In the past two decades, search engine advertising (SEA) has evolved into a dominant form of digital marketing. Yet academic research in this domain remains fragmented across disciplines, highlighting the need for a comprehensive synthesis. Consequently, this study conducts a theory-context-method framework-based systematic literature review of 122 peer-reviewed articles published between 2021 and 2025, gathered from the Scopus database. The findings reveal a major theoretical deficit, with over 75% of studies lacking a formal framework. Contextually, SEA research clusters heavily within China and the US, leaving developing regions underrepresented and themes like privacy, ad design, and big data underexplored. Methodologically, while approaches range from econometric models to deep learning, quantitative designs heavily dominate (n = 87), leaving qualitative and mixed-methods research scarce. Additionally, this review proposes a future research agenda incorporating various theories (information foraging theory) and methods (goal programming) to advance scholarly and practical SEA understanding.

Purpose Given the multifaceted nature of research in green advertising (GA), it is critical to holistically review the extant literature to understand their contributions. This study aims to perform a hybrid literature review by combining citation analysis and a theory-context-method (TCM) framework to map the conceptual development of GA in the past three decades. Design/methodology/approach Using the SPAR-4-SLR technique, the current study identified the literature contributions of 133 articles published between 1993 and 2024 in the Scopus and Web of Science databases. Findings The study’s findings identified that certain theories, such as attribution theory and signalling theory have been used frequently in investigating the influence of GA on consumer behavior. The context analysis identified emerging themes like green trolling and green demarketing. This study also proposes substantial avenues with alternative theories for further advancement of GA research. Research limitations/implications By conducting a hybrid literature review, this study identifies the highly cited articles and sources and provides a comprehensive synthesis of GA research. Practical implications Analysis of 133 articles from eight subject areas identified various themes in GA, aiding future researchers in identifying potential research opportunities and for marketers to develop appropriate strategies for their GA campaigns. Originality/value This study is the initial attempt to perform a hybrid literature review on GA using citation analysis and TCM framework.

The dynamicity of the technology market and varied consumer tastes make the technology product market highly unpredictable and complex. Besides, due to competition and fast breakthroughs in the technology market, it can be observed that in recent years, the product life cycle has shortened significantly. It created immense pressure on managers to develop inventory policies corresponding to actual market realities. Economics order quantity (EOQ) models are often used to develop inventory policies. However, due to the variable nature of the demand rate function of technology products, the traditional EOQ models may not be useful for developing replenishment policies for technology products. In addition to the consumer adoption process, inventory managers also face the challenge of imperfect quality products while strategizing business policies. Imperfect quality products can come from flawed transport and storage conditions, or they may come due to the faulty production process. Proper inspection or screening of the lot is important for removing the desired level of defective items before delivery to the customers. In this paper, we propose a new EOQ model for technology items with imperfect quality where the demand rate will follow life-cycle dynamics, and sales are treated as a function of product awareness, utility, and consumer affordability. To confirm the validity of the proposed framework, a numerical analysis is performed under different market conditions.

After the financial crisis, the Indian banking system has accumulated a mountain of bad loans which has crippled the banking sector and halted the credit flow to the industry. Several immediate causes for the bad loan crisis have been pointed out. However, poor market discipline, the ultimate root cause of the bad loan crisis, has not been paid adequate attention. This study seeks to investigate how effectively the market disciplinary forces, captured through information disclosure, interbank deposits, concentration and ownership structure, incentivise the Indian banks to adopt prudential risk management by enhancing their risk-weighted capital ratio. The findings of the study show that information disclosure and interbank deposits do not induce prudential risk behaviour among banks in India. However, with increasing concentration in the banking sector, a higher level of information disclosure effectively induces banks to maintain higher capital ratios, but inter-bank deposits do not have any significant effect on bank capital. We also observe that government banks maintain lower capital ratios as compared to private banks indicating government banks' higher expectation of government bailout.

This paper analyses the non-performing assets (NPA) crisis in the Indian banking system from the perspective of soft budget constraints. Using a panel dataset of 105 publicly listed firms, it explores the relationship between NPAs and bank lending behaviour, particularly examining credit rationing regarding firm size and risk level. The findings indicate that Indian banks favour large firms over smaller ones, while credit rationing is not adequately aligned with borrower riskiness. However, the Asset Quality Review (AQR) by the Reserve Bank of India and the introduction of the Insolvency and Bankruptcy Code (IBC) seem to have enforced risk-based lending to some extent. These results shed light on the systemic issues that drive NPAs, linking them to governance weaknesses and the prevalence of soft budget constraints.

Internet giants like Google, Facebook, and Amazon have relied heavily on revenue from online advertising sales in recent years. The Click-through rate of an advertisement is the percentage of people who clicked it out of those who viewed it. The CTR as a metric represents the performance of their online advertisements. For over two decades now, researchers in academia and industry have paid great attention to getting good CTR prediction accuracy because of the demand it has generated in the digital world. This paper reviews the scholarly literature on CTR prediction during the previous decade by a bibliometric analysis of 1051 publications from journals indexed by Scopus. The goals of this research are to (1) conduct a structured quantitative analysis of the bibliometric data, (2) chart the development of CTR Prediction research, and (3) identify the most recent and relevant research literature and viewpoints in the field. A handful of studies have been conducted on this subject, and they have presented an in-depth analysis of specific methods and learning models being applied for CTR prediction. In addition to the previously submitted studies, this literature review aims to provide an overall bibliometric analysis showing the evolution of techniques employed for CTR prediction in the articles published over the last ten years, using a combination of bibliographic coupling, citation, and co-citation. The outcome of this literature evaluation will aid future researchers in gaining a deeper comprehension of the scientific studies around CTR prediction.

This study aims to investigate the size–leverage relationship in the context of India—one of the important emerging economies. Most of the studies that have tested the relationship between firm size and leverage have been conducted in the developed economies. For testing the much-discussed size–leverage relationship, we employ a large sample of firms for the study over a time span of 17 years from 2002 to 2018. Our findings support the negative size–leverage relationship, confirming the propositions of the pecking order theory. The study has implications for policymakers regarding the development of corporate debt market in India.

Since the introduction of online advertising, an increasing number of advertisers and firms are relying on this ad type to acquire consumers, making it an important revenue source for search engines and other internet giants like Google, Amazon, etc. Clicks are used as a metric in two ways: (i) for advertisers, it is used to measure the effectiveness of an advertisement, and (ii) for internet companies, it is used as a quality metric for their search engine or website. The click-through rate is the ratio of the number of impressions to the number of clicks of an advertisement. Managers are using this metric to allocate the budget for their advertisement campaigns based on their effectiveness. In the last decade, due to the growing industry demand, it has attracted scholars from industry and academia. This literature review article examines click-through rate evolution from an empirical standpoint using the bibliometric methods, reviewing 596 articles from the Scopus index. This review article (i) identifies the most influential articles, authors, and journals, serving as a baseline for future research, and (ii) charts the evolution of the topic over the last decade, assisting managers and future researchers in gaining a performance-based comprehensive view of click-through rate.

This research work puts forward the inventory optimisation model for the high technology multi-generation products under the situation of limited warehouse storage space. It is assumed that the manufacturer has its own space which has a lesser opportunity cost of usage as compared to another rented space. Therefore, the manufacturer utilises the own storage space for the period of time where his own space is sufficient to keep the inventories. While the research work has been done earlier on various demand patterns under such a scenario, there is no research present under storage space constraints for the generations of innovative products whose demand follows the Norton Bass Model of Innovation Diffusion. This paper lays down such a model for inventory optimisation, but also puts forward a few theorems on the dynamics of inventory decisions, and also performs numerical illustrations of the proposed model.

In this age of digitalization, when every industry is undergoing technological disruption, there is a big role of digital gadgets and technology products. A key feature of these digital gadgets is the short length of the product life cycle, since the newer and more advanced generations of technologies are developed regularly to replace the earlier conventional technologies. The traditional EOQ models that assume a constant demand cannot be used here. This research paper formulates an inventory optimization model for the multi-generational products under the trade credits and the credit-linked and innovation diffusion dependent demand. The study also performs a numerical illustration of the proposed model, and establishes important dynamics among the key variables. It also performs the sensitivity analysis with the cost of credit and the trade credit period. The paper concludes with the managerial implications for the inventory practitioners and the possible areas of extension for this research in the future.

All supply chain around the world are established with an aim of reducing customer order cycle time, drive customer value and facilitate financial success. This also results in implementing effective service recovery strategies to achieve these goals. This paper identifies and analyses various factors which influences and contributes to service recovery process by exploring the existing literature on service recovery. Based on these factors, decision-making trial and evaluation laboratory (DEMATEL) method is illustrated to precisely measure causal relations between all factors and define the processes influencing these factors. The results of this study state that proactive recovery capability, communication from suppliers in form of early warnings, focus on service outcome failures and moment of truth are the key enabling factors for service recovery, while customer commitment level, original cost and placing inventory close to customers are the most direct influencing factors.

The high technology products come in generations, where the demand for newer technology generations is strongly influenced by the installed base of earlier generations (such as computers, cameras, notebooks, etc). However, the effect of technology substitution on inventory replenishment policies has received little attention in the supply chain literature. In the hi-technology market, consumers' purchasing capability, the utility of a product along with the entry of the advanced generation product influence the market expansion/contraction of the products. In this study, the impact of parallel diffusion of two successive generations' products on inventory policies of the monopolist has been analysed. The demand models have been characterised by considering the life-cycle dynamics for a P-type inventory system. The purpose of this paper is to develop a model for joint pricing and replenishment of technology generation products. The model has been solved by using a genetic algorithm technique. The impact of yearly price drop and the price sensitivity of demand on the profit margins vis-a-vis on replenishment policies has also been studied. The paper also brings forward the dynamics of the launch of newer generations and the pricing strategies on optimal inventory replenishment policies. Numerical illustrations have also been covered in the paper.

In this paper, the multi-period EOQ model is developed for the technology products that have multiple generations co-existing in the market, with each of them having a very short product life cycle. The paper first develops the framework for computation of inventory-related costs and then minimizes the total replenishment costs using random search technique and approximating the non-linear expressions while using Simpson’s Rule for integration. The paper also provides numerical illustrations and establishes a few important theorems that relate the EOQ to the innovation of diffusions. It is found that the total replenishment cost curve, drawn on the EOQ axis in the case of technology generations is convex to the origin. Since the objective function is highly non-linear, the genetic algorithm has been used to find the solution to the problem. The study also suggests that the faster diffusion of the next generations has a conflicting effect on the EOQ of the first generation in the case of pooled and non-pooled logistics.

The inventory policies for any product under the trade credit mechanism are influenced by the procurement price per unit and the credit period offered by the seller to the buyer. This paper develops an inventory model for the technology generations under the imprecise trade credit period and the imprecise procurement cost. It considers the demand that is credit-linked and governed by innovation diffusion as well. The imprecise nature of the parameters is captured by the use of fuzzy numbers. The trapezoidal membership function has been used to fuzzify the profit function with the imprecise parameters, and then the centroid method is used to de-fuzzify the profit. The numerical illustrations have been performed, followed by the sensitivity analysis with the launch timing of the second generation product. A few important implications for the inventory practitioners and the possible extensions of this work have also been discussed.

In this paper, a new Economic Order Quantity (EOQ) model for a successive generation of technology products has been discussed. The classical EOQ model is based on the assumption that the demand rate is constant. Hence it cannot be used for technology products where competition-substitution among products is a usual phenomenon. To address this problem, the EOQ model proposed in this article is considered a demand model for a technology product that follows the innovation-diffusion process. A numerical example has been illustrated and a comprehensive sensitivity analysis is conducted to understand the path of the optimal planning horizon and optimal costs under varied innovation and imitation effect. The sensitivity analysis of the introduction timing of the second generation has been performed to know the applicability of the model in actual circumstances. The behavior of the model has been discussed in detail in the numerical illustration section.

In this article, we discussed optimal replenishment policies for two succeeding generations' technology products under partial trade credit financing. It is often seen that in technology market, advanced generation product plays an important role in cannibalising the market of existing generation product. Thus, precise estimation of demand of technology generations' product is critical for taking any policy decisions. Demand estimation of technology products is a complex process, as the consumer buying behaviour of technology generational products is not only depends on marketing mix variables but also associated with the time-to-market phenomenon of new technologies. In technology market, interaction among users of different generational products controls the rate of substitution of older technology products with the new one. Therefore, due to the substitution nature of demand of multi-generation product, it is important to incorporate the interaction-substitution effect in replenishment policies for this kind of products. We used life cycle dynamics to project demand rates of technology generations. In this paper, we formulate the total cost function for five different situations depending upon the new generation introduction timing and length of the trade credit period. A detail sensitivity analysis is been performed to explore the efficacy of the model in a given situation.