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This study examines the correlation between Competitive Intelligence (competitive intelligence) maturity levels and organisational decision-making styles in software industry firms. For this purpose, we followed the competitive intelligence maturity model established by Zuquetto and Beltrame [(2012). Modelo de maturidade em inteligência competitiva. Global Manager Acadêmica, 1(2), 1–19] and the Organisational Decision-Making Perception Scale (ODMPS) model proposed by Nascimento and Gomide Jr [(2014). Tomada de decisão organizacional. In Novas medidas do comportamento organizacional: ferramentas de diagnóstico e de gestão, M. M. M. Siqueira (ed.), pp. 298–306. Porto Alegre: Artmed] was used to assess organisational decision-making styles. A quantitative study was carried out using a closed-ended questionnaire and a Likert scale, targeting managers of software companies. It is worth noting that technical abbreviations were clarified when first used. In addition, the language used in the study was objective, value-neutral and free of ornamental or figurative language. The structure of the study was clear and concise, with logical causality between each statement. The text followed scientifical guidelines for structure, terminology and footnoting. The results show that a significant number of companies have an intermediate level of competitive intelligence maturity and that the systematic approach to decision-making is ubiquitous. Hence, the Spearman Rank Correlation Coefficient (i.e. Spearman’s rho) indicates a positive correlation between competitive intelligence maturity and systematic decision-making in software companies. So, this research helps to understand the relationship between competitive intelligence and organisational decision-making in software companies. According to the findings, organisations with higher levels of competitive intelligence maturity are more likely to adopt a systematic decision-making style. This is noteworthy because systematic decision-making can lead to improved organisational outcomes. The research has implications for academics and practitioners alike. It provides new insights into the relationship between competitive intelligence and organisational decision-making. For practitioners, the findings suggest that investing in competitive intelligence and adopting a methodical approach to decision-making can improve organisational performance. The relationship between competitive intelligence maturity level and organisational decision-making styles in different industries should be explored in further research.

Digitization makes knowledge and information a centerpiece since these connect all dimensions affected by innovation, i.e., people, processes, organization, business, and technology. Maturity Models (MMs) support managers in this evolution. The paper provides a theoretical formalization of MMs to give a practical knowledge contribution to increasing the intensiveness of information in enterprises, through digitization evolution. The paper reviews the main MMs, and presents their state of the art. Then, it defines a backbone structure common to MMs to abstract and describe their features by a meta-model. The new meta-model-driven approach guides companies in the selection of MMs, and in designing a new MM, where needed. The meta-model formalizes the two-levels inputs, the process, and the output to align the company’s motivations with the MM features, resulting in the definition of an appropriate MM for organizations. A qualitative exploratory case study shows the approach and its results, providing guidelines for future actions.

In the current data-driven digital economy, organizations attempt to harness big data power to make their decisions better. The big data analytics assist them not only to identify new opportunities but extract knowledge and obtain better performance. Despite a huge investment in big data analytics initiatives, the majority of organizations have failed to successfully exploit their power. Although big data analytics have received considerable research attention, a little has been done on how organizations implement strategies in order to integrate the different dimensions of big data analytics; hence, a roadmap is required to navigate these technological initiatives. This paper is also an attempt to overcome this challenge by developing a comprehensive big data analytics maturity model to help managers evaluate their existing capabilities and formulate an appropriate strategy for further progress. A mixed-method was applied in this research using a qualitative meta-synthesis approach. For this purpose, first, a systematic literature review was conducted to identify the capabilities and practices of big data analytics maturity. Then the proposed key capabilities and practices were assessed and prioritized based on the opinions of experts using the quantitative survey method. Finally, considering the architecture of the big data analytics maturity model, the capabilities were assigned to maturity levels according to their priority of implementation using a focus group. The proposed model is comprised of four main capabilities, nine key dimensions (KDs) and five maturity levels based on the capability maturity model integration (CMMI) architecture. A questionnaire and a focus group were used to present the big data maturity model. The capabilities and KDs, as well as their implementation order and weight in the proposed maturity model are presented as a roadmap for implementing big data analytics effectively. The proposed model enables organizations to assess their current big data analytics capabilities and navigate them to select appropriate strategies for their improvement. Due to its nature, it allows managers to find their strong and weak points and identify investment priorities. This study provides a comprehensive maturity model using a meta-synthesis which has not been used in this field so far. The proposed model is both descriptive and prescriptive and has a significant theoretical contribution to big data researches. The paper provides a mechanism to benchmark big data analytics projects and develop an appropriate strategy in terms of progress.

This study aims to better understand the process of knowledge management capabilities (KMC) development at the municipal context to assess its impact on organisational performance. This process is a part of the organisational learning school that promotes organisational learning processes and knowledge accumulation that could be spread over time and in different levels of maturity. Thus, using the KMC maturity model (KMCMM) which has five levels of maturity, this study helps to better understand the phenomenon of KMC in the municipal context.

In idea creation and assessment processes, the accruement and the description of an idea are mostly allocated to a fixed point in time, defined as the end of the generating process and the start of the idea assessment. This static approach does not fit the reality in industrial idea processes. A dynamic approach for idea assessment is therefore introduced. An idea is not seen as a static but as a dynamic state, characterized by different degrees of maturity. Maturity is understood as a measure of the assessability of the individual evaluation characteristics.

Based on Crosby’s maturity model and classical capability maturity models, a new idea maturity model (IMM) has been developed and is introduced for the first time. The five idea maturity levels (IML) are named Initial, Awareness, Appraisability, Valuation and Realizability and harmonize well with other maturity models. The levels are described by general characteristics, although the development of the maturity model focussed on new product or service development. The compatibility of the IMM with idea assessment processes and conditions in organizations and companies has been checked.

Roadmapping is an established visual mapping method used widely in a variety of organizations. It navigates technology and strategic planning in a structured manner for improved strategy development and decision-making. However, roadmapping implementation can be challenging particularly when initiating in corporate organizations due to the broad range of stakeholder involvement. This paper examines the background history and lessons learned from IHI Corporation, a leading Japanese heavy industry manufacturer, to provide insights regarding how roadmapping implementation can be initiated effectively in corporate settings. Using the roadmapping maturity model for analysis and discussion, this paper contributes to both industrial practice and academic understanding of roadmapping implementation maturity.

The aim of this paper is to investigate the current landscape of digital maturity models for small- and medium-sized enterprises (SMEs) and the capabilities embedded in the models using a semi-systematic literature review and semi-structured interviews. Based on current knowledge, our research provides insights into the gaps in maturity model research for SMEs. Our primary findings show that digital maturity modelling, particular for SMEs, is currently a significant topic across several industries but remains limited by the lack of procedural properties and suitable SME-specific capabilities. This is supported by our literature review and initial interview findings. The paper offers a conceptual framework including defined criteria and essential properties. Furthermore, the paper outlines a transparency approach to empirical validation to increase the rigor of future maturity model research.

Although the potentials of data analyses appear promising, companies often fail to develop specific ideas und successfully implement complex data analyses. In this paper, we present BMC Data, a novel, stand-alone tool for the comprehensive assessment of the maturity of a company’s business model in terms of data integration. To obtain a holistic view of all facets of a business model, this analysis reflects on all relevant dimensions of data integration. We tested the functionality of BMC Data in a survey aimed at managing directors and persons at management level (sample size $n$ = 158) within a wide range of company sizes and branches. The evaluation shows that BMC Data is suitable for supporting companies in analysing and reflecting on their business model.

Business Process Maturity Models (BPMMs) receive growing attention within digital transformation to evaluate the maturity level of business processes regarding specific capabilities. However, existing models are primarily descriptive, with limited prescriptive features and guidance to improve the current state. Besides, the assessment methods of popular BPMMs do not adequately reflect the indispensable role of information technology (IT), i.e., IT systems and information flow, for business process management (BPM) and process performance. Motivated by a research project with an industry partner, we apply the practice-oriented Action Design Research methodology to introduce our comprehensive and prescriptive “BPMMxIT” maturity model that integrates the IT and BPM perspectives. This study details the development and application procedures and provides generalised design principles to evaluate process performance and capability maturity for digital transformation. Transparency and reproducibility are facilitated by extensive supplementary material. Finally, theoretical implications and pivotal directions for organisational practice and further research are outlined.

One of the most challenging difficulties for incumbent organisations, especially small- and medium-sized enterprises (SMEs), is to manage digital transformation driven by technological change. Incumbent organisations’ responses to digital transformation have been extensively studied in the current literature. However, most research neglects digital transformation in SMEs. There are hardly any valid developed measures for the maturity of digital transformation. We present a holistic digital transformation maturity model based on an extensive literature review, qualitative computer-assisted data analysis, and empirical findings. The digital transformation maturity model focuses on small- and medium-sized enterprises’ unique features and characteristics. We proved the practical applicability and relevance of the digital transformation maturity model in an extensive study involving various organisations, particularly German SMEs ($n$= 310). Organisations can use this model to assess themselves initially and, through this process, gain a comprehensive understanding of the multiple forms of digital transformation.

Enterprises around the world face significant challenges in an increasingly competitive and global economy. Given this scenario companies’ survival depends on their ability to stay ahead in their markets and innovation is considered a pressing need, the key resource for achieving sustainable competitive advantage. Innovations, product and technology development, are transformed into reality through projects and are affected by the management model and the practice adopted in order to be decisive and support the achievement of the organization’s goals. In this way, this paper proposed a conceptual maturity model which was developed for innovation project management through qualitative approach, interviews; and then, through quantitative research involving 162 specialists and project managers who work with innovations from many sectors subsequently, the concept’s applicability was evaluated in the real environment of aerospace enterprises in the Brazilian ecosystem.

The present industrial revolution in modern history, called Industry 4.0, completely changes the vision and processes of modern production and makes it more efficient, high quality, and potentially more profitable. The spreading of Industry 4.0 across all industries did not leave behind the oil and gas sector. More developed countries such as Norway, where the oil and gas industry plays a crucial role, have already initiated Industry 4.0 transformation and progressing toward achieving higher maturity levels. At the same time, developing countries such as Kazakhstan, where the oil and gas sector is also one of the most important industries, make their initial attempts towards Industry 4.0 but in a more cut-rate way. Therefore, this study aimed to conduct a maturity assessment of the oil and gas sectors of two countries, through a new Industry 4.0 readiness assessment model (RAM), developed exclusively for oil and gas companies. As a result, the model helped to identify the current maturity level of enterprises, strengths and weaknesses, and pinpoint country-specific patterns. Although the research focuses primarily on the upstream sector, the developed model and research results can also be extended to address the needs of the midstream and downstream sectors.

The technological evolution that enterprises are facing is increasingly fast and dynamic, demanding more and more flexibility and agility in the application of new technologies in an increasingly globalized and competitive business environment. This work has a broad view of the industry 4.0 influence in the global supply chain companies, as it is essential for long-term business sustainability and one of the key drivers of profitability and growth. The model proposed in this research effort considers the application in the processes of a global supply chain enterprise, which includes from the beginning of the demand request, also the industrial activities, to the post-sale analysis; finally, an unprecedented indicator was developed to measure the level of application of industry 4.0 concepts in organizations according to the state-of-the-art in the evaluated processes. Then, the model was applied to the real case of a global high-tech supply chain company. The first findings demonstrate the ease of understanding and applicability of the model for companies to analyze the digital transformation in their processes and identify gaps to convert them into real opportunities to leverage their business.