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This article aims to establish the need for professionals and industry to get together on a common platform to engage in discussions, identify innovations and set standards. The movement of employees from unorganized to organized segment is accelerating and subsequently the cost of managing employees is also going to increase manifold for Indian organizations. The HR Tech landscape is pegged at $400m annually and expected to grow significantly.

The paper looks at the HR Tech landscape and posits how HR technologies can help companies save significant costs, and also bring in advanced techniques such as automation and analytics which will improve work efficiency.

While big enterprises are also working in the area of HR technology, startups are showing rapid progress, owing to the fact that they do not have to cope with legacy data/technology/operations. Challenges still remain such as failure to lay groundwork for adoption by employees.

While there are diverse products and technologies in the market, the core challenge is to find the right product-market fit. HR buyers have to ensure that the solutions are future proof.

Automation is going to affect a lot of jobs, certain profiles more so than others. At a larger scale, integrating automation in work processes will have a substantial adverse impact on employees in these job profiles.

The HR Tech landscape is inundated with technology-first startups. There is a dearth of academic literature. There is also a cogent need for professionals and industry to get together on a common platform to engage in discussions, identify innovations, and set standards.

This study aims to examine the key antecedents influencing the private firm’s intention to adopt big data analytics (BDA) in developing economies. To do so, the study follows the sequential explanatory approach.

To test the hypothesized model that draws on the technology–organization–environment (TOE) framework paired with the diffusion of innovation (DOI) theory, a purposive sampling technique was applied to gather data from 156 IT and management domain experts from the private firms that intend to adopt BDA and operate in Pakistan’s service industry, including telecommunication, information technology, agriculture, and e-commerce. The data were analysed using the partial least squares structural equations modelling (PLS-SEM) technique and complemented with qualitative analysis of 10 semi-structured interviews in NVIVO 12 based on grounded theory.

The empirical findings revealed that the two constructs – perceived benefits and top management support – are the powerful drivers of a firm’s intention to adopt BDA in the private sector, whereas IT infrastructure, data quality, technological complexity and financial readiness, along with the moderators, BDA adoption of competitors and government policy and regulation, do not significantly influence the intention. In addition, the qualitative analysis validates and further complements the SEM findings.

Unlike the previous studies on technology adoption, this study proposed a unique research model with contextualized indicators to measure the constructs relevant to private firms, based on the TOE framework and DOI theory, to investigate the causal relationship between drivers and intention. Furthermore, the findings of PLS-SEM were complemented by qualitative analysis to validate the causation. The findings of this study have both theoretical and practical implications.

This paper aims to help small- and medium-sized enterprises (SMEs) to seize the potential of Big Data in their marketing strategies to leverage a competitive advantage in the rising digital marketplace and lead the post-pandemic economic recovery. However, Spanish SMEs have had serious difficulties in working with Big Data.

In this study, a roadmap is designed to pave the way for a successful Big Data implementation.

SMEs with updated information on how to access the historic funding opportunities might profit to this opportunity. It not only generates financing opportunities for SMEs but also grants continuity to their strategies by offering the most profitable techniques by which they can efficiently analyse data and gain consumer insights, thus overcoming the many problems they face when working with Big Data as well as helping them to monetise their marketing strategies.

The main advantage of this research is its innovative approach to business strategy as it provides. The value added by this paper lies in its holistic and updated approach to supporting SMEs’ Big Data strategies.