Data science, intelligence and future analysis
Mohammad Amin Yalpanian; Iman Raeesi Vanani; Mohammad Taghi Taghavifard
Abstract
The ever-increasing development of digital technologies has brought about significant changes in business performance. The increase in the number of published articles on this topic also shows the special attention of researchers in information systems, business management, and innovation. While digital ...
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The ever-increasing development of digital technologies has brought about significant changes in business performance. The increase in the number of published articles on this topic also shows the special attention of researchers in information systems, business management, and innovation. While digital changes are inevitable in the digital age, previous research has been limited to a specific domain. This research aims to identify key themes and macro topics through a systematic review of 201 articles from 2018 to 2023 through two high-quality databases (Scopus and Web of Science). First, using thematic analysis, the main themes are identified, and their relationships are investigated from the perspective of digital technology development. In the next step, by using topic modeling (Latent Dirichlet Allocation), the major domains of the impact of these technologies will be investigated, and future research trends will be identified using the scientometric approach. The innovation of this research is designing a thematic network through in-depth text review and text mining analysis, which leads to a better understanding of the relationships between critical components. In the last step, recommendations are given to researchers and managers to conduct future research.
Management approaches in the field of smart
Shiva sadat Ghasemi; Abbas khamseh; Seyed Javad Iranban
Abstract
In the contemporary landscape of technology-driven industries, the integration of artificial intelligence into technology scouting is imperative for enhancing innovation and sustaining competitiveness. This research aims to forge a framework for technology scouting based on artificial intelligence, with ...
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In the contemporary landscape of technology-driven industries, the integration of artificial intelligence into technology scouting is imperative for enhancing innovation and sustaining competitiveness. This research aims to forge a framework for technology scouting based on artificial intelligence, with a specific focus on technology-based companies. Employing a qualitative approach, data collection utilized the meta-synthesis method devised by Sandelowski and Barroso. This involved a systematic review of 28 articles relevant to the research goal out of a pool of 253 primary articles. The final selection of articles was based on predefined inclusion criteria. The research's validity was confirmed through adherence to criteria, team meetings, expert consultations, and an exhaustive audit for theoretical consensus, while reliability was ascertained through the Critical Evaluation Skills Programme. The framework spans five dimensions: technology scouting tools, technology life cycle, firm environment, firm's approach to the environment, and firm's absorptive capacity. The findings underscore the pivotal role of AI-based technology scouting tools, elucidate the nuanced dynamics of the technology life cycle, and reveal the multifaceted aspects of the enterprise environment. The research outlines strategic approaches for navigating the evolving technology landscape, underscoring the imperative of absorptive capacity for the effective utilization of artificial intelligence technologies. By delivering actionable insights and strategic counsel, this research serves to furnish technology-based companies with a robust underpinning for negotiating the intricate intersection of AI and technology surveillance. In doing so, it propels sustainable growth, fortifies competitive advantage, and fosters enduring innovation.IntroductionIn the dynamic world of technology-driven industry, the role of strategic technology management, particularly in the technology selection and acquisition phases, cannot be overemphasized if success is sought in innovation-driven companies. Focusing on technology-oriented companies that currently face a rapid industrial evolution, the present study highlights the indispensable role of technology scouting, equipped specifically with artificial intelligence (AI), in grappling with the imminent competitive environment. The study proposes a framework that anticipates a future where AI plays a central role in technology acquisition and that strives to enhance absorptive capacities by bridging the adaptation gap. Drawing upon AI, the propsoed framework not only ensures proper technology selection by firms but also drives them toward cutting-edge technological innovations. Serving as a guide for decision-makers, technology strategists, and specialists, the study is expected to contribute, both theoretically and practically, to the understanding and advancement of technology scouting in tech-driven companies. Moreover, it explores and identifies the needs of organizations navigating the intricate technology landscape to derive actionable insights that ensure sustainable innovation leadership.What is the framework for technology scouting based on artificial intelligence in technology-oriented companies? Literature ReviewIn today's rapidly evolving tech landscape, it is essential to cope with the changing business environment (Kujawa and Paetzold, 2019). Ahammad et al. (2021) linked strategic agility to search strategies. Wang and Quan (2021) studied the impact of technology selection uncertainty on firms’ absorptive capacity. Vuorio et al. (2018) explored the significance of competitive edge in tech-driven enterprises. Kerr and Phall (2018) developed a scouting process model. Nasullaev et al. (2020) reiterated the alignment of strategy and tech scouting. Xu et al. (2021) advocated patent analysis in scouting. Sikandar et al. (2021) reiterated patents' innovation measure. Tabrizi et al. (2019) observed a shift to tech-centric business models. Stute et al. (2021) noted the importance of AI in supply chain enhancement. Mariani et al. (2023) classified the motivations underlying AI adoption. Stahl et al. (2023) addressed AI ethics while D'Almeida et al. (2022) categorized AI applications. Wang et al. (2020) identified AI algorithms. Despite these efforts, scant research has been reported on tech transformation, especially AI. This study adopts the meta-synthesis method to explore the digital transformation complexities, focusing on AI's transformative potential and bridging the gaps to derive a roadmap for navigating tech-driven industries. MethodologyEmploying a qualitative approach and the meta-synthesis method, a seven-step process (including goal setting, review, selection, extraction, analysis, quality control, and model development) was meticulously followed to develop an AI-based technological scouting model for advanced tech firms. A systematic search yielded 253 articles, 28 of which met the inclusion criteria and were validated through team meetings, software analysis, and expert consultation. Reliability was ensured since 89% of the articles received excellent scores via the Critical Evaluation Skills Program, indicating high quality. ResultsThe research adopted a classified analysis perspective, utilizing inductive analysis based on Sandelowski and Barroso (2007). This involves extracting primary codes related to AI-based technology observation in high-tech companies, identifying patterns through open coding, and classifying concepts into sub-categories and main categories via axial coding. Table 1Factors Affecting AI-Based Technology ScoutingCategorySubcategoryConceptsTechnology Scouting ToolOpen Source Intelligence (OSINT) ToolsWeb scraping tools, social media monitoring, online forums, patent databases, news aggregators, competitive intelligence tools, and data analytics platforms.Machine Learning and AI ToolsNatural Language Processing (NLP), predictive analytics, pattern recognition, chatbots, sentiment analysis, machine learning, and cognitive computing tools.Collaboration and Communication PlatformsOnline collaboration tools, project management platforms, virtual team collaboration, idea management, crowdsourcing, communication apps, and workflow automation.Technology Life CycleInnovation and InventionIdea generation, R&D, concept testing, prototyping, patenting, technology transfer, proof of concept, funding, collaborative research, and feasibility studies.Technology Adoption and DiffusionTechnology readiness, market analysis, adoption theories, market penetration, standardization, compliance, user testing, and overcoming adoption barriers.Technology Evolution and ObsolescenceContinuous improvement, iterative development, versioning, obsolescence management, legacy systems, discontinuation planning, sustainability, disruptive tech, and sunset planning.Company EnvironmentCompetitive Landscape AnalysisCompetitor mapping, SWOT analysis, industry benchmarking, market share analysis, competitive intelligence, PESTLE analysis, collaboration strategies, positioning, and sustainable advantage.Regulatory and Legal EnvironmentIntellectual property management, standards compliance, regulatory impact, patent landscape analysis, legal risk, data protection, ethics, antitrust, government policies, and international regulations.Internal Organizational EnvironmentCulture, cross-functional collaboration, governance, change management, talent, agile structures, infrastructure, decision-making, metrics, and employee engagement.The Company's Approach in Facing the EnvironmentInnovation Strategy FormulationRoadmapping, open innovation, blue ocean strategy, core competency analysis, innovation ecosystems, portfolio management, ambidextrous approach, horizon scanning, lean methodologies, and design thinking.Adaptive and Resilient PracticesCrisis management, scenario planning, risk management, agile project management, supply chain resilience, continuous learning, adaptive capabilities, technology portfolio flexibility, and fostering innovation culture.Strategic Alliances and PartnershipsCollaborative innovation, joint ventures, technology ecosystems, university-industry collaborations, innovation networks, open source, licensing, technology transfer, competition, and strategic partnerships.Absorption Capacity of the CompanyLearning and Knowledge ManagementOrganizational learning, knowledge creation, sharing platforms, communities of practice, intellectual capital, training programs, technology scouting, learning culture, and tacit knowledge transfer.Resource Allocation and UtilizationTechnology budgeting, allocation models, ROI analysis, portfolio management, cross-functional sharing, resource efficiency, project prioritization, dynamic reallocation, innovation finance, and risk management.Adoption of Emerging TechnologiesScanning trends, piloting new tech, foresight methodologies, early adoption, readiness assessments, and collaborative ecosystems for adoption, mitigating risks, cross-functional teams, integration, and continuous monitoring. DiscussionTo address the crucial gap in technology scouting in technology-oriented companies involved in the joint AI and technology scouting, the study develops a framework of five dimensions. Open-source smart tools and machine learning are explored as essential components of the "Technology Scouting Tool"dimension to contribute to the development of a cohesive strategy. The "Technology Life Cycle" dimension guides the firm through the innovation, adoption, and evolution stages. The "Company Environment" dimension adopts a multifaceted approach, considering competitive analysis, regulatory factors, and internal dynamics. The strategic components of the "Firm's Approach to the Environment" underline the contributions of innovation strategy, adaptability, and alliances while "Firm's Absorptive Capacity" offers practical insights by underscoring learning, resource allocation, and technology adoption. ConclusionThe proposed framework provides a strategy tailored for tech-oriented firms incorporating AI into scouting and offers strategic insights across the five dimensions to tackle nuanced challenges in the technology landscape. Advocating advanced open-source tools and strategic approaches, it explores the technology life cycle, considers diverse aspects of firm environment, and launches an AI-driven future. Acknowledging limitations and emphasizing proper deployment of AI, the study lays the foundations for future studies to validate and expand the framework while ensuring responsive and sustainable application of AI-based surveillance technologies in corporate contexts. Keywords: Artificial intelligence, Technology scouting, Technology-oriented companies, Digital transformation.
Mohsene Asadi; Mehdi Shami Zanjani
Abstract
For today's businesses, the move to digital transformation and the use of disruptive technologies for survival and growth is inevitable and can create many innovative opportunities for them. In order for organizations to move in this direction and design a roadmap for their digital transformation, they ...
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For today's businesses, the move to digital transformation and the use of disruptive technologies for survival and growth is inevitable and can create many innovative opportunities for them. In order for organizations to move in this direction and design a roadmap for their digital transformation, they must first have a comprehensive and holistic understanding of the current digital situation of themselves. Assessing digital maturity can be the first step in developing the roadmap of digital transformation. Since digital transformation is not a one-dimensional issue and involves many dimensions in the organization, so identifying and paying attention to these dimensions can make it easier to plan for the digital transformation of the organization. This is possible with the help of digital maturity models. This study was conducted to provide a framework for assessing digital maturity. The research method used here was a systematic literature review. Dimensions of digital maturity in this framework include "strategy", "governance and leadership", "business model and ecosystem", "culture and skills", "process", "employee experience", "customer experience", "technology", "data" "Innovation" were identified and then 69 indicators related to each dimension were introduced.
Mahdi Hamidi; Seyed Soroush Ghazinoori; Mohammad Naghizadeh; Naser Bagheri Moghaddam
Abstract
vIntroductionBig data and digital technologies, such as artificial intelligence (AI), blockchain, the Internet of Things (IoT), and robotics, will transform businesses in many ways. This process of adapting to these changes is called digital transformation. Companies and policymakers in different countries ...
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vIntroductionBig data and digital technologies, such as artificial intelligence (AI), blockchain, the Internet of Things (IoT), and robotics, will transform businesses in many ways. This process of adapting to these changes is called digital transformation. Companies and policymakers in different countries are implementing various programs, strategies, and policies to achieve digital transformation. However, they face many challenges and obstacles that need to be identified and addressed. This research provides a comprehensive classification of the barriers to digital transformation using the meta-synthesis method including 38 selected articles.Literature ReviewThe literature on this topic has proposed various classifications of the barriers to digital transformation. This research focuses on the barriers from the perspective of digital transformation management and excludes the studies that examine the barriers from a technical perspective. The literature on this topic can be divided into three main approaches: "examining the barriers to digital transformation across industries and companies", "innovation approach" and "barriers to organizational change".Some of the barriers mentioned in the literature are:Lack of standards, lack of legal basis for data use, integration with existing system environment, dependence on other technologies, and fear of transparency/acceptance (Vogelsang et al, 2019)Law/standards, management, and workforce (Bailie & Chinn, 2018)Strategy, business model, business processes, organizational structures, and organizational culture (Vukˇ si´, 2018)The conflict between physical and digital systems, as well as the pervasive mindsets that are ingrained in the organization's culture (Nate & Erica, 2014).Lack of guidance from the government and hence resistance to change; Changing the operating models requires replacing a large number of equipment and systems and involves significant capital; The interaction of different technologies may cause problems in a complex system; The adoption of new technologies requires a certain time for evaluation, and it takes time to adjust the strategy according to the new environment; Lack of overall planning and standardization (Lu et al, 2019).MethodologyThis research is a descriptive-applied study that uses the meta-synthesis method. In this study, the seven-stage model of Sandusky and Barroso (2006) is applied. Based on the research objectives and questions, as well as the theoretical foundations related to the barriers to digital transformation, relevant articles were searched using the keywords in the national and international scientific databases. This search resulted in finding 173 articles related to the keywords. After identifying the articles, the models and concepts presented in them were coded. In this study, open, axial, and selective coding were performed. In the next step, the internal validity and reliability of the codings were checked.ConclusionAs mentioned in the literature, the technological change in the digital field can be analyzed as a technological system and from the perspective of socio-technical transitions (Reinhardt, 2022). Different approaches for analyzing technological transitions are of interest in the literature. This study has chosen a multi-level perspective for this analysis. This perspective views the transition as a historical pattern that can be depicted in three different layers. According to this perspective, technology transition can be conceptualized as three nested levels: landscape, socio-technical regime, and niche.Using this perspective, at the landscape level, the environment of a system is examined, and the two sub-categories of government and society are identified as barriers to digital transformation. Moreover, based on the systemic approach to the topic, the socio-technical regime, which is the system that governs the industry or the field studied in this study, is proposed. The basis of companies developing digital technologies should be analyzed and investigated according to the concept of barriers to digital transformation. In this thesis, this category is explained and the dimensions related to each category are discussed in detail.Considering the scope of the study, this framework does not examine the internal relationships between the variables (sub-categories) under the categories. It seems that suggesting the relationship between sub-categories in the proposed framework can be a recommendation for future studies. Also, examining the solutions to overcome the barriers identified in a case study can be suggested as another recommendation for future studies.Keywords: Digital Transformation, Barriers, Meta-Synthesis, Multi-Level Perspective (MLP). IntroductionBig data and digital technologies, such as artificial intelligence (AI), blockchain, the Internet of Things (IoT), and robotics, will transform businesses in many ways. This process of adapting to these changes is called digital transformation. Companies and policymakers in different countries are implementing various programs, strategies, and policies to achieve digital transformation. However, they face many challenges and obstacles that need to be identified and addressed. This research provides a comprehensive classification of the barriers to digital transformation using the meta-synthesis method including 38 selected articles.Literature ReviewThe literature on this topic has proposed various classifications of the barriers to digital transformation. This research focuses on the barriers from the perspective of digital transformation management and excludes the studies that examine the barriers from a technical perspective. The literature on this topic can be divided into three main approaches: "examining the barriers to digital transformation across industries and companies", "innovation approach" and "barriers to organizational change".Some of the barriers mentioned in the literature are:Lack of standards, lack of legal basis for data use, integration with existing system environment, dependence on other technologies, and fear of transparency/acceptance (Vogelsang et al, 2019)Law/standards, management, and workforce (Bailie & Chinn, 2018)Strategy, business model, business processes, organizational structures, and organizational culture (Vukˇ si´, 2018)The conflict between physical and digital systems, as well as the pervasive mindsets that are ingrained in the organization's culture (Nate & Erica, 2014).Lack of guidance from the government and hence resistance to change; Changing the operating models requires replacing a large number of equipment and systems and involves significant capital; The interaction of different technologies may cause problems in a complex system; The adoption of new technologies requires a certain time for evaluation, and it takes time to adjust the strategy according to the new environment; Lack of overall planning and standardization (Lu et al, 2019).MethodologyThis research is a descriptive-applied study that uses the meta-synthesis method. In this study, the seven-stage model of Sandusky and Barroso (2006) is applied. Based on the research objectives and questions, as well as the theoretical foundations related to the barriers to digital transformation, relevant articles were searched using the keywords in the national and international scientific databases. This search resulted in finding 173 articles related to the keywords. After identifying the articles, the models and concepts presented in them were coded. In this study, open, axial, and selective coding were performed. In the next step, the internal validity and reliability of the codings were checked.ConclusionAs mentioned in the literature, the technological change in the digital field can be analyzed as a technological system and from the perspective of socio-technical transitions (Reinhardt, 2022). Different approaches for analyzing technological transitions are of interest in the literature. This study has chosen a multi-level perspective for this analysis. This perspective views the transition as a historical pattern that can be depicted in three different layers. According to this perspective, technology transition can be conceptualized as three nested levels: landscape, socio-technical regime, and niche.Using this perspective, at the landscape level, the environment of a system is examined, and the two sub-categories of government and society are identified as barriers to digital transformation. Moreover, based on the systemic approach to the topic, the socio-technical regime, which is the system that governs the industry or the field studied in this study, is proposed. The basis of companies developing digital technologies should be analyzed and investigated according to the concept of barriers to digital transformation. In this thesis, this category is explained and the dimensions related to each category are discussed in detail.Considering the scope of the study, this framework does not examine the internal relationships between the variables (sub-categories) under the categories. It seems that suggesting the relationship between sub-categories in the proposed framework can be a recommendation for future studies. Also, examining the solutions to overcome the barriers identified in a case study can be suggested as another recommendation for future studies.
Mostafa Ghelichkhani; yahya Samadi Moghadam; Kiamars Fathi Hafashjani
Abstract
Effecting fundamental changes in all organizational dimensions, digital transformation refers to the aggregation and integration of digital technologies in all areas of an organization. The design science methodology was used in this study to propose a maturity assessment model for digital transformation ...
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Effecting fundamental changes in all organizational dimensions, digital transformation refers to the aggregation and integration of digital technologies in all areas of an organization. The design science methodology was used in this study to propose a maturity assessment model for digital transformation in industrial organizations. . For data collection in the qualitative phase, a systematic review of the literature was conducted to analyze relevant papers within the 2015–2019 period. As a result, 49 papers were selected. At the same time, the experts were interviewed. Axial and theoretical coding phases were then implemented through the grounded theory in MAXQDA 10 to classify data as four dimensions and 12 categories. Causal conditions, context conditions, intervening conditions, strategies, and consequences were then identified to design the paradigm model. In the quantitative phase, research questionnaires were used for data collection, and the structural equation modeling technique was employed for model testing in SmartPLS. According to the ISO 15504, the capability maturity model for digital transformation was designed at incomplete, initial, performed, managed, established, and optimized levels in order to make research practical, and a corresponding 48-item researcher-made questionnaire was then developed. The proposed model was analyzed in an industrial organization of the electronics sector to determine the organizational maturity level.the result showed that the organization is at the second level of maturity and transformation has begun in it.the focus of the organization has been on the technology aspect and it is necessary to develop organization aspects accordance with them.
