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This study aims to develop a modular and prescriptive digital transformation maturity model whose constituent elements have conceptual integrity as well as reveal the priority weights of maturity model components.

A literature review with a concept-centric analysis enlightens the characteristics of constituent parts and reveals the gaps for each component. Therefore, the interdependency network among model dimensions and priority weights are identified using decision-making trial and evaluation laboratory (DEMATEL)-based analytic network process (ANP) method, including 19 industrial experts, and the results are robustly validated with three different analyses. Finally, the applicability of the developed maturity model and the constituent elements are validated in the context of the manufacturing industry with two case applications through a strict protocol.

Results obtained from DEMATEL-based ANP suggest that smart processes with a priority weight of 17.91% are the most important subdimension for reaching higher digital maturity. Customer integration and value, with a priority weight of 17.30%, is the second most important subdimension and talented employee, with 16.24%, is the third most important subdimension.

The developed maturity model enables companies to make factual assessments with specially designed measurement instrument including incrementally evolved questions, prioritize action fields and investment strategies according to maturity index calculations and adapt to the dynamic change in the environment with spiral maturity level identification.

A novel spiral maturity level identification is proposed with conceptual consistency for evolutionary progress to adapt to dynamic change. A measurement instrument that is incrementally structured with 234 statements and a measurement method that is based on the priority weights and leads to calculating the maturity index are designed to assess digital maturity, create an improvement roadmap to reach higher maturity levels and prioritize actions and investments without any external support and assistance.

Industry 4.0 is characterised by systemic transformations occurring exponentially, encompassing an array of dynamic processes and technologies. To move towards a more sustainable future, it is important to understand the nature of this transformation. However, construction enterprises are experiencing a capacity shortage in identifying the transitional management steps needed to navigate Industry 4.0 better. This paper presents a maturity model with the acronym “Smart Modern Construction Enterprise Maturity Model (SMCeMM)” that provides direction to construction enterprises.

It adopts an iterative procedure to develop the maturity model. The attributes of Industry 4.0 maturity are obtained through a critical literature review. The model is further developed through knowledge elicitation using modified Delphi-based expert forums and subsequent analysis through qualitative techniques. The conceptual validity of the model is established through a validation expert forum.

The research defines maturity characteristics of construction enterprises across five levels namely ad-hoc, driven, transforming, integrated and innovative encompassing seven process categories; data management, people and culture, leadership and strategy, automation, collaboration and communication, change management and innovation. The maturity characteristics are then translated into assessment criteria which can be used to assess how mature a construction enterprise is in navigating Industry 4.0.

The results advance the field of Industry 4.0 strategy research in construction. The findings can be used to access Industry 4.0 maturity of general contractors of varying sizes and scales and generate a set of recommendations to support their macroscopic strategic planning.

The purpose of the study is to understand the relationship between family-driven innovation and the incorporation of corporate sustainability in German family firms.

The study conducted 26 interviews with 22 German family firms. Thematic analysis was undertaken on the collected data resulting in five major themes.

The study identified five main themes of corporate sustainability-oriented innovation in family firms, which include measuring corporate sustainability performances, building corporate sustainability-oriented infrastructure, stabilizing/optimizing operations, enhancing operational flexibility/independence and knowledge management and development. The study also provides an activity-based guide for family firms to use innovation to achieve corporate sustainability goals and present the findings’ implications for policymakers.

The present study is the first study to empirically investigate the relationship between family-driven innovation and the incorporation of corporate sustainability at each of the corporate sustainability maturity levels.

This paper aims to investigate whether the perceived level of corruption influences companies' decision to address principles and standards aimed, inter alia, at fighting corruption [i.e. Sustainable Development Goals (SDGs), (2) United Nations Global Compact (UNGC), (3) International Standards Organisation (ISO) 26,000 and (4) Organisation for Economic Co-operation and Development (OECD) Guidelines] in companies' sustainability reporting.

The paper uses a sample of 1,171 sustainability reports published in the year 2017 by organisations from Asia and Africa's low- and middle-income countries.

Results from the Probit model reveal that corruption negatively affects corporate sustainability reporting activity. Indeed, the more companies are exposed to high levels of corruption, the less likely they appear to engage in sustainability reporting. Furthermore, the authors find clear regional and sector-level differences in the extent to which companies engage in sustainability reporting. The results show that Asian companies operating in the agricultural and financial services sectors exhibit significantly higher reporting activity, whilst those operating in the construction and mining sectors report less than the sectors' peers.

The authors' findings provide important implications for understanding companies' behaviour in the sustainability reporting in emerging economies as well as for designing corporate social responsibility (CSR) disclosure initiatives in the future.

This paper provides a better understanding of the impact of corruption on companies' reporting behaviour in the context of emerging economies.

The purpose of this research is to study how compliance evaluation becomes performed in practice. Compliance evaluation is a common practice among organizations that need to evaluate their posture against a set of criteria (e.g. a standard, legislative framework and “best practices”). The results of these evaluations have significant importance for organizations, especially in the context of information security and continuity. The author argues that how these evaluations become performed is not merely a “social” activity but shaped by the materiality of the evaluation criteria

The authors adopt a sociomaterial practice-based view to study the compliance evaluation through in situ participant observations from compliance evaluation workshops to evaluate organizational compliance against a information security and business continuity criteria. The empirical material was analyzed to construct vignettes that serve to illustrate the practice of compliance evaluation.

The research analysis shows how the information security and business continuity criteria themselves partake in the compliance evaluations by operating through (ventriloqually) the evaluators on three strata: the material, the textual and the structural. The author also provides a conceptualization of a hybrid agency.

This research contributes to lack of studies on the organizational-level compliance. Further, the research is an original contribution to information security and business continuity management by focusing on the practices of compliance evaluation. Further, the research has theoretical novelty by adopting the ventriloqual agency as a hybrid agency to study the sociomateriality of a phenomenon.

This paper investigates the relationship between climate finance and climate ergodicity. More specifically the paper examines how climate ergodicity as measured by a mean-reverting Ornstein–Uhlenbeck process affects the value of climate-linked bonds.

Bond valuation is evaluated using Monte Carlo methods of the Ornstein–Uhlenbeck process. The paper describes climate risk in terms of the Hurst coefficient and derives a direct linkage between the Ornstein–Uhlenbeck process and the Hurst measure.

We use the Ornstein–Uhlenbeck mean reversion relationship in its OLS form to estimate Hurst coefficients for 5 × 5° grids across the US for monthly temperature and precipitation. We find that the ergodic property holds with Hurst coefficients between 0.025 and 0.01 which implies increases in climate standard deviation in the range of 25%–50%.

The approach provides a means to stress-test the bond prices to uncover the probability distribution about the issue value of bonds. The methods can be used to price or stress-test bonds issued by firms in climate sensitive industries. This will be of particular interest to the Farm Credit System and the Farm Credit Funding Corporation with agricultural loan portfolios subject to spatial climate risks.

This paper examines bond issues under conditions of rising climate risks using Hurst coefficients derived from an Ornstein–Uhlenbeck process.

This study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023.

The study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing.

The study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes.

These findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market.

The Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants.

This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes.

We highlight how factors like volatility, futures volume, and open interest vary in their impact.

The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing.

We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities.

Blockchain for energy trading is a trending research area in the current context. However, a noticeable gap exists in the review articles focussing on solar energy trading with blockchain technology. Thus, this study aims to systematically examine and synthesise the existing research on implementing blockchain technology in sustainable solar energy trading.

The study pursued a systematic literature review to achieve its aim. The data extraction process focussed on the Scopus and Web of Science (WoS) databases, yielding an initial set of 129 articles. Subsequent screening and removal of duplicates led to 87 articles for bibliometric analysis, utilising VOSviewer software to discern evolutionary progress in the field. Following the establishment of inclusion and exclusion criteria, a manual content analysis was conducted on a subset of 19 articles.

The results indicated a rising interest in publications on solar energy trading with blockchain technology. Some studies are exploring the integration of new technologies like machine learning and artificial intelligence in this domain. However, challenges and limitations were identified, such as the absence of real-world solar energy trading projects.

This study offers a distinctive approach by integrating bibliometric and manual content analyses, a methodology seldom explored. It provides valuable recommendations for academia and industry, influencing future research and industry practices. Insights include integrating blockchain into solar energy trading and addressing knowledge gaps. These findings advance societal goals, such as transitioning to renewable energy sources (RES) and mitigating carbon emissions, fostering a sustainable future.

Data-driven innovation is a key pillar for economic development in the 21st century, accordingly the role and value of data in developing new products, services and business models has gained attention. The study aims to identify three important factors explaining the challenges faced by young entrepreneurial firms when seeking to use public data in their business development.

This study presents the user journeys of five data-driven start-ups in the capital region of Norway aiming to use public data in their business development process. Multiple methods for data collection were used, such as interviews, observations and secondary data.

The study reveals how differences in culture, communication and work methods across stakeholders and sectors affect and delay the process of gaining access to data. Moreover, how various forms of knowledge are expressed differently throughout the user journeys of the start-up firms, with stronger dependency on explicit and codified in early phase, and tacit and experience-based knowledge in later stages, each stage relating to the maturity of the entrepreneurial ecosystem for data-driven innovation. Therefore, the ecosystem for data-driven innovation may be further developed through arenas for collaboration that simplify the process for access to – and use of public data.

The study reveals how young entrepreneurial firms identify opportunities whilst simultaneously facing substantial barriers when they seek to take advantage of publicly available data and transforming them into value creating activities. The study contributes to the theoretical understanding of data-driven innovation by exploring the user journeys of start-up firms and identifying the challenges they face when utilizing public data, highlighting the importance of a mature ecosystem, collaboration for market development and the evolving role of tacit and experience-based knowledge throughout the business development process.

This study introduces the Digital Maturity Assessment Model (DMAM), a model tailored to assess the digital maturity of SMEs, tracing its development from addressing business challenges to establishing a comparative analysis framework grounded in Resource Dependence Theory (RDT).

DMAM is based on positivist philosophy and objectivist epistemology, supported by Design Science Research (DSR) and Capability Maturity Model Integration (CMMI). The methodology involves iterative development, from problem identification to creating a practical solution for assessing SMEs' digital maturity and guiding digitalization efforts.

DMAM offers a clear and specific methodology, distinguishing itself by addressing the unique needs of SMEs, particularly resource-dependent ones. The model’s development fills critical gaps in existing literature and provides a practical artifact for SMEs' digitalization.

DMAM is original in its focus on the specific needs of resource-dependent SMEs, offering actionable recommendations and addressing shortcomings in existing models. It serves as a foundational framework for SMEs' digital transformation, making a significant contribution to the digital maturity assessment literature.