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The paper aims to develop a comprehensive framework for adopting an information and communication technology (ICT)-enabled customer relationship management (CRM) system, toward strengthening and expanding the customer basis of the organization.

Advanced ICT-enabled CRM includes mobile CRM, AI integrated CRM and social CRM and several business organizations have already started exploring their advanced utilization for direct financial gain and indirect “soft” benefits. Most of them, however, are challenged in the process, due to lower-than-demanded adoption of such CRM systems. To deal with the issue, the paper methodologically applies a dual qualitative case study approach that ultimately develops an advanced comprehensive CRM adoption framework.

The key findings pertaining to the evaluation of organizations’ readiness to adopt advanced ICT-enabled CRM systems in terms of infrastructure and resources, the identification of the critical functional areas of the organization’s focus and the differences in the requisite approaches across industries and organization types.

In the context of the internet and social media, the analysis and management of customer big data and their transcription into useable information is crucial. Traditional and conventional CRM was deemed unable to address this need, thus necessitating the use of advanced ICT-enabled CRM. The proposed corresponding framework tangibly and practically, thus, directs businesses toward the successful deployment of ICT-based CRM systems.

This study constitutes a novel attempt to identify the issues of deployment of ICT-based CRM systems in an organization and proposes a comprehensive framework, which will enable organizations to overcome the barriers when adopting a new system.

Artificial biomaterials are implanted to the human body to support the structure depending upon the extent of deformity or damage. This paper aims to formulate an experimental approach to assess the suitability of materials that can be used in the manufacture of human implants.

Five different pin materials such as SS304, Alumina, HDPE, UHMWPE and Brass have been chosen to be suitable for implants. The tribological properties of the aforementioned materials have been tested on a simple pin-on-disc apparatus. EN31 was chosen as the disc material because its hardness value is much higher than that of the pin materials used. The test materials were constructed in the form of spherical end pins to have point contacts and to reduce the depth of wear.

It has been observed that the polymeric (HDPE and UHMWPE) and ceramic materials (Alumina) are much better than the traditional metallic materials. The wear rate is very low for these materials owing to their self-lubricating properties.

The experimental studies will help predict the performance and life of implant materials in the human body.

In most cases, SS316L that possesses nickel compositions is used as the disc material; SS316L is toxic to the human body. In the present study, a high carbon alloy steel with high degrees of hardness EN31 is used as a disc counter-face material.

Rising crude oil prices are likely to have an asymmetric and nonlinear negative impact on GDP growth. The purpose of this paper is to ask the following questions: Does the effect of a crude price shock depend on the position of crude price cycle, i.e. is the effect of price shock larger/smaller in periods of already elevated crude price? And, does the effect of crude price shock depend on the position of the economy in the business cycle, i.e. does the crude price shock affect growth differentially in periods of low/high growth?

The authors use a local linear projection (LLP) model to examine the asymmetric impact of crude price on GDP growth in an environment of high crude price. Next, a quantile regression model is used to account for differential impact on growth around high and low growth periods.

Results from the LLP model show that when oil price is above $70, each additional percentage point of increase in oil price results in a 20 basis point (bps) drop in quarterly GDP growth rate on average. The impact is felt between the third and sixth quarters. When oil prices rise above $80, the impact is similar, with a sharper drop in growth (30 bps). The exercise with quantile regression shows that the impact of an increase in crude prices on growth is almost double at lowest quantiles of growth compared with the median.

There is a growing literature that evaluates the impact of oil price in developing economies. However, nonlinearities in crude price-GDP growth dynamics have not received enough attention, especially during phases of elevated crude price or a growth downcycle. The authors believe that accounting for such effects is especially relevant in the present economic scenario of high oil prices because of geopolitical crises and a period of vulnerable growth because of supply chain issues arising out of the pandemic. Using recent data from oil-importing emerging market economies such as India, this paper fills a crucial gap in the literature.

The purpose of this paper is to identify the critical success factors (CSFs) for AI-integrated CRM system for better knowledge management (KM) in organizations to improve business process.

The factors critical for adoption of AI-integrated CRM system for efficient knowledge management are innumerable. The salient factors may be identified by several means. Methods like brainstorming and Delphi have been applied here. Sixteen CSFs have been identified. Then the interrelationship among these 16 factors, levels of their importance and the principal driving factors have been established by interpretative structural modelling (ISM) methodology.

The results show that out of 16 CSFs, leadership support, adequate fund and support of functional area leads are the most important CSFs for AI–CRM–KM integration.

The results show that support of top management is essential for successful adoption of AI-integrated CRM system for better knowledge management to improve the business process.

This paper has taken a novel attempt to identify CSFs for AI-integrated CRM adoption for efficient knowledge management system in organizations for improvement of business process and to establish interrelationship among those CSFs with the help of ISM methodology.

The study aims to examine the heterogeneous effects of physical infrastructure on farm and non-farm sector output in 16 prominent states of India for the period 2005–2019.

We construct a composite physical infrastructure index comprising four indicators namely road density, rail density, tele-density and per capita power availability using the principal component analysis (PCA) method. We estimate the impact of physical infrastructure on aggregate as well as farm and non-farm sector output using the fixed effects regression with Driscoll–Kraay (FE-DK) standard errors to account for cross-sectional dependence in our sample. Furthermore, to mitigate the potential endogeneity concern, we utilise the panel instrumental variable (IV) regression method to estimate the model.

Our results imply that physical infrastructure plays a significant role in driving economic growth. We find that infrastructure development affects the non-farm sector output positively while the farm sector output remains unaffected.

Due to data limitations, our study is based on 16 prominent states only. It is plausible that the selected infrastructure indicators are not highly relevant for the farm sector. Further research can be done to identify appropriate infrastructure that helps stimulate agriculture sector output.

We examine the varying effects of physical infrastructure on farm and non-farm sector output in the Indian context. To the best of our knowledge, this is the first study that examines the effect of infrastructure development on sectoral output at the sub-national level.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2024-0468

The purpose of this study is to make an attempt to identify the factors responsible for the adoption of an artificial intelligence (AI)-integrated customer relationship management (CRM) system in Indian organizations with a special focus on security and privacy issues.

This study was conducted for the identification of factors responsible for the adoption of the AI-integrated CRM system in Indian organizations focusing attention on security and privacy perspective. For this, the adoption theories and models have been studied. The literature available in this context has also been studied with a focus on security and privacy issues. After the initial study, few hypotheses have been formulated and a conceptual model has been developed. These hypotheses were validated with the help of statistical tools by conducting sample survey with 324 usable responses against 36 questionnaires.

The results of this study highlight that of the eight hypotheses conceptually formulated, one hypothesis was not supported as is evident from the application of statistical analysis. This is the influence of perceived ease of use on attitude of the stakeholders intending to use the AI-integrated CRM system in Indian organizations. The results also transpire that the model so provided has achieved 87% explanative power.

The model so provided has taken the help of the technological acceptance model. It has also used the issues circumscribing menace of security and privacy vulnerabilities. Consideration of the technological acceptance model and aspects of issues of security and privacy has enriched the model rendering its explanative power to 87%.

The model is simple. Practitioners can execute this model without having any complexity. The policymakers could also get inputs from the model, as it has focused specially on security and privacy issues that could help to enhance the trust of the potential users.

Not many studies are found covering the adoption of the AI-integrated CRM system by Indian organizations with a special focus on security and privacy aspects. In this light, this study is a novel attempt.

This study aims to identify the business benefit of and factors affecting the use of social customer relationship management (SCRM) in Indian organizations.

Building on theoretical foundations, a conceptual model of factors affecting SCRM in Indian organizations is developed and empirically tested through a survey and corresponding analysis using SPSS and AMOS software.

The study presents empirical evidence that technological competence, environmental characteristics and organizational environment positively impact the actual use of SCRM on Indian organizations. Additionally, leadership support of organizations impacts positively the actual use of SCRM in organizations, while the trust factor insignificantly impacts the latter. The actual use of SCRM in organizations was found to have a positive impact on their business benefits.

The theoretical model is built on the constructs of the technology, environment and organizational framework. It has added new factors, such as leadership support and trust, and thereby identified the business benefits of organizations using SCRM mediating through the organizations’ actual use of SCRM. The proposed model is simple, implementable and has a high explanative power of 81 per cent.

The study provides practitioners with evidence and practicable knowledge regarding the means and impact/benefits of SCRM use in Indian organizations.

The study is one of few empirical studies on the topic and contributes valuable knowledge to extant works through additional factors, theoretical conceptualization and empirical scientific findings of both scholarly and executive worth.

The purpose of this study is to explore the behavioral intention of the employees to adopt artificial intelligence (AI) integrated customer relationship management (CRM) system in Indian organizations.

To identify the factors impacting the behavioral intention of the employees to adopt AI integrated CRM system in Indian organizations helps of literature review and theories have been taken. Thereafter, some hypotheses have been formulated followed by the development of a theoretical model conceptually. The model has been tested statistically for validation using a survey by considering 308 usable respondents.

The results of this study show that perceived usefulness and perceived ease of use directly impact the behavioral intention of the employees to adopt an AI integrated CRM system in organizations. Also, these two exogenous factors impact the behavioral intention of the employees to adopt an AI integrated CRM system mediating through two intermediate variables such as utilitarian attitude (UTA) and hedonic attitude (HEA). The proposed model has achieved predictive power of 67%.

By the help of the technology acceptance model and motivational theory, the predictors of behavioral intention to adopt AI integrated CRM systems in organizations were identified. The effectiveness of the model was strengthened by the consideration of two employee-centric attitudinal attributes such as UTA and HEA, which is claimed to have provided contributions to the extant literature. The proposed theoretical model claims a special theoretical contribution as no extant literature considered the effects of leadership support as a moderator for the adoption of an AI integrated CRM system in Indian organizations.

The model implies that the employees using AI integrated CRM system in organizations must be made aware of the usefulness of the system and the employees must not face any complexity to use the system. For this, the managers of the concerned organizations must create a conducive atmosphere congenial for the employees to use the AI integrated CRM system in the organizations.

Studies covering exploration of the adoption of AI integrated CRM systems in Indian organizations are found to be in a rudimentary stage and in that respect, this study claims to have possessed its uniqueness.

The purpose of this paper is to develop a conceptual framework to check if an organization is ready to adopt an AI-integrated CRM system. The study also analyzes different situations which can provide a comprehensive check list in the form of indicators that could provide a signal indicating whether the organization is ready to adopt an AI-integrated CRM system by capturing actionable and appropriate data.

The paper is a general review, and appropriate literature has been used to support the conceptual framework.

The key findings of this study are the different indicators that make up the conceptual framework. This framework can help organizations to check at a glance whether they are ready to adopt AI-integrated CRM system in their organizations. Specifically, it has been identified that different approaches are needed to tackle various types of customer data so that those may be made fit and actionable for appropriate utilization of AI algorithms to facilitate business success of an organization.

The paper has elaborately discussed the different approaches to be undertaken to calibrate and reorient the various kinds of actionable data and the contemplated challenges one would face in doing so. This would help the practitioners that how the data so captured can be made fit for action and utilization toward application of AI technologies integrated with existing CRM system in an organization.

This study is claimed to be a unique study to provide a conceptual framework which could help arranging and rearranging of captured data by an organization for making the data fit and ready for use with the help of AI technologies. This successful integration of AI with CRM system can help organizations toward taking quick and automated decision-making without much intervention of human beings.

This paper investigates the selection of semiconductor switches used in contactless power transfer (CPT) system. In the present paper a single phase high frequency full bridge inverter using different semiconductor switches like IGBT, MPOSFET and GTO has been considered. Harmonic injection in input current of the inverter for different semiconductor switches has been analyzed using PSIM software. The THD of input current of the inverter for the particular switching device has been determined by using Fourier Transforms. It has been observed that THD in case of the IGBT is minimised.