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This study aims to follow a rigorous approach to identify, critically analyze and synthesize 75 papers published from 2000 to 2022.

The study presents a systematic literature review on identity expressiveness (IE), clarifying and expanding what is currently known about the concept.

To synthesize current knowledge on IE, the study uses the overarching framework of antecedents-phenomenon-consequences, using this same framework to identify gaps and future research directions. The findings show individual and brand-related factors such as the need for uniqueness and anthropomorphism as antecedents of IE, and eWOM/WOM, impulse purchases and upgrading to more exclusive lines as consequences of IE.

The study contributes to theory by synthesizing and mapping current understanding of the state of knowledge on the concept of IE while highlighting gaps in the extant literature and paving future research directions for scholars in the field.

The study offers useful insights for practitioners, broadening marketers’ actionable options in identity-based marketing. Marketers can use insights from this study to inform marketing strategy and communication campaigns for different types of brands.

To the best of the authors’ knowledge, this study is the first of its kind and offers an integrative review of the current literature on IE, thus enhancing understanding of the concept, its antecedents and consequences. The study also contributes to knowledge by highlighting future research priorities for researchers in this field of enquiry.

This study aims to explore the “psychological contract of safety” (PCS), a key factor in the safety climate (SC), which relies on the behavioral safety actions of workers at construction sites. While numerous factors have been identified in various sectors across different countries, there is a consensus among researchers that there is a dearth of common assessment factors specifically for the Indian construction industry (ICI). Therefore, this study undertakes a systematic review of existing literature to identify the factors that determine PCS in construction and to ascertain the relative importance index (RII) of these variables and their interrelationships using structural equation modelling (SEM).

A structured survey was conducted among 420 professionals in the ICI to collect data. This data was then analyzed using descriptive and inferential statistical methods to derive results.

The findings of the study indicate that PCS factors have a significant impact on the construction industry (CI). The inferential analysis ranks “Safety System” as the top factor with the highest RII value. The chi-square results highlight two key SC factors that enhance and regulate an organization’s safety performance. The SEM results reveal that SC factors contribute to the improvement of PCS and influence worker safety behavior.

The outcomes of this study will be beneficial for stakeholders aiming to improve safety at construction sites and enhance safety performance by fulfilling the mutual safety obligations of employers and employees and by improving safety norms, procedures and policy-making. This paper also provides a theoretical framework for scholars to reassess the results in various contexts.

Whilst a modest number of investigations have been undertaken concerning nanofluids (NFs), the exploration of fluid flow under exponentially stretching velocities using NFs remains comparatively uncharted territory. This work presents a distinctive contribution through the comprehensive examination of heat and mass transfer phenomena in the NF ND–Cu/H₂O under the influence of an exponentially stretching velocity. Moreover, the investigation delves into the intriguing interplay of gyrotactic microorganisms and convective boundary conditions within the system.

Similarity transformations have been used on PDEs to convert them into dimensionless ODEs. The solution is derived by using the homotopy analysis method (HAM). The pictorial notations have been prepared for sundry flow parameters. Furthermore, some engineering quantities are calculated in terms of the density of motile microbes, Nusselt and Sherwood numbers and skin friction, which are presented in tabular form.

The mixed convection effect associated with the combined effect of the buoyancy ratio, bioconvection Rayleigh constant and the resistivity due to the magnetization property gives rise to attenuating the velocity distribution significantly in the case of hybrid nanoliquid. The parameters involved in the profile of motile microorganisms attenuate the profile significantly.

The current simulations have uncovered fascinating discoveries about how metallic NFs behave near a stretched surface. These insights give us valuable information about the characteristics of the boundary layer close to the surface under exponential stretching.

The novelty of the current investigation is the analysis of NF ND–Cu/H₂O along with an exponentially stretching velocity in a system with gyrotactic microorganisms. The investigation of fluid flow at an exponentially stretching velocity using NFs is still relatively unexplored.

The Internet of Things (IoT), as one of the new digital technologies, has created wide applications in various industries, and one of the most influential industries of this technology is the transportation industry. By integrating the IoT with the transportation industry, there will be dramatic changes in the industry, and it will provide many entrepreneurial opportunities for entrepreneurs to develop new businesses. Opportunity identification is at the heart of the entrepreneurial process, and entrepreneurs identify innovative goods or services to enter a new market by identifying, evaluating, and exploiting opportunities. Despite the desire of transportation managers to invest in the IoT and the increase in research in this area, limited research has focused on IoT-based entrepreneurial opportunities in the transportation industry. Therefore, the present study aims to identify IoT-based entrepreneurial opportunities in the transportation industry and examine their importance.

To achieve the research objective, the authors applied a mixed approach. First, adapting the lens of the industry value chain theory, a comprehensive literature review, besides a qualitative approach including semi-structured interviews with experts and thematic analysis, was conducted to identify the entrepreneurial opportunities. The identified opportunities were confirmed in the second stage using a quantitative survey method, including the Student t-test and factor analysis. Finally, the identified opportunities were weighted and ranked using the best worst method (BWM).

Entrepreneurial opportunities are classified into five main categories, including “smart vehicles”, “business partners/smart transportation supply side”, “supporting services”, “infrastructures”, and “smart transport management and control”. The infrastructures group of opportunities ranked the highest amongst the identified groups.

This study adds to the digital entrepreneurship opportunity recognition literature by addressing opportunities in a smart industry propelled by digital technologies, including developing new products or new applications of the available technologies. Additionally, inspired by the industry value chain theory, this article develops a framework including various digital entrepreneurial opportunity networks which are necessary to add value to any industry and, thus, could be applied by entrepreneurs to recognize opportunities for new intermediaries to enter other digital-based industries. Finally, the present study identifies the IoT-based entrepreneurial opportunities in the smart transportation industry and prioritizes them, providing practical insights regarding the creation of entrepreneurial ecosystems in the field of smart transportation for entrepreneurs and policymakers.

This study aims to investigate earnings management practice of classification shifting of revenues in Chinese-listed firms.

The study employs a dataset of 2,920 A-listed firms from Chinese stock exchanges of Shanghai and Shenzhen for the period of 2003–2019. We apply both univariate and panel regression analysis by using fixed effect estimation with robust standard errors.

Our findings reveal that firms misclassify revenues by taking advantage of the flexibility provided by applicable financial reporting standards. The empirical evidence obtained through regression analysis suggest that managers reclassify non-operating revenues as operating revenue to alter the economic reality while seeking the advantage of financial reports users’ vulnerability for valuing the upper half of income statement items more as compared to lower part. The results further indicate that international financial reporting standards adoption inhibits the earnings management practices using classification shifting of revenues. It is also concluded that firms, which are suffering losses or having low growth, are more persistently involved in misclassification of revenues.

The study is unique from the point of view that it investigates earnings management from the prospective of revenue’s classification in an emerging market characterized by various market imperfections such as lower investor protection and higher information asymmetry.

This paper aims to focus on the issue of high employee turnover in the Indian tech industry. An integrative review is conducted to analyse the past and current state of literature, as well as prepare a research agenda for future studies.

A pool of 72 articles published between 2010 and 2022 is reviewed with a special focus on Indian tech employees. This study elucidates the extent and impact of employee retention strategies through content analysis.

Two broad perspectives have been established in the literature: the reasons for quitting and the explanations for staying. By means of a comprehensive review, this paper combines these two aspects of literature and suggests factors under organization’s control to retain competent tech employees.

The study is designed to integrate the two theoretical viewpoints of employee turnover literature by consolidating the reasons behind quitting behaviour and staying intention. Codes combining the two aspects are presented as a valuable resource to retain tech talent.

After reading this case study, the students will be able to identify the various choices available in social e-commerce using network marketing, interpret data-driven decisions in social e-commerce and evaluate their role in scaling business, analyse cost and revenue management in value segments, evaluate technology adoption among the masses using appropriate communication structures and develop customer relationships and manage their sentiments in the era of social media.

DealShare became a unicorn in 2022 and targeted the rural and low-income groups. Based on a networking model for customer acquisition and a hyperlocal supply chain model, DealShare is increasing its customer base at a rapid pace. However, profitability was still a challenge, and converting high volume into high value continued to be a daunting task. This case study delves deep into the challenges co-founder Sourjyendu Medda and the DealShare team faced. It seeks to address key issues: how should DealShare leverage customer network for faster customer acquisition and how should they increase ticket size and profitability? As a data-driven business, what advantages does DealShare have in influencing customers’ buying behaviour using data? Dependence on social media could have a cascading effect on “word of mouth”. How can they manage customer complaints and increase engagement?

This case study has the potential to be used in different settings. In strategic cost management, this case study can demonstrate strategies for cost management in the value-conscious segment. This case study can be used in marketing management courses while teaching “positioning” in business-to-consumer markets and CRM. For second-year management students, this can be used in entrepreneurship and strategic management courses to demonstrate the network effect in social e-commerce start-up businesses. This case study is also relevant for various course modules in graduate management programmes to demonstrate the power of data-driven decision-making in business.

Teaching notes are available for educators only.

CSS 8: Marketing

In the developing field of nano-materials synthesis, copper oxide nanoparticles (NPs) are deemed to be one of the most significant transition metal oxides because of their intriguing characteristics. Its synthesis employing green chemistry principles has become a key source for next-generation antibiotics attributed to its features such as environmental friendliness, ease of use and affordability. Because they are more environmentally benign, plants have been employed to create metallic NPs. These plant extracts serve as capping, stabilising or hydrolytic agents and enable a regulated synthesis as well.

Organic chemical solvents are harmful and entail intense conditions during nanoparticle synthesis. The copper oxide NPs (CuO-NPs) synthesised by employing the green chemistry principle showed potential antitumor properties. Green synthesised CuO-NPs are regarded to be a strong contender for applications in the pharmacological, biomedical and environmental fields.

The aim of this study is to evaluate the anticancer potential of CuO-NPs plant extracts to isolate and characterise the active anticancer principles as well as to yield more effective, affordable, and safer cancer therapies.

This review article highlights the copper oxide nanoparticle's biomedical applications such as anticancer, antimicrobial, dental and drug delivery properties, future research perspectives and direction are also discussed.