Market Context Research Articles

Intersection of Islamic Values and Consumer Preferences

This study aims to explore the intersection of Islamic values and consumer preferences within the realm of marketing, particularly focusing on the implications for businesses operating in Muslim-majority markets or targeting Muslim consumers worldwide. The research design adopts a qualitative approach, utilizing thematic analysis to synthesize recent literature on Islamic marketing and consumer behavior. The findings reveal that Islamic principles such as halal authenticity, ethical considerations, and cultural sensitivity significantly influence consumer decision-making processes across various product categories. Additionally, the study highlights the growing importance of understanding the impact of technological advancements, regulatory developments, and socio-cultural shifts on consumer behavior and market dynamics within the context of Islamic marketing. The implications of these findings underscore the need for businesses to prioritize halal authenticity, ethical sourcing, and cultural sensitivity in their marketing strategies to build trust, credibility, and loyalty within Muslim communities. This study contributes to the advancement of theory and practice in Islamic marketing, informing strategic decision-making and promoting inclusive and ethical approaches to consumer engagement in the global marketplace.

Inclusion for LGBTQ talent: a practice theory approach

PurposeThere is an emerging interest in Lesbian, Gay, Bisexual, Transgender and Queer (LGBTQ) inclusion among researchers and practitioners. However, the interplay of macro-, meso- and micro-level factors that influence the behavior of various agencies, systems, structures and practices in different national, cultural and social contexts still needs to be researched. This paper aims to examine how organizations meaningfully engage with the marginalized and underrepresented workforce, especially the LGBTQ community, to promote diversity and inclusion through comprehensive policies and practices, thereby developing a sustainable inclusivity culture.Design/methodology/approachAdopting a practice theory lens and using a case study design, including multilevel interviews with 28 different stakeholders, this study examines how organizations institutionalize LGBTQ inclusion practices in an emerging market context with a historically low acceptance of the LGBTQ community.Findings Findings indicate that macro influences, such as regulatory, societal and market pressures and adopting international standards and norms, impact meso-level structures and practices. At the organizational level, leadership evangelism and workforce allyship serve as relational mechanisms for institutionalizing LGBTQ-inclusive practices. Furthermore, collaboration, partnerships and enabling systems and processes provide the structural frameworks within which organizations build an LGBTQ-inclusive culture. Lastly, at the micro level, cisgender allyship and the LGBTQ micro work environments provide the necessary psychological safety to build trust for authentic LGBTQ self-expressions. This study also indicates that organizations evolve their LGBTQ inclusion practices along a trajectory, with multiple external and internal forces that work simultaneously and recursively to shape HRM policies and practices for building an inclusive culture.Originality/valueThis study addresses the significant gaps in diversity and inclusivity research on LGBTQ employees and contributes to the literature in three significant ways. First, this study examines the diversity management mechanisms at the organizational level and explicates their interplay at the micro, meso and macro levels to create congruence, both internally and externally, for engaging with LGBTQ talent. Second, this study adopts a practice theory lens to examine the behavior of various actors, their agencies, the “flow” of underlying and emerging structures and processes, the continuous interplay between structure and action and how they enable inclusive culture for the LGBTQ community as a whole. Last, it addresses the call by diversity researchers for context-specific multilevel research design, including qualitative research, focusing on national, cultural and institutional contexts, where socio-organizational and historical factors and interactions among them shape diversity practices. Much of the literature on LGBTQ inclusion has, thus far, been within the Western context. By examining the emergence of inclusion practices in emerging markets like India, this study contributes to diversity and inclusion research.

Renewable Energy Policy and Legitimacy: a Developing Country Case

This paper analyzes some policies to promote renewable energy technologies in order to level the playing field in the energy sector. It is argued that although there are several energy policy options to be considered, their successful implementation depends on specific market and institutional contexts in any given country. Ecuador´s electricity sector is presented as a case study. By extending the concept of industry legitimacy this study makes contributions to institutional and strategic change theories. The Ecuadorian case has important implications for policy makers and energy managers because it shows how a renewable energy industry can be developed in a regulated electricity market.

Do diverse board structures affect intellectual capital? Transitional economic evidence

This study investigates the impact of board structures on the intellectual capital (IC) of Vietnamese commercial banks from 2007 to 2020, utilizing the generalized method of moments (GMM). The results indicate a significant negative relationship between board independence and IC. Moreover, the findings suggest that IC in financial institutions is influenced by the size of the board and the level of gender diversity. However, the research revealed that diverse foreign members on the board did not significantly impact IC in banks. Although this study revisits the correlation between board structures and IC, it is the inaugural endeavor to investigate the role of board structures in augmenting IC within an emerging market context. The conclusions drawn from this research illuminate how board structures serve as a governance mechanism for heightening IC. The research findings are expected to provide recommendations to bank managers in their ongoing efforts to achieve business excellence through implementing Total Quality Management.

Short-term and long-term effects of foreign direct investment on tax revenue: empirical evidence from an emerging economy

PurposeThis paper examines the short- and long-run effects of foreign direct investment (FDI) on tax revenue in Ghana.Design/methodology/approachThe paper adopts the autoregressive distributed lag approach to estimate FDI’s long-run and short-run effects on tax revenue. The study uses time-series data from 1983 to 2019 for Ghana, mainly obtained from The Bank of Ghana, the World Bank and the IMF.FindingsThe results show that, in the short-run, FDI has no significant effect on direct tax revenue and total tax revenue but significantly hurts indirect tax revenue. In the long run, however, the results show that FDI has significant positive effects on indirect tax revenue and total tax revenue but no significant effect on direct tax revenue.Originality/valueEmpirical studies often fail to analyse the short-run and long-run effects of FDI on tax revenue. This study contributes to the mixed literature by analysing the short-run and long-run effects of FDI on tax revenue in an emerging market context. Additionally, this study employs three tax revenue measures in analysing the nexus.

Customer engagement strategies within family businesses in emerging economies: A multi-method study

Customer Engagement (CE) research has been growing considerably over time, however, most of this research emanates from developed economies and large organizational contexts. Little is known about CE strategies when it comes to family-owned businesses in emerging market context. This paper tries to synthesize the extant literature on CE to identify the state of research concerning CE practices followed in family-owned businesses from emerging markets. To achieve this objective, we first conducted a structured literature review of CE research published in last two decades in the top tier management and marketing journals. Second, drawing from the insights received from the findings of the structured review, we design a structured interview schedule to collect first-hand data from the managers working in family-owned businesses in India. This rich data is then analyzed using a qualitative data analysis package. Findings from our analysis reveal that social media platforms, continuous monitoring of the customer feedback and reviews, importance to non-financial goals, safeguarding the reputation, and maintaining the trustful relationship are critical aspects of family-owned firms’ CE strategy. Another interesting aspect about the CE strategies in family businesses vis-à-vis non-family-businesses, specifically from an emerging country context is the reliance on the third-party agencies to manage the content creation and digital marketing. Study findings not only help advance the scholarship within the twin fields of customer engagement and family business, but also provide valuable insights to the practitioners of family-owned businesses.

Model design to investigate the role of augmented reality technology on contextual marketing: a system dynamics approach

In E-commerce, one of the most essential reasons of company failures is conveying the right content of their product to the customers at the wrong time. This could include the company inability to adapt to their environment dynamically. With the existence of special circumstances like COVID-19 pandemic, companies require launching new techs, if they are to grow and stay competitive, and since the technology and related factors have a major effect on marketing success, “Systems Dynamic” sounds like a suitable tool in this regard. This article discusses augmented reality (AR) roles in contextual marketing (CM) using a system dynamics approach. After the identification of the key variables in both augmented technology and CM fields, casual relationships are defined. Then, the mathematical functions between these variables are extracted and the behavior of each factor is analyzed. Finally, based on this behavioral model, various scenarios are simulated to demonstrate how augmented reality technology can enhance the CM.

The impact of supply chain strategy on supply chain transparency over the firm lifecycle: evidence from China

PurposeIn this study, the authors investigate whether supply chain (SC) strategies (lean or agile) improve or hinder the supply chain transparency (SCT) and what factors affect this relation.Design/methodology/approachThe authors measure the level of SC strategy using natural language processing based on the annual financial reports of listed firms. Secondary data analysis is conducted on various databases encompassing 1,241 listed firms in China from 2011 to 2020. Additional tests are performed to assess the robustness of the results, and alternative explanations are duly considered.FindingsThe authors find that firms with an advanced level of SC strategy perform better on SCT. Furthermore, the authors observe that Agile SC strategy and Lean SC strategy have different effects on SCT over a firm’s life cycle. Agile SC strategy (the ratio of the proportion of Agile SC strategy word frequency divided by the proportion of Lean SC strategy word frequency greater than 1) has a significantly positive effect on SCT in the maturity stage; Lean SC strategy (the ratio less than 1) has a positive effect on SCT in the growth and decline stages. An increase in online media coverage negatively moderates the impact of the SC strategy (frequency of Lean and Agile SC strategy-related keywords) on SCT in the maturity stage. An increase in government environmental subsidies positively moderates the impact of SC strategy on SCT in the maturity and decline stages. Additionally, an increase in industrial competition intensity positively moderates the impact of the SC strategy on SCT in the decline stage.Originality/valueThe authors' study contributes to the Operations and Supply Chain Management (OSCM) literature by revealing the positive impact of SC strategy on SCT with objective secondary data. Additionally, the authors examine the moderating effects of moderators over the lifecycle of a firm on this relationship in an emerging market context. The authors' findings offer valuable guidance to companies operating in diverse market environments, providing actionable insights to strengthen their SC strategies and enhance SCT.

Understanding consumer herding behavior in online purchases and its implications for online retailers and marketers

Herding behavior refers to the imitation of the behavior of the masses. The primary purpose of this study is to determine how herding affects digital purchases. It is an under-researched area in the marketing and consumer behavior context. First, one needs to understand the circumstances under which one is likely to herd. Second, it is yet to be understood how the effect of observing others' buying behavior transmits into herding. Third, there needs to be a better understanding of the role of price in one's tendency to herd. An online experiment with 541 participants was conducted using a 3 (consumer ratings/sales volume/control) × 2 (high involvement product/low involvement product) between-subjects design. LED TV was selected as a high-involvement product and ball pen pack was selected as a low-involvement product. This research study combined three theories to propose a holistic herding model. It used the social impact theory to capture its persuasive impact, the information adoption model to analyze the acceptance of herding information, and the cue utilization theory to assess the inference from herding information in consumer decision-making. Results indicated significant negative moderating effects of perceived price and product knowledge and the positive moderating effect of perceived uncertainty between the relationship of herding cues (star rating and the number of products sold) and herding behavior. In addition, the mediating results of perceived information usefulness and perceived quality were also significant. This herding model provides a holistic view of the herding process beyond limiting it to an attempt to follow others blindly. Therefore, this study has both theoretical as well as managerial implications.

Protection against unfair competition in the European Union: from divergent national approaches to harmonized rules on search result rankings, influencers and greenwashing

Abstract This article provides an overview of the complex interplay between harmonized rules of unfair competition law at EU level and national approaches in the Member States. It discusses case law, sheds light on the objectives underlying protection against unfair competition and describes intersections with intellectual property rights. The analysis addresses general clauses that allow unfair competition law in the EU to keep pace with constantly changing marketing practices. It discusses the concept of confusion from a comparative trademark and unfair competition law perspective. Moreover, misleading practices, discrediting and denigrating allegations, slavish imitation, unfair free-riding, trade secret rules and transparency obligations will be explored. The analysis includes recent extensions of the canon of unfair competition rules, in particular in the field of product rankings within search results, influencer marketing and greenwashing. Particular attention will also be devoted to the growing body of transparency obligations in online marketing contexts, including obligations in the area of targeted behavioural advertising that follow from the Digital Services Act.

Would You Rather . . . a Simulation or a Client-Based Project? A Case Study of Experiential Learning Methods in Digital Marketing Analytics Courses

The rapid evolution of digital marketing, characterized by the surge in digital channels and data, has amplified the demand for graduates specialized in digital marketing analytics (DMA). Yet, marketing educators contend with insufficient theoretical guidance and practical pedagogical methods in the course design that aligns with industry expectations in teaching analytics within the novel context of digital marketing. Addressing this void, this paper presents a case study operationalizing a practice-informed model through two experiential learning methods—simulations and client-based projects–in teaching these courses. These methods, grounded in three practice-informed dimensions (i.e., knowledge, hard skills, and soft skills), are incorporated to structure two separate sections of a DMA course over distinct semesters. Preliminary findings from student data regarding actual and perceived learning outcomes suggest a slight edge for the simulation approach over the client-based project approach. However, our faculty reflection delves deeper into the benefits and challenges of both approaches. This case study aims to offer insights and recommendations for marketing educators considering incorporating a practice-informed approach integrated with experiential learning methods to teach their DMA courses.

MNE nonmarket strategy in a changing world: Complexities, varieties, and a values-based approach

Burgeoning complexity and variability in the political and social contexts in which multinational enterprises (MNEs) operate has led to increased research on MNE nonmarket strategy. This focus is enhanced by ethical concerns about the nonmarket practices of big business, particularly in institutionally fragile or nascent market contexts. In this introduction to our special issue on the complexities and varieties of nonmarket strategy, we review the extant research on MNE nonmarket strategy, specifically on corporate political activity and corporate social responsibility. Our review suggests that to address the complexities related to nonmarket strategy in a changing international context, corporate stewardship is inadequate and MNEs must adopt a more authentic and culturally embedded values-based nonmarket strategy approach, which can contribute to long-term advantage. Subsequently, we introduce and synthesize the papers in our special issue and present a research agenda for furthering scholarship on values-based nonmarket strategy.

Trust, Socioeconomic Factors, and Institutional Dynamics in Africa's Emerging Insurance Market

The study investigates the causality link between trust and insurance market development and the influence of socioeconomic and institutional factors on trust in insurance in the emerging market context. Relying on dual theories of bounded rationality and opportunism, VAR Granger Causality Test and the Two-Stage Least Squares (2SLS) analytical approaches, and a contextualised understanding of Africa's insurance market, the study finds a unidirectional causal link from trust to insurance market development, which suggests that market activities are driven by trust in insurance. Additionally, results show that trust in insurance is driven by socioeconomic and institutional factors in Africa’s insurance market. Against expectation, however, income inequality increases trust in insurance. But as expected, corruption reduces trust in insurance and human capital development increases it. The paper recommends reforms targeted at increasing trust within the socioeconomic and institutional framework. The findings provide useful insights into the growth paths of emerging insurance markets, which adds significantly to extant knowledge on the connection between trust and financial exchanges.

A Python-based Empirical Study of Markowitz's Portfolio Theory in the Chinese Stock Market

Markowitz's portfolio theory serves as a pivotal framework in investment science, offering investors a scientifically robust approach to asset allocation. This paper conducts an empirical analysis of the theory within the context of the Chinese stock market, utilising Python for data manipulation and computation. A selection of five stocks was made based on pre-established criteria, and Python was employed to examine their correlation for portfolio construction viability. Subsequently, Python facilitated the identification of the portfolio with the maximum Sharpe ratio and the minimum variance, as well as the delineation of the efficient frontier. Results from the empirical study confirm the applicability and utility of Markowitz's portfolio theory for Chinese investors, thereby substantiating its significant relevance in China's financial market. Based on the findings, investors are advised to diversify their portfolios following the guidelines of the efficient frontier to optimize returns while managing risks. Moreover, considering the complexities of the Chinese stock market, harnessing computational tools like Python can grant an analytical edge, enabling more informed decision-making. It's also pivotal for investors to routinely review and adjust portfolio compositions in line with market dynamics, ensuring they stay aligned with the principles of the efficient frontier.

The Current Status and Future Prospects of Machine Learning in the Chinese Stock Market

This article explores the current status and prospects of machine learning techniques in the stock market of china. It begins by providing an overview of the relevant concepts of machine learning and analyzing its strengths and weaknesses in the stock market context. The article also introduces the current application of machine learning techniques in the gobal stock market. In the methodology section, several machine learning models that have performed well in the stock market are listed. The article then summarizes and analyzes the specific applications of machine learning and its algorithms in the stock market of China, highlighting the advantages and limitations due to the unique characteristics of the market.Finally, the article concludes by summarizing the aforementioned content and providing an outlook on the future development direction of machine learning technology in the stock market of chinese. While the performance of machine learning technology in the stock market has its pros and cons, it is undeniable that it holds an important position in the future development of the financial market, especially within the wave of innovation and progress in the financial industry.

TiFLCS-MARP: Client selection and model pricing for federated learning in data markets

In recent years, the burgeoning data market has witnessed a surge in data exchange, playing a pivotal role in augmenting the predictive and decision-making capabilities of machine learning. Despite these advancements, persistent concerns surrounding data privacy have resulted in stringent limitations on data sharing and trading. Consequently, the data market is undergoing a transformative shift from pricing individual datasets to pricing the models themselves. The challenge of training high-performance machine learning models with restricted data from a single client is substantial. Federated learning has emerged as a popular solution, allowing collaborative model training without the need to transfer client data beyond local environments. However, training federated models within a data market introduces several challenges, including the effective selection of clients for model training and the optimization of utility through model pricing. In response to these challenges, we propose the Tiered Federated Learning Client Selection Algorithm (TiFLCS-MAR), employing a multi-attribute reverse auction approach. Integrated into the federated learning framework, TiFLCS-MAR excels at the comprehensive evaluation of client attributes, employing a tiered strategy to mitigate issues arising from client heterogeneity. Additionally, we introduce the TiFLCS-MAR Pricing Framework (TiFLCS-MARP), leveraging Nash equilibrium principles to maximize profitability for both clients and servers. Our framework accommodates the heterogeneity of diverse clients, efficiently selecting suitable candidates from a large pool, thereby boosting training efficiency and curbing model pricing costs. Empirical evidence showcases the efficacy of federated training with TiFLCS-MAR, demonstrating nearly double the convergence speed and a 5-10 percentage point improvement in accuracy across real and synthetic datasets. Furthermore, when compared to three baseline algorithms, TiFLCS-MARP substantially increases central server revenue by factors of 1.99, 27.05, and 1.78, highlighting its superior performance in the data market context.

Development of Integrated Tourism Destinations Based on Contextual Marketing vis-à-vis Digital Marketing

The tourism sector has experienced expansion, diversification, and intensification of sustainable management as one of the largest and fastest growing economic sectors in the world because it is developed in an integrated manner based on contextual or conventional marketing and virtual marketing, called digital marketing at once. This research article analyzes and explains the importance of contextual marketing as the basis of the value creation process for the development of integrated tourism destinations. The data, information, and knowledge regarding the focus on the research locus in the Province of South Sulawesi Indonesia were obtained through observations of several leading tourism destinations along with the author’s experience of visiting tourist destinations in the country and abroad as benchmarking. In addition, interviews and focus group discussions were conducted with stakeholders on tourism destinations. Qualitative descriptive analysis was carried out following an interactive model which included data collection, data condensation, data presentation, and verification or concluding. The results of the study found that the development of integrated tourism destinations based on contextual marketing strategies is related to generic marketing functions that are applied to strengthen tourism functions as a leading sector of sustainable multi-dimensional development. The development is carried out contextually or conventionally and virtually or referred to as tourism marketing in the air while still revitalizing the content, context, and infrastructure of tourism marketing. The success of integrated tourism development in the eyes of the public can be seen from the existence of social learning and knowledge creation processes that form a harmonious partnership-collaborative relationship in a Hexa-helix pattern. The basic values and value orientation of integrated tourism destination development both contextually and virtually or digitally marketing can be achieved when togetherness has become a necessity that is practiced. The effectiveness of its implementation is carried out - at least - through the 7-C’s public relations communication (PRC) approach which consists of credibility, context, content, clarity, continuity and consistency, channels, and capability of the audience as the basis of digital marketing.
 Keywords: tourism destinations, marketing mix, contextual marketing, digital marketing, hexa-helix

Carbon trading price forecasting in digitalization social change era using an explainable machine learning approach: The case of China as emerging country evidence

Carbon trading prices are considered important reference indicators for policy formulation and enterprise decision-making, and play an important role in low-carbon development. However, predictive approaches that balance the accuracy and interpretability of carbon trading prices remain limited. This study proposes a hybrid approach that integrates group variable selection regularization and uncertainty inference in Bayesian neural networks (BNN) with inherent interpretability to predict carbon trading prices. We also identify effective predictive indicators in an emerging market context. Based on the carbon data of emerging markets, a dataset of price information was constructed for model training. The results indicate that under the same regularized dataset, all BNN results surpass other artificial neural networks (ANNs). The group smoothly clipped absolute deviation-BNN performs best in all models and has interpretability. The model effectively identified new influencing factors for predicting carbon trading prices in emerging countries, including coal prices and blockchain-related information. The proposed new prediction approach provides a new basis for the prediction and evaluation of carbon trading prices and provides the necessary references for policy formulation in the digitalization era of social change.

Does Corporate Social Responsibility Expenditure Influence Tax Avoidance? Evidence from Tanzanian Listed Firms

This study examines the relationship between corporate social responsibility expenditure and tax avoidance in Tanzanian listed firms, utilising secondary data from 25 firms listed on the Dar es Salaam Stock Exchange over a ten-year period from 2013 to 2022. Employing a retrospective research design and purposive sampling, we applied the fixed effects model as the estimation technique. The analysis reveals a positive and significant association between corporate social responsibility expenditure and tax avoidance, indicating that firms with higher levels of corporate social responsibility expenditure are more likely to engage in tax avoidance practices. Furthermore, control variables such as firm size, profitability, and leverage also demonstrated positive and significant relationships with tax avoidance. The findings contribute to the understanding of the intricate dynamics between corporate social responsibility initiatives and tax management strategies in the Tanzanian context. The implications of this study extend to policymakers, suggesting the need for a nuanced approach to corporate tax regulation, taking into account the interplay between corporate social responsibility initiatives and tax management practices. Policymakers should recognise that firms engaging in higher corporate social responsibility activities may employ tax planning strategies that warrant attention in regulatory frameworks. This study contributes to the ongoing discourse on the complex dynamics between corporate social responsibility and financial decision-making in emerging market contexts.

Determinants on Successful Investment of Venture Capitalist

Venture capital (VC) has transcended its Silicon Valley origins to become a global force shaping the innovation and economic landscape. It is imperative to recognize the critical role of VC and improve its success rate. Employing the “jockey-horse-meadow” model, this paper identifies three crucial determinants influencing VC success: the entrepreneurial team, strategic approach and business model, and the market context. Delving into specific sub-points, the paper underscores the unique significance of each determinant, substantiating their pivotal roles in shaping VC outcomes. This research not only contributes insights into the success dynamics of VCs but also serves as a valuable reference for startups seeking financing and development. Additionally, it offers guidance to investors navigating the selection process, aiding them in identifying startups with genuine potential for impactful investment. By shedding light on these fundamental factors, this paper enriches the discourse surrounding VC success, fostering a more informed and strategic approach in the dynamic world of venture capital.