicate that prompt delivery of the goods plays a crucial role in customer feedback and high sales volume.

This is one of the rare study that develops a seller segmentation strategy by utilizing digital transformation-based methods in order to achieve seller group division.

Generic quality management system standard ISO 9001 and the automotive quality management system standard IATF 16949 both require organisations to demonstrate continual improvement in their customer satisfaction and the number of non-conformities identified during quality system audits. However, the long-term trends of these two parameters under ISO 9001 and IATF 16949 standards are not researched so far. It is expected that under continual improvement, organisations will achieve a step-function/stair-case shaped pattern. This study evaluates if this expectation is true when long-term performance of certified organisations is assessed.

A longitudinal exploration of three organisations certified to ISO 9001 standard and three certified to IATF 16949 standard is done. The observations are further substantiated using secondary data for the same ten years period about customer satisfaction of the major automobile manufacturers.

It is observed that none of the two indicators, i.e. the customer satisfaction and number of non-conformities, in any of the six organisations show step-wise/stair-case type improvement. All indicators followed random up and down patterns like ocean waves. It is paradoxical that certified organisations are claiming continual improvement and are remaining certified but there is actually no long-term improvement.

Longitudinal studies for the generic quality management standard ISO 9001 and the quality system standard for automotive sector IATF 16949 are rare. The revelation about ocean wave patterns observed in the long-term trends for customer satisfaction and the number of non-conformities in ISO 9001 and IATF 16949 certified organisations is a startling finding. It is outlandishly different from the conventional perception of a staircase-styled continual improvement pattern expected a priori in certified organisations.

Consumers are increasingly choosing social media over other channels and mechanisms for grievance redressal. However, not all social media grievances elicit a response from businesses. Hence, in this research the authors aim to explore the effect of the complainant's social characteristics and the complaint's social and content characteristics on the likelihood of receiving a response to a grievance from the business on social media.

The authors build a conceptual model and then empirically test it to explore the effect of the complainant's characteristics and the complaint's characteristics on the likelihood of response from a business on social media. The authors use data of consumer grievances received by an Indian airline operator on Twitter during two time periods – the first corresponding to lockdown during Covid-19 pandemic, and the second corresponding to the resumption of business as usual following these lockdowns. The authors use logistic regression and the hazard rate model to model the likelihood of response and the response delay, respectively, for social media customer grievances.

Complainants with high social influence are not more likely to get a response for their grievances on social media. While tagging other individuals and business accounts in a social media complaint has negative effect on the likelihood of business response in both the time periods, the effect of tagging regulatory bodies on the likelihood of response was negative only in the Covid-19 lockdown period. The readability and valence of a complaint were found to positively affect the likelihood of response to a social media grievance. However, the effect of valence was significant only in lockdown period.

This research offers insights on what elicits responses from a service provider to consumers' grievances on social media platforms. The extant literature is a plenty on how firms should be engaging consumers on online media and how online communities should be built, but scanty on grievance redressal on social media. This research is, therefore, likely to be useful to service providers who are inclined to improve their grievance handling mechanisms, as well as, to regulatory authorities and ombudsmen.

This paper investigates the dynamic pricing strategy of a firm for the successive-generation products under the conditions of the limited trade-in duration and strategic customers. Further, it explores the effect of a limited trade-in duration on the choice of the myopic and strategic customers, besides the optimal dynamic pricing and trade-in strategy of the firm.

Based on the choice behavior of the myopic and strategic customers, the authors have developed a two-period game-theoretic analytical model to decide the optimal retail prices of the successive-generation products and the optimal trade-in rebate when the firm adopts a dynamic pricing strategy and then investigate three extensions of the basic model to discuss the change in the results owing to the relaxation of certain conditions.

The authors find from the results that, in terms of profit maximization, it is better to extend the limited trade-in duration, and hence, the firm should implement a dynamic pricing strategy. However, in the situation of using a static pricing strategy, the firm should extend the limited trade-in duration only if the incremental value of the new generation products is below a certain threshold. Moreover, the firm should use a dual rollover strategy instead of a single rollover one. If all customers in the market are myopic, then the firm should also extend the limited trade-in duration.

This study mainly discusses the impact of limited trade-in duration on the firm's dynamic pricing strategy when facing strategic customers, which provides several directions for future research. First, if the government offers subsidies to consumers, how will strategic consumers make purchase decisions? How would the enterprise make its pricing decision? Second, when asymmetric information exists between consumers and firms, how will it affect consumers' choice behavior and firms' pricing decisions? All these issues are worth exploring in the future.

These results offer certain managerial insights for the firm in the decision making on pricing within the trade-in program.

This is the first work to study the dynamic pricing strategy of the firm for the successive-generation products under the conditions of the limited trade-in duration and strategic customers. Further, this work discusses the changes in results owing to the relaxation of certain conditions.

Prior research has produced conflicting results on the relationship between firm-level patenting activity and financial performance. To identify a factor that impacts the results, this study tests whether the level of customer-base concentration (defined as focusing on a small number of major customer sales transactions) changes the relationship between firm-level patenting activity and financial performance (return on assets: ROA).

Using a longitudinal secondary dataset from Japanese manufacturers from 1991 to 2016, this study investigates the interaction effect between firm-level patenting activity and customer-base concentration. With additional analysis using multiple profitability measures, this study provides robust evidence that customer-base concentration is an important factor in changing the relationship between firm-level patenting activity and financial performance.

The analysis results show that there is a positive relationship between firm-level patenting activity and ROA. In addition, this relationship is positively moderated by the customer-base concentration. This means that suppliers can improve the performance of the patenting activity by concentrating on their customer base.

By identifying a moderating factor between patenting activity and financial performance, this study advances the interpretation of conflicting results in patent research. Moreover, this study reveals a situation where customer-base concentration, which has a direct negative impact on financial performance, leads to better financial performance. This also indicates that firm-level patenting activities may compensate for the negative aspects of customer-base concentration.

This research explores mitigating carbon emissions and integrating sustainability in e-commerce logistics by optimizing the multi-depot pollution routing problem with time windows (MDPRPTW). A proposed model contrasts non-collaborative and collaborative decision-making for order assignment among logistics service providers (LSPs), incorporating low-carbon considerations.

The model is substantiated using improved adaptive large neighborhood search (IALNS), tabu search (TS) and oriented ant colony algorithm (OACA) within the context of e-commerce logistics. For model validation, a normal distribution is employed to generate random demand and inputs, derived from the location and requirements files of LSPs.

This research validates the efficacy of e-commerce logistics optimization and IALNS, TS and OACA algorithms, especially when demand follows a normal distribution. It establishes that cooperation among LSPs can substantially reduce carbon emissions and costs, emphasizing the importance of integrating sustainability in e-commerce logistics optimization.

This paper proposes a meta-heuristic algorithm to solve the NP-hard problem. Methodologies such as reinforcement learning can be investigated in future work.

This research can help logistics managers understand the status of sustainable and cost-effective logistics operations and provide a basis for optimal decision-making.

This paper describes the complexity of the MDPRPTW model, which addresses both carbon emissions and cost reduction. Detailed information about the algorithm, methodology and computational studies is investigated. The research problem encompasses various practical aspects related to routing optimization in e-commerce logistics, aiming for sustainable development.

This paper examines whether and how customers' annual report tone affects suppliers' innovation decisions.

Using the data from disclosed information on top five customers and annual report tone by Chinese listed firms, this paper used a two-way fixed effect model and intermediary effect model tests to explore the impact of customers' annual report tone on suppliers' innovation decisions.

The results indicate that the more positive the tone of customer annual reports is, the higher the suppliers' technological innovation level. The customers' annual report tone affects suppliers' innovation decisions through alleviating financing constraints and reducing the bullwhip effect. In addition, the authors find that the worse the supplier's bargaining power and the higher the customer's media coverage, the more significant the impact of positive customer annual report tone on the level of corporate technological innovation.

For downstream customers, to improve the quality of their text information disclosure. For upstream suppliers, the tone of customers' annual reports has incremental information, so the attention to customers' text information should be strengthened. As far as the market is concerned, it is recommended that regulators should strictly require the quality of text information disclosure and introduce relevant penalty mechanisms better to regulate the quality of corporate text information disclosure.

To the best of the author's knowledge, this paper is the first to expand the research related to textual information from a supply chain innovation perspective. The textual information can provide incremental information, and spillover effects may occur among supply chains, affecting suppliers' innovation decisions. And it clarifies the specific mechanism by which the supply chain tone spillover effect affects corporate innovation, enriching the relevant research on supply chain influence mechanisms.

The purpose of this paper is to examine the nuanced effects of downstream complexity on supply chain resilience, based on portfolio theory and normal accident theory. Intelligent manufacturing is considered to clarify their boundary conditions.

The ordinary least squares regression was conducted, based on the data collected from 136 high-tech firms in China.

Horizontal downstream complexity has a positive effect on supply chain resilience significantly, while the negative impact of vertical downstream complexity on supply chain resilience is not significant. Contingently, intelligent manufacturing plays a negative moderating role in the relationship between horizontal downstream complexity and supply chain resilience, while it positively moderates the relationship between vertical downstream complexity and supply chain resilience.

This study disentangles the nuanced effects of both horizontal and vertical downstream complexity on supply chain resilience, based on portfolio theory and normal accident theory. It also clarifies their boundary conditions by considering the focal firm's intelligent manufacturing level as the contingent factor.

Hotels are considered one of the keys to tourism industry, without which it is impossible to visualize this industry. Setting the proper price for hotels has always been a nuisance for the decision makers because of its direct relationship with the demand for hotels. Thus, in the current study a Stackelberg game between the government (leader) and the hotels (follower) has been presented to determine the optimal price under competitive conditions. The selected hotels are different with respect to energy consumption and the environmental impact. Thus, the government makes efforts to control their prices with incentives and tariffs.

The fuzzy inference system (FIS) has also been applied to forecast the hotel demand. Therefore, first off, the demand forecast criteria have been chosen by the experts and in the continuation, it has been screened by fuzzy Delphi approach. Finally, the quantity of hotel demand is computed by the Mamdani inference system. A mathematical model has been presented for determining the optimal sequencing of hotels and minimizing the searches to find a hotel.

A case study based on the data extracted from online travel agencies (OTAs) has been presented to validate the proposed model. The results demonstrate that by the ranking position increase, the number of the tourists decreases and the higher the star number of a hotel, the lower its ranking position.

Considering the energy saving and environmental impacts in hotel pricing and considering the government's intervention in hotel revenues regarding the incentives and tariffs are the innovations of the present study.

In online shopping, there is often a risk due to the inability to check the products. As a result, it can reduce the impact of factors affecting the intention to buy online, which is a significant point in e-commerce. The purpose of this research is to investigate the impact of the evidence and the perceived usefulness of a store site on the existing risk and online participation of customers for online shopping.

The statistical population of this research is people who have had the experience of online shopping at the Digikala store. The research method was descriptive-survey and the samples were randomly selected. A questionnaire was also used to collect information. Structural and statistical analysis of the model was done by SmartPLS3 software.

In this research, the authors found that the perceived usefulness (benefits of the site) has a favorable effect on the online participation of customers. It can also reduce the risk of online shopping. This research suggests good ways to increase sales to those who have a store site. The present research provides useful findings for those who sell their products online.

This research specifically examines the interaction of customers with sellers and can be a help for progress in e-commerce. In this study, perceived risk and participation as mediating variables, information and perceived usefulness on the site as independent variables and online shopping intention a dependent variables. This research was done about Digikala's online store. The results were obtained with the participation of customers in completing the questionnaire. Then the theoretical model and the background of the research are examined and then the hypotheses are evaluated according to the statistical results and finally, the research results and limitations are stated.