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Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model.

A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data.

The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly.

Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking.

This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.

In order to address the gender divide in technology entrepreneurship, we explore how different national contexts impact policies and policy implementation. We investigate how transnational concerns (macro level) about women’s low participation in (technology) entrepreneurship are translated and implemented amongst actors at the meso level (technology incubators) and understood at the micro level (women tech entrepreneurs).

We adopt gender institutionalism as a theoretical lens to understand what happens in the implementation of gender equality goals in technology entrepreneurship policy. We apply Gains and Lowndes’ (2014) conceptual framework to investigate the gendered character and effects of institutional formation. Four countries represent different levels of gender equality: high (Norway and Sweden), medium (Ireland) and low (Israel). An initial policy document analysis provides the macro level understanding (Heilbrunn et al., 2020). At the meso level, managers of technology business incubators (n = 3–5) in each country were interviewed. At the micro level, 10 female technology entrepreneurs in each country were interviewed. We use an inductive research approach, combined with thematic analysis.

Policies differ across the four countries, ranging from women-centred approaches to gender mainstreaming. Macro level policies are interpreted and implemented in different ways amongst actors at the meso level, who tend to act in line with given national policies. Actors at the micro level often understand gender equality in ways that reflect their national policies. However, women in all four countries share similar struggles with work-life balance and gendered expectations in relation to family responsibilities.

The contribution of our paper is to (1) entrepreneurship theory by applying gendered institutionalism theory to (tech) entrepreneurship, and (2) our findings clearly show that the gendered context matters for policy implementation.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

The purpose of this paper is to analyse innovation policies in India from 1958 to 2020. A policy tools framework was developed to compare the innovation policies in India. India developed and implemented four innovation policies from 1958 to 2013. The recent policy change was brought in the year 2020 with releasing the draft of the Science, Technology and Innovation Policy (STIP 2020). The authors analysed the recent draft of STIP 2020 with the earlier four innovation policies.

Innovation policies implemented from 1958 to 2013 in India were studied and analysed in the “text as a data approach” and a comparative policy analysis tool was designed for this purpose. The recent draft of STIP 2020 was evaluated and the provisions of the fifth draft of STIP 2020 were compared with the previous four innovation policies' design and formulation. The CPA tool design consists of five broad themes Awareness and capacity building; Finance and infrastructure; Resource management and governance; Outreach and networking; and Policy implementation and evaluation.

Draft STIP 2020 has many features similar to earlier policies. However, policy has focused on bringing in more clarity about national challenges, goals and objectives, yet it needs better implementation to achieve stated outcomes more effectively and efficiently. New initiatives include strengthening the innovation system with open science, improving STI education, expanding the financial landscape, establishing national STI observatory acting as a central repository of all data related to the STI ecosystem and accountable research ecosystem, promoting translational and foundational research of global standards, promoting entrepreneurship, self-reliance, mainstream science communication and public engagement and decentralised institutional mechanisms. Thus, the STIP 2020 is ambitious in its approach to promoting STI in India and needs a supportive mechanism to achieve the stated objectives.

Current comparative policy analysis focused only on identifying similarities and differences among innovation policies implemented in India from 1958 to 2020 and its evolutionary changes in policy and its instruments choice. The recent draft STIP 2020 is not approved and has no update regarding its approval and implementation by the government. The revised and yet to implement STIP 2020 may have variances in the policy instruments. The earlier policies are so broad and without specific problem statements. They were released as statements and resolutions which makes it is challenging to understand the impact of each policy. Similar policy tools in STI 2013 and STIP 2020 were only considered to observe the policy instrument choice. The achievements of the innovation policies implemented before STIP 2020 are not included. Evolutionary changes in the problem statements and policy tools prescribed in innovation policies are studied.

Department of Science and Technology, Government of India, released a draft of STIP in 2020. The draft of STIP was evaluated with previous Innovation Policies. To the authors’ understanding, this is the first attempt to evaluate the STIPs of India using the “text as a data” approach. The tool can be validated by using it for CPA of innovation policies of other emerging, developed and least developed economies to understand cross-country variations in policy instrument choice by policymakers.

The border closure and lockdowns due to Covid-19 pandemic resulted in partial closure of many industrial and commercial complexes, halted the performance of key strategic sectors such as logistics and supply chains, and thus disrupted the global value chains and the economy. The authors argue, however, that the pursuit of survival has driven companies to innovate and use digitization to overcome the negative consequences of the pandemic. More specifically, in this paper the authors aim to assess the success and challenges faced by companies in digitization policy design, adoption and implementation and their effects on firms’ operation, outputs and customer base during Covid-19.

Sixty-one samples of the companies surveyed between 10 January and 30 April 2021 were analyzed, using the Krushkal–Wallis test and Independent-Samples Mann–Whitney U test to identify the relationships between variables including operation, overall output, customer base, digitization policy, technology use and implementation costs of new technologies.

Results revealed a positive impact of digitization on the operation and overall outputs, while no effect was observed on the customer base. Analysis also showed that only 1.8% of companies were able to fully implement digitization, and that the cost of technology prevented most companies from using emerging technology or implementing their digitization policy.

While the research has practical implications, it is not without flaws. For instance, the outcome of technology varies as per geographic area and people. The study was conducted in the Sultanate of Oman, a developing country in the Middle East region; therefore, it is difficult to generalize the outcomes suited to developed countries. The developed countries usually have a population quite used to the advanced technologies so some of the issues raised in the study might not work in the logistics and supply chain sectors of the developed countries. Such countries need separate studies.

The findings will have implications for both supply chain companies as well as the technology providers. The supply chain companies will invest in technology infrastructure and add technology as an important component in their business models. The technology providers will consider the costs of implementation and adoption issues of technology in the supply chain companies.

To the best of authors' knowledge, no work has been produced on logistics and supply chain companies considering the technological sustainability during the time of Covid-19. The study will improve understanding of the digitization policy design, adoption and implementation and their effects on logistics and supply chain companies’ performance.

Promoting electric vehicles (EVs) is an effective way to achieve carbon neutrality. If EVs are widely adopted, this will undoubtedly be good for the environment. The purpose of this study is to analyze the impact of network externalities and subsidy on the strategies of manufacturer under a carbon neutrality constraint.

In this paper, the authors propose a game-theoretic framework in an EVs supply chain consisting of a government, a manufacturer and a group of consumers. The authors examine two subsidy options and explain the choice of optimal strategies for government and manufacturer.

First, the authors find that the both network externalities of charging stations and government subsidy can promote the EV market. Second, under a relaxed carbon neutrality constraint, even if the government’s purchase subsidy investment is larger than the carbon emission reduction technology subsidy investment, the purchase subsidy policy is still optimal. Third, under a strict carbon neutrality constraint, when the cost coefficient of carbon emission reduction and the effectiveness of carbon emission reduction technology are larger, social welfare will instead decrease with the increase of the effectiveness of emission reduction technology and then, the manufacturer’s investment in carbon emission reduction technology is lower. In the extended model, the authors find the effectiveness of carbon emission reduction technology can also promote the EV market and social welfare (or consumer surplus) is the same whatever the subsidy strategy.

The network externalities of charging stations and the subsidy effect of the government have a superimposition effect on the promotion of EVs. When the network effect of charging stations is relatively strong, government can withdraw from the subsidized market. When the network effect of charging stations is relatively weak, government can intervene appropriately.

Comparing previous studies, this study reveals the impact of government intervention, network effects and carbon neutrality constraints on the EV supply chain. From a sustainability perspective, these insights are compelling for both EV manufacturers and policymakers.

The purpose of this study is to explore how digital construction policy (DCP) drives enterprise green innovation (EGI) from an information processing theory (IPT) perspective, including the mediating mechanisms of market information accessibility and operational risk, the moderating role of intellectual property protection (IPP) and product market competition (PMC) and the heterogeneous effects of ownership, Internet development and managerial ability.

Based on the matched panel data of A-share listed enterprises from 2011 to 2019 and the Broadband China policy as a quasinatural experiment, this study investigates the impact of DCP on EGI by constructing a multi-time point difference-indifferences (DID) model.

Digital construction policies can significantly promote EGI. DCP works in two fundamental ways, namely by increasing market information accessibility and reducing operational risk. IPP and PMC significantly increased the contribution of digital construction policies to EGI. Heterogeneity analysis found that digital technology has a stronger promotion effect for SOEs, high-managerial-ability enterprises and enterprises in regions with low Internet development levels.

The study provides new insights about the antecedents of EGI from a DCP perspective. It also enlightens emerging economies to actualize green innovation under the digital wave.

From the perspective of IPT, this study explains the mechanism of DCP-driven EGI. It enhances understanding of the relationship between DCP and EGI.

This study aims to empirically examine how blockchain, one of the emerging Industry 4.0 technologies, can combat climate change by improving their green innovation performance, particularly under conditions of policy uncertainty.

This study utilizes the difference-in-difference-in-difference (DDD) method to explore the effect of blockchain on enterprises' green innovation performance. The analysis is based on data from Chinese-listed enterprises spanning the period from 2013 to 2021.

First, the adoption of blockchain in enterprises registered in areas designated as low-carbon pilot cities can significantly improve their green innovation performance. Second, the enhancement of green innovation efficiency emerges as the primary driving force behind the adoption of blockchain, thereby leading to improved green innovation performance. Lastly, it is observed that blockchain adoption has a greater positive impact on improving green efficiency in private enterprises compared to state-owned enterprises in China.

For managers, the findings can provide valuable insights to help them better prepare for the challenges and opportunities presented by the era of Industry 4.0. For policymakers, this study offers valuable insights into the interaction between new technologies in Industry 4.0 and the performance of green innovation, thereby aiding in the formulation of effective policies.

This study contributes to bridging the existing gap between the adoption of new technologies, such as blockchain, and their potential impact on climate change. Moreover, this research enriches practitioners' understanding of how new technologies in the era of Industry 4.0 can be applied to address significant challenges like climate change.