Indian Institute of Management Ranchi

Purpose Extant research on the knowledge-intensive dynamic capabilities (DCs), in general, and information technology-enabled dynamic capabilities (ITDCs) has been criticized for lacking clarity and prescriptive value, reification of the focal construct and offering black box explanations of important organizational phenomena. The purpose of this paper is to address these issues and clarify the ITDC construct by discussing the role of IT in enabling DCs, classifying ITDCs based on function served, classifying the enablers of ITDCs and proposing a framework integrating the enablers and ITDCs. Design/methodology/approach Through a deep-dive into the extant literature, the authors develop a conceptual framework to bring out the dynamics around ITDCs. Findings The authors find that IT plays a critical role in enabling DCs, as these capabilities are knowledge intensive. Further, they develop a typology of ITDCs and classify the enablers of ITDCs. Research limitations/implications This paper will help future research by clarifying the confusion surrounding an important construct leading to accretion of related research and by facilitating more granular empirical studies. The proposed typology and framework will also help managers seeking to develop, deploy and maintain appropriate ITDCs in their respective organizations. Originality/value The DC theoretical perspective is an important theoretical perspective for examining the value added by information technology (IT) to businesses operating in volatile, uncertain, complex and ambiguous environments, with dynamic IT capabilities and ITDCs being the two key conceptualizations. To the best of the authors’ knowledge, this is arguably the first paper to examine ITDCs in sufficient detail to unpack the associated dynamics.

Social networks are increasingly in demand for text mining applications. Text analysis has become a more popular technique used on the internet. Social media platforms provide abundant access to text data, allowing users to post comments, making the analysis of these comments crucial for various business applications. Sentiment analysis (SA) is a subfield of natural language processing that aims to automatically identify and classify opinions expressed in text as positive, negative, or neutral. It plays a crucial role in understanding public opinion, especially when applied to large-scale textual data from social media platforms. However, social media data extraction policies governed by platform-specific APIs, privacy constraints, and data usage limitations pose challenges in acquiring high-quality, representative datasets for research. This study proposes TSAPM-BERT, a parametrically modified BERT-based framework that integrates a weighted attention mechanism, information from the sentiment lexicon and optimization of the learning rate to enhance the classification of sentiment at the aspect level. We evaluate the model on a benchmark emotion dataset, comparing its performance against traditional machine learning and deep learning baselines. Experimental results demonstrate that TSAPM-BERT achieves a training accuracy of 99.37 and testing accuracy of 98.52, outperforming competing methods. This work provides a reproducible framework that bridges the gap between high-accuracy sentiment classification and the practical constraints of social media data usage.

Blockchain has gained significant attention, but operations literature lacks an integrated framework capturing its adoption dynamics. This paper addresses the gap through a multi-case study, proposing a framework that examines drivers, enablers, resistors, mechanisms, and outcomes of blockchain adoption in supply chains. The process is segmented into three phases—preadoption, early adoption, and full adoption—across technological, organizational, and environmental dimensions. Findings show organizational and environmental factors dominate preadoption, while enablers and resistors shape early adoption. In full adoption, mechanisms such as resilience, trust, visibility, information sharing, and cost reduction deliver operational benefits. The study offers valuable insights for researchers and practitioners to better understand and implement blockchain in supply chain operations.

This research examines the motivations for, and obstacles to, establishing a Joint Audit (JA) system in Kuwait. The research includes a survey taken by 190 people and 25 in-depth interviews with key individuals. According to the qualitative study, 'auditor performance and collaboration' as well as 'market confidence and cost efficiency' are two factors that contribute to the widespread use of JA. According to the research, however, 'coordination difficulties and free rides' prevent its widespread use. The study found that JA laws, stricter regulations, and more open financial reporting were all required. In order to restore confidence in financial reporting, the results highlight the need for enhancements to audit quality, investor protection, and communication amongst audit firms. The limited size of the qualitative and quantitative samples used in this study may reduce the external validation. The study's limited generalisability may be attributable to its exclusive emphasis on JA practices in Kuwait.

Bangla (Bengali) voice synthesis presents distinct issues due to its restricted language resources and complex vocabulary. This work introduces a state-of-the-art Bangla text-to-speech (TTS) system that utilizes the Variational Inference Synthesis of Speech (VITS) architecture. We tailor VITS for the Bengali language by utilizing a 20-hour custom-made dataset consisting of speech samples from a single speaker from native literary works. The model undergoes comprehensive training, enabling it to convert text into waveforms while detecting subtle pronunciation variations accurately. This study introduces a Bangla TTS system based on VITS, which has attained a Mean Opinion Score (MOS) of 4.01, a Mel-Cepstral Distortion (MCD) ranging from 5.24 to 5.48, a Word Error Rate (WER) between 0.16 and 0.18, and a Phoneme Error Rate (PER) of 0.15 to 0.17. These performance indicators validate the system’s excellence compared to current alternatives like Google Bangla TTS and Narakeet. Our model exhibits strong generalization capabilities, particularly with previously unseen complex vocabulary. The model has demonstrated superior performance to publicly accessible Bangla TTS models such as Google Bangla TTS, Narakeet, and AI4Bharat. Objective metrics validate the high level of comprehensibility and resemblance among speakers. Preliminary informal hearing tests indicate that the speech sounds highly natural. This study demonstrates the current advancements in neural text-to-speech (TTS) technology, which can be used in low-resource Indian languages, provided sufficient data is available.

ABSTRACT The growing influence of entrepreneurship on the advancement and economic progress of the people and regions surrounding the entrepreneurs has led to increased attention on developing an entrepreneurial ecosystem (EE). However, such an ecosystem demands an in‐depth understanding of the factors that can support and further grow entrepreneurship in the region surrounding it. This paper (i) consolidates literature and findings on different success factors of the EE and (ii) identifies key success factors, including environment, social, and governance (ESG) factors that are responsible for the EE's success and sustainability. A bibliometric‐systematic literature review (B‐SLR) of 109 papers has been conducted, guided by Preferred Reporting Items for Systematic Reviews and Meta‐analysis (PRISMA). In the analysis of 109 studies, a total of 14 critical factors of entrepreneurial ecosystem development have been identified based on primary and secondary level factors. This is the first study in which a B‐SLR has been conducted to understand the critical success factors of entrepreneurial ecosystem development based on the Quadruple and Quintuple Helix Model—academia, industry, civil society, government, and natural environment. The study invites scholars and researchers to explore the dynamic interplay between institutional logics, resource configurations, and sustainability imperatives—thus enriching EE literature with a more holistic, interdisciplinary perspective. It also offers a comprehensive framework for managers and policymakers to foster resilient, innovative, and sustainable EEs. By leveraging the critical success factors and integrating ESG principles, stakeholders can create ecosystems that not only enable entrepreneurial growth but also address pressing global challenges and contribute to a more inclusive and sustainable future.

RDTs are primarily responsible for transporting large quantities of overburden and coal from extraction sites to disposal sites in mining operations. Their efficiency, however, depends on a variety of factors, including geo-mining conditions, haul road quality, and the characteristics of the RDTs themselves, such as their age, capacity, make, maintenance and many other factors. These variables not only affect operational performance but also have a direct impact on fuel consumption and its environmental outcomes, particularly on the Greenhouse Gas Emissions (GHGE). This paper investigates the effect of some geo-mining conditions, age and tonnage factors of the RDTs on its performance in a large opencast coal mining project. Data from RDTs of different makes, ages, and capacities operating under varying haul road conditions and lead distances were collected for analysis. Using mathematical tools, the study draws inferences on how these factors influence diesel consumption, operational efficiency, and impact the environment. The results demonstrate that, while lead distance, haul road quality, and capacity of the RDTs are critical parameters, the make of the dumper also plays a significant role in the performance. Major Findings: The findings suggest that improving geo-mining conditions and proper maintenance could lead to reduced fuel consumption, lower GHGE emissions, and better cost-efficiency in surface coal mining operations.