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When customers make their purchase decisions, they use all the available information from all the initiatives and behaviors that companies carry out with their stakeholders. This research aims to identify whether a company's financial performance and reputation determine the customer's perception of the company, which affects their engagement. This study is based on the theories of engagement, stakeholder and signalling. Are customers engaged solely based on their feeling of satisfaction, or do employees and brand value play a role in this engagement?

Secondary data was collected from 14 automotive companies and empirically tested through a longitudinal study over the period 2010–2018. For panel data analysis this study used weighted least squares.

The variables proposed in this research, firm value and corporate reputation, were significant for the analysed panel sample. Furthermore, employee satisfaction influences customer engagement as an independent and moderating variable, just like brand value.

This research contributes to the emerging stream of customer engagement research by combining insight as a company-initiated resource, with the sheer transaction, integrating data obtained from employees and customers in an international context.

This study aims to develop an empathetic and user-centric customer support service design model. Though service design has been a critical research focus for several decades, few studies focus on customer support services. As customer support gains importance as a source of competitive advantage in the present era, this paper aims to contribute to industry and academia by exploring the service design model.

The study adopted a theories-in-use approach to elucidate mental models based on the industry’s best practices. In-depth interviews with 62 professionals led to critical insights into customer service design development, supported by service-dominant logic and theory of mind principles.

The ensuing insights led to a model that connects the antecedents and outcomes of empathetic and user-centric customer service design. The precursors include people, processes and technology, while the results are user experience, service trust and service advocacy. The model also emphasises the significance of the user’s journey and the user service review in the overall service design.

The model developed through this study addresses the critical gap concerning the lack of service design research in customer support services. The key insights from this study contribute to the ongoing research endeavours towards transitioning customer support services from an operational unit to a strategic value-creating function. Future scholars may investigate the applicability of the empathetic user service design across cultures and industries. The new model must be customised using real-time data and analytics across user journey stages.

The empathetic and user-centric design can elevate the customer service function as a significant contributor to the overall customer experience, loyalty and positive word of mouth. Practitioners can adopt the new model to provide superior customer service experiences. This original research was developed through crucial insights from interviews with senior industry professionals.

This research is the original work developed through the key insights from the interview with senior industry professionals.

This research explores the mediating role of a firm's innovativeness and customer orientation on the relationship between commercial success and total quality management (TQM) in the Indian SaaS (Software-as-a-Service) sector.

The hypotheses suggested for this study were validated using partial least squares-based structural equation modeling on data collected from 189 executives working in SaaS companies in India.

The findings of this study revealed that a SaaS company's innovativeness fully mediates the relationship between TQM and corporate performance, whereas the customer orientation of SaaS companies partially mediates the relationship between TQM and corporate performance.

Findings of this paper indicate that, in addition to TQM deployment, SaaS companies' innovativeness and customer-focused strategy will improve their corporate performance. With minimal research focusing on India, this study may be considered a pioneer work. It can serve as a basis for SaaS company promoters to improve their corporate performance by implementing TQM processes.

This chapter provides an overview of the strategic implications of chatbot use and implementation, including potential applications in marketing, and factors affecting customer acceptance. After presenting a brief history and a classification of conversational artificial intelligence (AI) and chatbots, the authors provide an in-depth review at the crossroads between marketing, business, and human–computer interaction, to outline the main factors that drive users' perceptions and acceptance of chatbots. In particular, the authors describe technology-related factors and chatbot design characteristics, such as anthropomorphism, gender, identity, and emotional design; context-related factors, such as the product type, task orientation, and consumption contexts; and users-related factors such as sociodemographic and psychographic characteristics. Next, the authors detail the strategic importance of chatbots in the field of marketing and their impact on consumers' perceived service quality, satisfaction, trust, and loyalty. After discussing the ethical implications related to chatbots implementation, the authors conclude with an exploration of future opportunities and potential strategies related to new generative AI technologies, such as ChatGPT. Throughout the chapter, the authors offer theoretical insights and practical implications for incorporating conversational AI into marketing strategies.

The role of Technology Transfer Units, examined in this study, was found to be vital to expedite the process of disseminating new varieties and their production technology.

A total of 1,000 households were surveyed in the sampled states. A probit model was used to analyse.

Age, education, land holding, tractor use and number of working family members in agriculture were found to significantly affecting adoption of the new seed varieties. Technology transfer through licensing has impacted the adoption of new seed varieties positively by highlighting Punjab possessing the highest adoption and western Uttar Pradesh was majorly adopting the old variety.

The authors believed in farmers’ memory to recall the varietal information of wheat.

The study recommended various incentives to attract the seed industry in UP to minimize the economic loss potentially suffered by them.

Quality seeds are germane to increase the productivity of crops, and it is paramount to disburse the seed varieties to the end users in an efficient way to achieve the overall objective of productivity enhancement.

In this context, a study was conducted in three states of India, namely, Punjab, Haryana and Uttar Pradesh (UP) to find out the adoption rate of newly developed varieties of wheat, HD 3086 after three years (2014–2015) of its commercialization by IARI as well as HD 2967, which was released in 2011.

Existing deterministic models that predict the capacity of corroded reinforced concrete (RC) beams have limited applicability because they were based on accelerated tests that induce general corrosion. This research gap was addressed by performing a combined numerical and statistical analysis on RC beams, subjected to natural corrosion, to achieve a much better forecast.

Data of 42 naturally corroded beams were collected from the literature and analyzed numerically. Four constitutive models and their combinations were considered: the elastic-semi-plastic and elastic-perfectly-plastic models for steel, and two tensile models for concrete with and without the post-cracking stresses. Meanwhile, Popovics’ model was used to describe the behavior of concrete under compression. Corrosion coefficients were developed as functions of corrosion degree and beam parameters through linear regression analysis to fit the theoretical moment capacities with test data. The performance of the coefficients derived from different combinations of constitutive laws was then compared and validated.

The results showed that the highest accuracy (R² = 0.90) was achieved when the tensile response of concrete was modeled without the residual stresses after cracking and the steel was analyzed as an elastic-perfectly-plastic material. The proposed procedure and regression model also showed reasonable agreement with experimental data, even performing better than the current models derived from accelerated tests and traditional procedures.

This study presents a simple but reliable approach for quantifying the capacity of RC beams under more realistic conditions than previously reported. This method is simple and requires only a few variables to be employed. Civil engineers can use it to obtain a quick and rough estimate of the structural condition of corroding RC beams.

Incorporating pre-existing crack in service life prediction of reinforced concrete structures subjected to corrosion is crucial for accurate assessment, realistic modelling and effective decision-making in terms of maintenance and repair strategies.

An accelerated corrosion test was conducted by using impressed current method on cylindrical specimens with varying cover thickness and crack width. Mechanical properties of the specimens were evaluated by tensile tests.

The results show that, the pre-cracked samples exhibited shorter concrete cover cracking times, particularly with wider cracks when compared to the uncracked samples. Moreover, the load-bearing capacity of the reinforcement bars decreased owing to the pre-cracks, causing structural deflection and a shortened yield plateau. However, the ductility index remained consistent across all sample types, implying that the concrete had good overall ductility. Comparing the results of the non-corroded rebar and corroded rebar samples, the maximum reduction in the yield load was 25.22%, whereas the maximum reduction in the ultimate load was 26.23%. The simple mathematical model proposed in this study provides a reliable method for predicting the chloride ion diffusion coefficient in cracked concrete of existing reinforced concrete structures.

A simple mathematical model was proposed for evaluation of the equivalent chloride ion diffusion coefficient considering crack width, average crack spacing and crack extending lengths for cracked reinforced concrete structures, which is used to incorporate existing crack in service life prediction models.

This study aims to explore the synergistic effect of oxide nanoparticles (ZnO, Fe₂O₃, SiO₂) and cerium nitrate inhibitor on anti-corrosion performance of epoxy coating. First, cerium nitrate inhibitors are absorbed on the surface of various oxide nanoparticles. Thereafter, epoxy nanocomposite coatings have been fabricated on carbon steel substrate using these oxide@Ce nanoparticles as both nano-fillers and nano-inhibitors.

To evaluate the impact of oxides@Ce nanoparticles on mechanical properties of epoxy coating, the abrasion resistance and impact resistance of epoxy coatings have been examined. To study the impact of oxides@Ce nanoparticles on anti-corrosion performance of epoxy coating for steel, the electrochemical impedance spectroscopy has been carried out in 3% NaCl solution.

ZnO@Ce3+ and SiO2@Ce3+ nanoparticles provide more enhancement in the epoxy pore network than modification of the epoxy/steel interface. Whereas, Fe2O3@Ce3+ nanoparticles have more to do with modification of the epoxy/steel interface than to change the epoxy pore network.

Incorporation of both oxide nanoparticles and inorganic inhibitor into the epoxy resin is a promising approach for enhancing the anti-corrosion performance of carbon steel.

Disruptive technologies are accelerating global growth. Artificial intelligence (AI) has the potential to transform the idea of delivering value to end users. On the other hand, the growth of Industry 5.0 has given rise to the concept of humanizing technology, and AI is a promising technology with the potential to contribute to business success. Nevertheless, the idea of value creation in the field of AI is novel, so it is necessary to define the meaning of value by understanding the context of AI applicability in different environments and industries. In this chapter, the author uses the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) procedure to conduct an SLR that provides interesting insights into the focus, industries, and methodologies and approaches used in existing research. Following the initial literature review on the state of the art of AI and value creation, the author also offers a reflection on the strategic implications of AI in the field of marketing, postulating a macrovalue creation framework that addresses the existence of implications on three different levels: emerging markets, Sustainable Development Goals, and adoption issues. Therefore, this chapter examines the value creation perspectives of AI to understand the current research focus and future directions.

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”