Search results

The purpose of this study is to investigate potential lenders of legitimacy for business-to-business (B2B) startups as reflected in the willingness of potential customers to do business with startup firms. This study theorizes the role of familiarity with B2B startups, their founding teams and their product offerings in influencing perceptions about legitimacy among potential customers.

Data are collected from key decision-makers involved in B2B procurements in large Indian companies and analyzed using conjoint analysis.

Results suggest that familiarity with product/service offerings from B2B startups is the most salient factor in forming favorable assessments for the venture, followed by the awareness of the startups and their founding teams, in that order.

The research makes several contributions to understanding the legitimacy of B2B startups from the customers’ perspective. The study provides a nuanced view of the factors impinging on legitimacy. The conceptualization of legitimacy as a reflection of willingness to buy (in other words, willingness to do business with) provides a useful lens with which to study the interactions between B2B startups and potential customers.

The strong empirical support the study finds for the predicted relationships in an international context, specifically India, enhances theory development, providing a solid foundation for future knowledge generation around the demand side legitimacy concept.

This paper aims to present the implementation of a multi-layer radiation propagation model in simulations of multi-phase flow and heat transfer, for a dissociating methane hydrate reservoir subjected to microwave heating.

To model the induced heterogeneity due to dissociation of hydrates in the reservoir, a multiple homogeneous layer approach, used in food processes modelling, is suggested. The multi-layer model is incorporated in an in-house, multi-phase, multi-component hydrate dissociation simulator based on the finite volume method. The modified simulator is validated with standard experimental results in the literature and subsequently applied to a hydrate reservoir to study the effect of water content and sand dielectric nature on radiation propagation and hydrate dissociation.

The comparison of the multi-layer model with experimental results show a maximum difference in temperature estimation to be less than 2.5 K. For reservoir scale simulations, three homogeneous layers are observed to be sufficient to model the induced heterogeneity. There is a significant contribution of dielectric properties of sediments and water content of the reservoir in microwave radiation attenuation and overall hydrate dissociation. A high saturation reservoir may not always provide high gas recovery by dissociation of hydrates in the case of microwave heating.

The multi-layer approach to model microwave radiation propagation is introduced and tested for the first time in dissociating hydrate reservoirs. The multi-layer model provides better control over reservoir heterogeneity and interface conditions compared to existing homogeneous models.

The paper aims to test the spectral line-based weighted sum of gray gases (SLW) method in axisymmetric geometries with particles and high temperature gradients.

An SLW model is coupled with Trivic’s mean wavelength approach to estimate the radiative heat fluxes at the wall of an enclosure and to the base wall of the rocket exhaust, thereby subsequently studying the effect of concentration variation of the gases and particles in these cases. Radiative transfer equation is solved using modified discrete ordinates method. Anisotropic scattering is modeled using transport approximation.

Two cases considered show the importance of particle emission and scattering in the rocket plume base heating problems. In cases involving only gases, the concentration of H₂O tends to have more impact on the flux values than any other gas.

A full model of gases with particles in an axially varying temperature field is reported. Such cases are very common in practical applications. The present methodology gives more insight and a firm handle on the problem vis-a-vis other traditional techniques.

The study aims to analyze factors impacting firms’ success and persistence in a digital platform competition using the structure-conduct-performance (SCP) framework. The study also includes real-life cases that are beneficial to academicians and practitioners to understand and develop strategies for success and persistence during uncertainty.

A literature review to identify the factors that impact success and persistence in a digital platform competition was conducted following Webster and Watson (2002). Findings were integrated into a SCP framework to examine and understand the identified factors’ relational impact.

While analyzing factors under the SCP framework, all factors were divided into three categories: those impacting positively, those impacting negatively and those with ambiguous impact on the success and persistence in digital platform competition. Digital platform firms can exploit the positively impacting factors to increase market share by being distinctive from other digital platform firms and becoming dominant by withstanding competition. On the other hand, negatively impacting factors increase barriers to entry, intensify competition and reduce the distinctiveness of digital platform firms. Lastly, a few factors may have either a positive or a negative impact depending upon the particular characteristics of the firm/industry.

The study opens the scope for future research on empirically testing the developed conceptual framework and relationships by developing propositions to posit the possible impact of these factors on digital platforms’ success and persistence.

The study contributed to the existing literature by using SCP framework to analyze the factors affecting firm’s success and persistence in a digital platform competition. Also, the study has discussed the relational impact of factors rather than their impact in isolation.

The present study examines the fluctuations in Socioeconomic and demographic (SED) factors and the prevalence of Non-Communicable Diseases (NCDs) across clusters of states in India. Further, it attempts to analyze the extent to which the SED determinants can serve as predictive indicators for the prevalence of NCDs.

The study uses three rounds of unit-level National Sample Survey self-reported morbidity data for the analysis. A machine learning model was constructed to predict the prevalence of NCDs based on SED characteristics. In addition, probit regression was adopted to identify the relevant SED variables across the cluster of states that significantly impact disease prevalence.

Overall, the study finds that the disease prevalence can be reasonably predicted with a given set of SED characteristics. Also, it highlights age as the most important factor across a cluster of states in understanding the distribution of disease prevalence, followed by income, education, and marital status. Understanding these variations is essential for policymakers and public health officials to develop targeted strategies that address each state’s unique challenges and opportunities.

The study complements the existing literature on the interplay of SEDs with the prevalence of NCDs across diverse state-level dynamics. Its predictive analysis of NCD distribution through SED factors adds valuable depth to our understanding, making a notable contribution to the field.

This paper aims to estimate the bearing capacity of a surface strip and circular footings lying on layered sand using numerical limit analysis.

Lower and upper bound limit analysis, as well as finite elements and second-order conic programming (SOCP), are used in this analysis. The yield criterion of Mohr-Coulomb is used to model soil behavior. Using this technique, stringent lower and upper bounds on ultimate bearing capacity can be achieved by assuming an associated flow law.

The obtained results indicate that the exact collapse load is typically being bracketed to within 6% about a mean of both the bounds. The obtained results are compared with the existing literature wherever applicable.

To the best of the authors’ knowledge, no study has used lower and upper bound limit analysis, as well as finite elements and SOCP, to estimate the bearing capacity of a surface strip and circular footings lying on layered sand.

As businesses in a wide range of industries increasingly adopt digital ecosystems, the benefits include opening up new and untapped customer segments and channels, enabling access to new and untapped talent pools and expanding previously unexplored modes of innovation. Ecosystems – defined as digitally enabled networks that enhance value propositions by linking business functions, suppliers, distributors, partners, customers and other stakeholders – are now an engine that drives performance. Multiple studies confirm that value creation and competitive advantage are increasingly tied to organizations’ ability to engage partners and stakeholders. IBM Institute of Business Value’s most recent research has found that revenue growth of ecosystem leaders outpaces others by a five-to-one ratio.

Yet despite these notable successes there is also a dark side to digital ecosystem engagement. Large ecosystems have become a central to business strategy while little attention has been paid to the potential for cyber threats. Increased openness of business operations can result in greater risk.

Security must graduate into a central enabler of business transformation. If businesses do not fundamentally rethink their security equation, ecosystems and the trust on which they are built, which often take years and billions of investment dollars to build, can be squandered in minutes by ever-growing security threats.

Large ecosystems have become a central to business strategy while little attention has been paid to the potential for cyber threats.

Mature, risk-mitigated ecosystems are starting to resemble a mature supply chain, where some risks are precluded by design, some partners are favored based on established trust criteria and remaining risks are explicitly managed transactionally as part of the partner relationship.

The purpose of the paper is to identify, analyze and select the enablers for vertical integration of Aloe vera supply chain (AVSC) so that rural employability will be enhanced in the context of Rajasthan, India.

Interpretive structural modeling (ISM) was proposed to develop a structural model to identify the right enablers for enhancing the rural employability and business prospects. Also, fuzzy-matrix cross-reference multiplication applied to classification (F-MICMAC) was applied to segregate the enablers into four clusters on the basis of their driving and dependence power. Finally, the significant enablers were selected.

Out of identified 13 enablers, three enablers (i.e. institute for training and research, transportation infrastructure and government incentives for value addition) were appearing at the bottom of the ISM structural model and also in the driving quadrant of driver-dependent diagram. Therefore, they are the significant enablers for vertical integration of AVSC to enhance the rural employability in the context of Rajasthan, India.

The interactions among enablers are not statistically validated. However, the empirical analysis and total interpretive structural modeling may be used for this purpose.

The outcomes of the study will provide the guidelines for implementation of vertical integration at the village level to enhance rural employability in the context of Rajasthan, India in specific.

Although a few studies have been reported in the literature related to value-addition process (vertical integration), but the modeling of enablers to segregate and identify the appropriate enablers for vertical integration of AVSC for enhancing employability at the rural areas is unique.

COVID-19 has affected the economies adversely from all sides. The sudden halt in production has impacted both the supply and demand sides. It calls for analysis to quantify the impact of the reduction in economic activity on the economy-wide variables so that appropriate steps can be taken. This study aims to evaluate the sensitivity of various sectors of the Indian economy to this dual shock.

The eight-sector open economy general equilibrium Global Trade Analysis Project (GTAP) model has been simulated to evaluate the sector-specific effects of a fall in economic activity due to COVID-19. This model uses an economy-wide accounting framework to quantify the impact of a shock on the given equilibrium economy and report the post-simulation new equilibrium values.

The empirical results state that welfare for the Indian economy falls to the tune of 7.70% due to output shock. Because of demand–supply linkages, it also impacts the inter- and intra-industry flows, demand for factors of production and imports. There is a momentous fall in the demand for factor endowments from all sectors. Among those, the trade-hotel-transport and manufacturing sectors are in the first two positions from the top. The study recommends an immediate revival of the manufacturing and trade-hotel-transport sectors to get the Indian economy back on track.

The present study has modified the existing GTAP model accounting framework through unemployment and output closures to account for the impact of change in sectoral output due to COVID-19 on the level of employment and other macroeconomic variables.

The general data protection regulation (GDPR) was designed to address privacy challenges posed by globalisation and rapid technological advancements; however, its implementation has also introduced new hurdles for companies. This study aims to analyse and synthesise the existing literature that focuses on challenges of GDPR implementation in business enterprises, while also outlining the directions for future research.

The methodology of this review follows the preferred reporting items for systematic reviews and meta-analysis guidelines. It uses an extensive search strategy across Scopus and Web of Science databases, rigorously applying inclusion and exclusion criteria, yielding a detailed analysis of 16 selected studies that concentrate on GDPR implementation challenges in business organisations.

The findings indicate a predominant use of conceptual study methodologies in prior research, often limited to specific countries and technology-driven sectors. There is also an inclination towards exploring GDPR challenges within small and medium enterprises, while larger enterprises remain comparatively unexplored. Additionally, further investigation is needed to understand the implications of emerging technologies on GDPR compliance.

This study’s limitations include reliance of the search strategy on two databases, potential exclusion of relevant research, limited existing literature on GDPR implementation challenges in business context and possible influence of diverse methodologies and contexts of previous studies on generalisability of the findings.

The originality of this review lies in its exclusive focus on analysing GDPR implementation challenges within the business context, coupled with a fresh categorisation of these challenges into technical, legal, organisational, and regulatory dimensions.