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This study examines the performance effect of working capital for a large sample of Indian manufacturing firms in light of supply chain disruption, i.e. the COVID-19 pandemic.

This study is based on secondary data collected from the Prowess database on Indian manufacturing firms listed on the Bombay Stock Exchange (BSE) 500. Panel data regression analyses are used to estimate all models. Moreover, this study has employed robust standard errors to consider for heteroscedasticity concerns.

The results challenge the current notion of working capital investment and reveal that higher working capital has a positive and significant impact on firm performance. Further, it highlights that Indian manufacturing firms suffered financially post-COVID-19 as they significantly lack the working capital to run day-to-day operations.

This research contributes to the scant literature by examining the association between working capital financing and firm performance in light of the COVID-19 pandemic, representing typical developing economies like India.

This paper aims to explore how historical context influences the content and selection of rhetorical legitimation strategies. Using case study method, this paper will focus on how insurance companies and labor tried to defend their legitimacy in the context of enactment of Medicare in the USA. What factors influenced the strategic (rhetorical) decisions made by insurance companies and labor unions in their institutional work?

The study is empirically grounded in archival research, involving an analysis of over 9,000 pages of congressional hearings on Medicare covering the period 1958–1965.

The authors show that rhetorical legitimation strategies depend significantly on the specific historical circumstances in which those strategies are used. The historical context lent credibility to certain arguments and organizations are forced to decide either to challenge widely held assumptions or take advantage of them. The authors show that organizations face strong incentives to pursue the latter option. Here, both the insurance companies and labor unions tried to show that their positions were consistent with classical liberal ideology, because of high respect of classical liberal principles among different stakeholders (policymakers, voters, etc.).

It is uncertain how much the results of the study could be generalized. More information about the organizations whose use of rhetorics the authors studied could have strengthened our conclusions.

The practical relevancy of the revised paper is that the authors should not expect hegemony challenging rhetorics from organizations, which try to influence legislators (and perhaps the larger public). Perhaps (based on the findings), this kind of rhetorics is not even very effective.

The paper helps to understand better how organizations try to advance their interests and gain acceptance among the stakeholders.

In this paper, the authors show how historical context in practice influence rhetorical arguments organizations select in public debates when their goal is to influence the decision-making of their audience. In particular, the authors show how dominant ideology (or ideologies) limit the options organizations face when they are choosing their strategies and arguments. In terms of the selection of rhetorical justification strategies, the most pressing question is not the “real” broad based support of certain ideologies. Insurance company and labor union representatives clearly believed that they must emphasize liberal values (or liberal ideology) if they wanted to gain legitimacy for their positions. In existing literature, it is often assumed that historical context influence the selection of rhetorical strategies but how this in fact happens is not usually specified. The paper shows how interpretations of historical contexts (including the ideological context) in practice influence the rhetorical strategies organizations choose.

This study aims to explore the conversion of metaverse marketing (MVM) into strategic agility among SMEs based on dynamic capabilities (DC) and dynamic management capabilities (DMC) theories. This paper discusses how constructs such as immersive marketing technologies (IMT), customer immersion (CI) and managerial capabilities (MC) play critical role in the transformation of MVM into strategic agility (SA).

A theoretical framework based on DC and DMC theories, and a comprehensive review of the literature on MVM, IMT, CI, MC and SA, was developed in order to theoretically investigate the relationships between MVM and SA. In this theoretical framework, MVM is the independent variable, while the dependent variable is SA. Also, IMT and CI both mediate the association between MVM and SA, while MC moderate the association between MVM and SA in one stream; and CI and SA in another stream.

This research study develops a theoretical framework that recommends nine set of important research propositions in MVM. An extensive literature review was conducted to examine the theoretical framework on the effect of MVM on SA. The proposed theoretical framework suggests that brand community development and communication, experiential marketing and personalisation in MVM, once accessed through IMT (i.e. VR, AR, MR) and CI (i.e. customer engagement, customer absorption-customer acquisition and assimilation of knowledge, presence) can produce significant SA through customer experience management, value co-creation and process innovation.

This current study develops a theoretical framework that theorise the relationship between MVM and SA rooted in literature on MVM and SA, and also based on DC and DMC perspective. The moderating effect of MC on the relationship between IMT and SA on one hand, and CI and SA on the other, provides support to IMT and CI as mediators in the transformation of MVM into SA. This study also provides insight into SME adoption of MVM and how it generates SA. Lastly, the current study contributes to the body of knowledge on MVM, IMT, CI, MC and SA.

Today, e-government (electronic government) applications have extended to the frontiers of health-care delivery. E-Nabız contains personal health records of health services received, whether public or private. The use of the application by patients and physicians has provided efficiency and cost advantages. The success of e-Nabız depends on the level of technology acceptance of health-care service providers and recipients. While there is a large research literature on the technology acceptance of service recipients in health-care services, there is a limited number of studies on physicians providing services. This study aims to determine the level of influence of trust and privacy variables in addition to performance expectancy, effort expectancy, social influence and facilitating factors in the unified theory of acceptance and use of technology (UTAUT) model on the intention and behavior of using e-Nabız application.

The population of the study consisted of general practitioners and specialist physicians actively working in any health facility in Turkey. Data were collected cross-sectionally from 236 physicians on a voluntary basis through a questionnaire. The response rate of data collection was calculated as 47.20%. Data were collected cross-sectionally from 236 physicians through a questionnaire. Descriptive statistics, correlation analysis and structural equation modeling were used to analyze the data.

The study found that performance expectancy, effort expectancy, trust and perceived privacy had a significant effect on physicians’ behavioral intentions to adopt the e-Nabız system. In addition, facilitating conditions and behavioral intention were determinants of usage behavior (p < 0.05). However, no significant relationship was found between social influence and behavioral intention (p > 0.05).

This study confirms that the UTAUT model provides an appropriate framework for predicting factors influencing physicians’ behaviors and intention to use e-Nabız. In addition, the empirical findings show that trust and perceived privacy, which are additionally considered in the model, are also influential.

The purpose of this paper is to examine the antecedents of customer satisfaction during mortgage purchases. Mortgage demand in the USA has reached an all-time high because of an increase in housing demand after COVID-19. Nonetheless, several customers are dissatisfied with their service providers. Customers who actively search the market gain more information about mortgage providers and use this information to define expectations for lenders. The only way there will be customer satisfaction is if lenders meet these expectations. Therefore, it is economically significant for mortgage lenders to discover the antecedents of mortgage satisfaction.

In this study, the partial least squares approach was used to test the hypothesis that satisfaction was influenced by objective knowledge, familiarity and search intensity among a sample of customers (n = 4,512) from the National Survey of Mortgage Originations who had purchased a mortgage in the USA between 2019 and 2020.

The results of structural modelling showed that familiarity (β = 0.23 and p = 0.01) with and knowledge (β = 0.16 and p = 0.01) of mortgages significantly affected consumer satisfaction during mortgage purchase. Search intensity (p = 0.01) mediated the relationship between knowledge, familiarity and satisfaction.

The primary implication is that mortgage service providers should prioritise educating customers about the mortgage buying process on their websites and in person. So managers must actively assist clients in having realistic expectations. Second, mortgage companies should establish a presence on third-party mortgage comparison websites to ensure that customers actively consider alternatives, thereby increasing customer satisfaction.

This study is unique in being an exploratory study to examine the antecedents of mortgage satisfaction using a public data set. This study uniquely examines the National Survey of Mortgage Originations data set with partial least squares approach to examine underlying customer attitudes.

This study aims to analyze a conspicuous corpus of literature related to the field of technology-based intensive care research and to develop an architecture model of the future smart intensive care unit (ICU).

Papers related to the topics of electronic health record (EHR), big data, data flow and clinical decision support in ICUs were investigated. These concepts have been analyzed in combination with secondary use of data, prediction models, data standardization and interoperability challenges. Based on the findings, an architecture model evaluated using MIMIC III is proposed.

Research identified issues regarding implementation of systems, data sources, interoperability, management of big data and free text produced in ICUs and lack of accuracy of prediction models. ICU should be treated as part of a greater system, able to intercommunicate with other entities.

The research examines the current needs of ICUs in interoperability and data management. As environment changes dynamically, continuous assessment and evaluation of the model with other ICU databases is required.

The proposed model improves ICUs interoperability in national health system, ICU staff intercommunication, remote access and decision support. Its modular approach ensures that ICUs can have their own particularities and specialisms while ICU functions provide ongoing expertise and training to upgrade its staff.

Innovation management in the healthcare sector has undergone significant evolutions over the last decades. These evolutions have been investigated from a variety of perspectives: clusters, ecosystems of innovation, digital ecosystems and regional ecosystems, but the dynamics of networks have seldom been analyzed under the lenses of entrepreneurial ecosystems (EEs). As identified by Cao and Shi (2020), the literature is silent about the organization of resource allocation systems for network orchestration in EEs. This article investigates these elements in the healthcare sector. It discusses the strategic role played by entrepreneurial support organizations (ESOs) in resource allocation and elaborates on the distinction between sponsored and nonsponsored ESOs in EEs. ESOs are active in network orchestration. The literature explains that ESOs lift organizational, institutional and cultural barriers, and support entrepreneurs' access to cognitive and technological resources. However, allocation models are not yet discussed. Therefore, our research questions are as follows: What is the resource allocation model in healthcare-related EEs? What is the role played by sponsored and nonsponsored ESOs as regards resource allocation to support the emergence and development of EEs in the healthcare sector?

The article offers an explanatory, exploratory, and theory-building investigation. The research design offers an abductive research protocol and multi-level analysis of seven (sponsored and nonsponsored) ESOs active in French healthcare ecosystems. Field research elaborates on semi-structured interviews collected between 2016 and 2022.

This article shows explicit complementarities between top-down and bottom-up resource allocation approaches supported by ESOs in the healthcare sector. Despite explicit originalities in each approach, no network orchestration model prevails. Multi-polar coordination is the rule. Entrepreneurs' access to critical technological and cognitive resources is based on resource allocation modalities that differ for sponsored versus nonsponsored ESOs. Emerging from field research, this research also shows that sponsored and nonsponsored ESOs manage their roles in different ways because they confront original issues about organizational legitimacy.

Beyond the results listed above, the main originalities of the paper relate to the instantiation of multi-level analysis operated during field research and to the confrontation between sponsored versus nonsponsored ESOs in the domain of healthcare-related innovation management. This research shows that ESOs have practical relevance because they build original routes for resource allocation and network orchestration in EEs. Each ESO category (sponsored versus nonsponsored) provides original support for resource allocation. The ESO's legitimacy is inferred either from the sponsor or the services delivered to end-users. This research leads to propositions for future research and recommendations for practitioners: ESO managers, entrepreneurs, and policymakers.

Modern technologies are seen as an essential component of the fourth industrial revolution (industry 4.0) and their adoption is vital to transform the existing manufacturing system into industry 4.0-based manufacturing system. Therefore, the primary objective of this research explores the barriers of modern technology adoption and their mitigating solutions in order to align with Industry 4.0 objectives.

Barriers to adopting modern technologies and respective mitigating solutions are identified from the available literature. Further, these barriers are ranked with the help of expert opinions by using the BWM method appropriately. The identified solutions are ranked using the combined compromise solution (CoCoSo) method.

Several modern technologies and their capabilities are recognised to support the industry 4.0-based manufacturing systems. This study identifies 22 barriers to the effective adoption of modern technologies in manufacturing and 14 solutions to overcome these barriers. Change management, the high initial cost of technology and appropriate support infrastructure are the most significant barriers. The most prominent solutions to overcome the most considerable barriers are ‘supportive research, development and commercialisation environment’, ‘updated policy and effective implementation’ and ‘capacity building through training’ that are the top three solutions that need to be addressed.

The barriers and solutions of modern technology adoption are obtained through a comprehensive literature review, so there is a chance to ignore some significant barriers and their solutions. Furthermore, ranking barriers and solutions is done with expert opinion, which is not free from biases.

This identification and prioritisation of barriers will help managers to understand the barriers so they can better prepare themselves. Furthermore, the suggested solutions to overcome these barriers are helpful for the managers and could be strategically adopted through optimal resource utilisation.

This study proposes a framework to identify and analyse the significant barriers and solutions to adopting modern technologies in the manufacturing system. It might be helpful for manufacturing organisations that are willing to transform their manufacturing system into industry 4.0.

Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis.

This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory.

The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations.

LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning.

LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data.

To the best of the authors’ knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.