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The purpose of this paper is to identify the role of digitalization on the key characteristics of professional service firms (PSFs) that are part of the service sector and inherently oriented with intense knowledge, capital and professionalized workforces.xD; xA.

This study adopted a qualitative, exploratory and inductive research methodology based on in-depth interviews with 49 entrepreneurs/professionals of PSFs focusing on the role of digitalization including capital intensity, knowledge intensity and professionalized workforce.

The result reflected that digitalization facilitates at lower levels of knowledge intensity, whereas it increases the capital intensity for most of the firms and decreases the professionalization of the workforce among PSFs.

The study provides empirical validations where digitalization has changed the distinctive characteristics of PSFs, which promotes new practices, allows for variation and transforms their competitive contexts. In light of these findings, the authors illuminated the application of digitalization on the Indian law firms, retail, education, healthcare and manufacturing industry.

Food and grocery, which accounts for around 60% of the overall retail market in India, is the most promising area for launching a retail firm. The objective of this research paper is to conduct a thorough investigation of the impact of customers’ geographic, demographic and psychographic characteristics on the selection of retail store format choice behavior in the quickly growing Indian food and grocery retail industry, also to analyze the mediating role of store image on the store choice behavior.

A descriptive research design is used to collect data using the survey method and a structured questionnaire. The data collected from more than 400 food and grocery retail customers from neighborhood Kirana stores, supermarkets and hypermarkets in Karnataka, India, would be analyzed using both descriptive (mean and standard deviation) and Structural equation modeling (SEM) techniques. SEM techniques are used for validation of the model with independent constructs namely Demographics factors, Socio-Economic factors, Geographic factors, Lifestyle and Shopping Motives, a Mediating variable Store Image, and a dependent variable Store choice behavior. Partial least squares structural equation modeling (PLS-SEM) is used to examine the suggested theoretical framework.

The model is tested to reveal the impact of shoppers’ age, gender, occupation, education, monthly household income, family size, and distance traveled to the store, which all play a role in their retail format choice. Also, the socio economic and life style factors of shoppers influence their purchasing decisions as well; store image partially mediates between customer characteristics and store choice behavior.

The study has practical implications for food and grocery retailer in understanding customer behavior in the context of changing customer demographic and psychographic features in the Indian retailing sector. The findings aid retail merchants, allowing them to develop more successful retail marketing strategies and gain a competitive advantage.

This study could serve as a springboard for future research in this field. Retail marketers will benefit from the findings in terms of format creation and reorientation of marketing strategies in the shortest time.

Introduction: Artificial intelligence (AI) and digitisation offer substantial human potential and profit margins, making them promising retail solutions. Retail leaders have successfully integrated comprehensive uses into their daily operations, while competitors heavily invest in new projects. The Indian retail sector is undergoing a significant transformation, which can be attributed to factors such as growing income, demographic characteristics, and enhanced consumerism, as well as the rapid development of new technologies such as digitisation and AI, which is changing both consumers’ and retailers’ buying behaviour.

Purpose: This study aims to determine the influence of AI on elements that drive digitisation in the retailing sector, as well as the factors that lead to organised retailers adopting digitisation and its impact on their business.

Methodology: The study employs a standardised questionnaire distributed to organised stores via an online link, and the data are analysed with SmartPLS software 3.0.

Finding: The retail sector is driven by elements that promote digitalisation in food and groceries retailing, such as simplicity of operation, adoption of digital payment, quicker internet connection, retailer consumer interface, and the involvement of AI.

Research implication: AI has significant consequences for retailing, which serves as the interface between marketers and customers.

Theoretical implication: The study’s findings reflect the perspectives of retailers, store managers, and entrepreneurs on how digitalisation and AI are crucial for the creation and growth of long-term competitive advantages in retail.

Need of the Study: Digitalisation, machine learning, and artificial intelligence (AI) is changing at a swift pace, significantly uplifting the role of information technology. The present human resource (HR) aspect transpires AI-based resolution, are gradually more effective with HR process, time-consumption and a complex tasks surrounded by the HRM functionalities.

Purpose: This study attempts to investigate the adoption and diffusion of human resource management (HRM) with the phenomenon of AI-based applications. Hence, this study has emphasised the predictors of AI adoption like competitive pressure, performance expectancy, top management support, strategic partner, employee champion, etc. Moreover, how the AI predictors are connected with HR practices. The research sample focused on 207 HR managers and senior managers from various industries.

Methodology: This study is based on a quantitative research technique encompassing mean, standard deviation, exploratory factor analysis (EFA), Confirmatory Factor Analysis (CFA), Average Variance Extracted (AVE), and Dependent Variable (DV).

Findings: The study’s empirical findings show that higher performance expectations and higher management support are both major predictors of AI adoption. In contrast, competitive pressure did not show a significant relationship with such an intention, and the ‘employee champion’ role has a negative impact on AI adoption.

Implications: AI diffusion and implementation show a significant research gap. In previous studies, adoption in HRM was overlooked. The study’s results provide a comprehensive picture of the situation. The framework and a major contribution to the study of the phenomenon in relation to its possible role in AI’s effectiveness and quality in HRM. The research inspires a debate among service providers, policy-makers, and stakeholders, and builds an efficient workplace.