Causal modelling of HR flexibility and firm performance in Indian IT industries

Technology usage, intellectual capital, firm performance and employee satisfaction: the accountants' idea

Purpose

The paper proposes a conceptual integration between two variables, both considered as capable of affecting public firm performance: technology and intellectual capital.

Design/methodology/approach

The analysis is performed by testing a structural equation model (SEM) which allows to measure simultaneously a plurality of variables, highlighting all the possible connections. Data is collected by administering more than 500 paper questionnaires to accountants working within Local Health Firms of Naples and Salerno.

Findings

The study seems to align with the considerations according to which intellectual capital expressed through its three dimensions – relational capital, human capital and organizational capital – exert a positive influence on perceived performance of healthcare firms, ultimately impacting on the Employees' Satisfaction.

Research limitations/implications

The study acts as a useful guide from a managerial point of view, because it may support firm decision-making. In fact, public sector managers can leverage an instrument capable of activating functional mechanisms to improve firm performance.

Originality/value

The work allows overcoming the literature gap due to the fact that, although there is a wide recognition of the potential of technology and intellectual capital, there are no studies that synergistically integrate both the aspects in the attempt to understand their value in terms of influence on the performance of public firms, on the one hand, and on employees' satisfaction, on the other. In this vein, the work, in an attempt to provide further scientific support to the link between technology and intellectual capital, is a tool capable of highlighting how this link positively impacts on company performance and employee satisfaction.

Data science in the business environment: customer analytics case studies in SMEs

Purpose

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising.

Design/methodology/approach

Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions.

Findings

Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams.

Research limitations/implications

The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper.

Originality/value

Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.