Sara Sabaghchi; Sepehr Ghazinoory; Fatemeh Saghafi; صحرایی Sahraei
Abstract
The emergence of a wide range of digital technologies has fundamentally changed the nature, process and outcomes of innovation. Researchers emphasize the distinction between digital innovation and traditional innovation and consider it necessary to theorize in this field due to the novelty of the concept ...
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The emergence of a wide range of digital technologies has fundamentally changed the nature, process and outcomes of innovation. Researchers emphasize the distinction between digital innovation and traditional innovation and consider it necessary to theorize in this field due to the novelty of the concept of digital innovation. In this research, in an inductive way with the aim of conceptualizing and identifying the main dimensions of digital innovation in industrial organizations, first, using the systematic literature review approach, related articles were examined in the period from 2014 to 2022, and 48 articles were selected for analysis. Then, using the grounded theory approach, open, axial, and selective coding procedures were performed. According to this, the main dimensions of digital innovation were categorized in the form of a paradigm model include causal factors (13 categories under the 4 main dimensions of digital industrial platform and technology management, digital innovation ecosystem management, intelligent processes, digital structure and organization), contextual factors, intervening factors, strategies, and consequences. Also, by reviewing the definitions and identified dimensions, a novel definition for digital innovation in industrial organizations was presented. The resulting theoretical framework, in addition helping further conceptualization of this phenomenon, can be used in the design of organizations' digital innovation maturity models.
Data science, intelligence and future analysis
Manijeh Ramsheh; mohammad hasan maleki; narges sarlak; monireh falahat bangdeh
Abstract
Fintech and its entrepreneurial opportunities have the ability to play an effective role in the development of the financial industry. Therefore, it is necessary to make a correct and effective policy in this area to identify its probable future. This study is exploratory in terms of purpose and practical ...
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Fintech and its entrepreneurial opportunities have the ability to play an effective role in the development of the financial industry. Therefore, it is necessary to make a correct and effective policy in this area to identify its probable future. This study is exploratory in terms of purpose and practical in terms of orientation. Interviews and questionnaires were used to collect data. 28 drivers were extracted by reviewing the background and interviewing experts. In order to screen the propellants, expert questionnaire and fuzzy Delphi method were used. Then the propellants screened were ranked through the priority assessment questionnaire and the developed COPRAS technique. The two drivers of the development of smart contracts in the financial industry and the tendency of financial institutions towards open innovation had the highest priority and were used to write research scenarios. Based on these two drivers, 4 scenarios of the era of pristine opportunities, the era of conservative managers, the era of dilapidated infrastructure and the ice age were developed. Then, using the MABAC technique, the scenario of the age of dilapidated infrastructures was selected as the possible scenario of the research. Research proposals were proposed based on priority drivers and possible scenarios. Government support, providing sufficient funds in order to create the necessary infrastructure for the development of smart contracts by banks or establishing a cooperative relationship between banks and financial institutions, fair legislation, development of regtechs, creating compatibility between current systems with new technologies were the most important practical proposals of the study.IntroductionThe financial industry includes a set of institutions and organizations that allocate credit and equip resources. The development of economic activities requires investment, and investment also requires the provision of financial resources, which is an important task for the financial industry. Therefore, the development of the financial industry is the driver of economic development. Therefore, the increasing need for new technologies to improve performance and increase efficiency in the financial industry is strongly felt. One of the working models affecting the financial industry is fintech (Qaemi & et al., 2017). Fintech is a field that uses innovative technologies to provide all services of the financial industry with greater speed and transparency and lower cost while maintaining security and quality (Zavolokina et al., 2016). According to reports, global fintech investments have grown from $9 billion in 2010 to $25 billion in 2016. Fintech's market share this year was not even 1%, but it is expected to increase to 35% by 2023 (Koshesh Kordsholi & et al., 2021). In Iran, one of the priorities of the sixth development plan is the issue of financing and expansion of financial instruments, which the financial and banking actors have encouraged to support fintech startups in order to realize innovation and expand their services (Qaemi & et al., 2017). But the fact is that fintech in Iran is behind the rest of the world. In iran, due to several problems, including legal challenges, technology problems, financing, etc., fintech businesses are not growing and there are many entrepreneurial opportunities in this field that have not been addressed. Thus, the current research seeks to answer the following questions:What are the key drivers affecting the future of fintech entrepreneurial opportunities in Iran?What is the degree of priority of the key drivers affecting the future of fintech entrepreneurial opportunities in Iran?What are the plausible future scenarios of fintech entrepreneurial opportunities in Iran?What is the possible future scenario of fintech entrepreneurial opportunities in Iran?Literature ReviewFintech can be considered as any innovative idea that improves financial services processes by providing technological solutions according to different business situations) Suryono & et al., 2020). Fintech startups are looking for new approaches to business models, improving customer experience and new approaches that lead to service changes and are trying to enter financial systems and challenge traditional financial institutions (Gomber et al., 2018). If the context and the possibility of growth and application of entrepreneurial opportunities hidden in the field of fintech, which are in their maturity stage, are provided; It will follow the increasing economic progress of the countries.Uncertainty about the future of organizations prompts managers to look for new tools and methods to determine future situations and create the future. Future research is a systematic way to look at the long-term future in any field and draw it, the main purpose of which is to know the new structures, mechanisms, opportunities and processes and to determine the sectors that have more efficiency. Understanding and applying futurist theories and methods enables individuals and groups to more usefully anticipate the future and shape it to a greater extent based on their preferences (Dator et al., 2019).MethodologyIn this research, four quantitative methods, fuzzy Delphi, entropy, developed COPRAS and MABAC technique were used. Also, to develop believable research scenarios, the qualitative method of the consultation workshop was used. The theoretical community of this research is 10 members of the academic staff of the university, fintech experts, managers of fintech businesses, experts of fintech associations and senior experts of the central bank in the regulatory field. The sampling method of the present study is judgmental and based on the expertise of individuals in the field of fintech. The steps of the current research are: 1) background review and interviews with experts to identify drivers affecting the future of fintech entrepreneurial opportunities in Iran; 2) Screening research drivers with the fuzzy Delphi technique; 3) prioritizing the final drives with the developed COPRAS method; 4) Compilation of plausible future scenarios of Iran's fintech entrepreneurship opportunities using a consultation workshop (participation of 7 experts); 5) Selecting a possible research scenario using the MABAC technique.ResultsAt first, 28 drivers were extracted through reviewing financial technology-oriented backgrounds and interviewing experts. Then, with the application of expert questionnaire and fuzzy Delphi technique, 15 drivers were removed from the calculations and 13 drivers were selected to extract the effect model of drivers. Based on the output of COPRAS technique, the drivers for the development of smart contracts in the financial industry are the tendency of financial institutions towards open innovation, the variety of financing methods and the attitude of the regulator towards fintechs, respectively, they have the most importance in terms of influencing the future of fintech entrepreneurial opportunities. The two drivers of smart contract development and the trend of financial institutions towards open innovation were used to map research scenarios. Considering that for each driver, two opposite situations can be set, four scenarios were developed, which are: the era of pristine opportunities, the era of conservative managers, the era of dilapidated infrastructure, and the ice age. In the following, MABAC technique was used to select the possible research scenario. The ranking of the research scenarios in terms of 3 selected indicators is such that the scenario of the age of dilapidated infrastructures is the most likely research scenario. The ice age scenarios, the conservative managers era scenario, and the pristine opportunities era scenario were ranked next.ConclusionThe trend of financial institutions towards open innovation in the scenario of the age of worn out infrastructure shows that the development of digital technologies has gradually created interest in the managers of this industry and has improved their attitude towards themselves. But the lack of development of smart contracts in the financial industry in this future has several reasons. Among the reasons for this lack of development, we can mention the lack of necessary infrastructure for the development of information technology, which is mainly due to the lack of support from the government and the relevant ministry and their lack of attention to the importance of learning information technology. In addition, banks should also provide the necessary funds in order to create the necessary infrastructure for the development of smart contracts, and if there is a heavy cost, establish a cooperative relationship between the banks and financial institutions of the country along with government support to reduce the cost and implement it in a tangible way. Another important discussion in this field is that fair regulation is necessary for the spread of smart contracts. Strengthening regtechs through science and technology parks and growth centers can also help. Another important reason for not developing smart contracts is the incompatibility of current systems with new technologies, which prompts managers of financial institutions to change the system and make them compatible. Lack of sufficient training for financial industry activists can also be another factor for this lack of development.Keywords: Future Study, Driver, Scenario Planning, Entrepreneurial Opportunities, Fintech
Soraya Bakhtiari bastaki,; Peyman Ghafari ashtiani,; Ali Hamidizadeh,; Rasoul Sanavi Fard,
Abstract
he present research aimed at developing a model for perceived social media advertising deception. To attain the aim, the interpretive structural modeling approach was employed. The research sample included all of the lecturers and experts of social media marketing and advertisement field selected by ...
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he present research aimed at developing a model for perceived social media advertising deception. To attain the aim, the interpretive structural modeling approach was employed. The research sample included all of the lecturers and experts of social media marketing and advertisement field selected by the purposeful sampling method. Eventually, eight lecturers and experts of social media marketing and advertisement answered the considered questions. The selected experts had at least ten years of experience in studying, teaching, or working in the field of social media. The sampling continued up to the theoretical saturation point. To determine the reliability of the measurement instrument, the ICC value was confirmed in terms of its consistency and absolute agreement. The research results indicated that in relation to the research subject and the proposed model for perceived social media advertising deception, social media advertising attributes had the strongest effect, while the perceived usefulness, customer knowledge, perceived trust, customer attitude, customer attributes, and media attributes were mostly affected by and their own effects were trivial. In addition, the results revealed that the primary factor in the research i.e. social media advertising attributes, was among the influential or driving variables due to its high directional power and low dependency. Other factors were described with high directional power and high dependency. The variables were non-static and so classified as the hybrid variables.
Mona Jomipour; Seyed Mohammad Bagher Jafari; Mahnaz Hosseinzadeh; Aghdas Soleimani
Abstract
Strategic alignment of marketing and information technology is one of the managers’ priorities in recent years, that should be considered in order to achieve optimal performance in the field of digital marketing. Applying information technology in a proper way, in line with marketing strategies, ...
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Strategic alignment of marketing and information technology is one of the managers’ priorities in recent years, that should be considered in order to achieve optimal performance in the field of digital marketing. Applying information technology in a proper way, in line with marketing strategies, to meet marketing needs is the main emphasis of strategic alignment between marketing and information technology. Despite the growth of investment in marketing technologies and the necessity of strategic alignment in this field, the literature on identifying the effective factors for strategic alignment of marketing and information technology has not been addressed yet. Therefore, the main purpose of the current research is to identify the effectiveness in strategic alignment of marketing and information technology in organizations hoping to improve the efficiency of IT investments in marketing. The research is conducted in two stages applying a mixed approach. In the first stage, qualitative approach and semi-structured interview tools are used to identify and extract the factors from the experts’ perspectives. In the second step, quantitative approach and BWM technique are used to weight and prioritize the identified factors. The statistical population of this study includes all academic experts in the field of research and senior executives with experience in marketing and information technology in organizations with digital marketing who were selected by purposeful sampling. The findings of the study include 7 factors of support and support of senior marketing manager, IT capabilities, partnerships / communications, governance, marketing competencies, skills / manpower and competitive factors, of which partnership / communication has the most weight and importance and competitive / environmental factors with the least weight were the least important.
Mitra Daneshparvar; Zohreh Dehdashti Shahrokh
Abstract
Today, we see the great impact of social networks on the purchase of many products in various industries. One of the industries that has been affected by social networks in recent years is the clothing industry. Therefore, this study seeks to identify the factors affecting the purchase of clothing in ...
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Today, we see the great impact of social networks on the purchase of many products in various industries. One of the industries that has been affected by social networks in recent years is the clothing industry. Therefore, this study seeks to identify the factors affecting the purchase of clothing in social networks and to develop a model of consumer shopping behavior in social networks for the clothing industry. In this research, a combined method has been used. In the qualitative section, through interviews with clothing sellers active in social networks, texts were prepared and coded, 46 concepts were identified, which were classified into four main categories and 16 sub-categories and presented in the form of a prototype. In the quantitative part, based on the initial model, a questionnaire was developed and distributed to 385 clothing buyers in networks, and the final model of consumer purchasing behavior for purchasing clothing through social networks was presented. The results showed that individual factors, company-related factors and social and cultural factors directly and also with the mediating role of trust, had a significant effect on the consumer's decision to buy clothing, followed by a significant effect on loyalty, repurchase and Advise others to buy through social media.
Mohammad Baradaran; Abbas Tolouei Ashlaghi; Mohammad Al i Afshar Kazemi; Mohammad Reza Motadel
Abstract
Smart transport is an indispensable necessity in today's smart cities. In this thesis, six scenarios were implemented based on intelligent transportation. In the first scenario, the speed control process was implemented. In the second and third scenarios, the process of traffic planning was even and ...
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Smart transport is an indispensable necessity in today's smart cities. In this thesis, six scenarios were implemented based on intelligent transportation. In the first scenario, the speed control process was implemented. In the second and third scenarios, the process of traffic planning was even and odd. In the fourth scenario, the vehicle relief system was implemented. In the fifth scenario, an emergency relief system was implemented. In the sixth scenario, the message was executed at the time of the car theft. The message delivery process has been fully implemented in the smart city area so that all vehicles in the smart transport process receive the message. An access point is called an access point whose task is to send a message. Given that the number of messages sent to the network decreases, there are two advantages to the low cost of sending messages as well as full coverage. So instead of sending the message directly to each car individually, the messages are sent to the roadside equipment and then sent to the vehicles passing the road through the roadside equipment. This roadside equipment is called fixed nodes. Certainly, sending messages to all these fixed nodes is not optimal and has two major drawbacks. The first disadvantage is that a car may cross several fixed target nodes and receive all of these fixed target nodes of the message. Also the second problem is that the number of messages received creates terrible statistics and imposes a computational overhead. So the solution to this problem is to select some of these fixed nodes as the target constant node. Only send the message to fixed target nodes and send it to the cars passing by. The first challenge of this thesis is the selection of these target constant nodes, which is implemented using the fuzzy ranked idealized programming algorithm. After solving the first challenge due to the communication board problem in the car network, the challenge of routing the message from the access point to the fixed node is raised. The solution to this challenge is computed using the Dijkstra algorithm and compared with the eTGMD algorithm in terms of message delivery rate, delivery delay, number of packets used, and number of fixed target nodes. The results of the proposed algorithm show a good and optimal improvement over the eTGMD algorithm.
Amin Habibirad; Ali Panahi
Abstract
Nowadays, Bitcoin is one of the most important cryptocurrencies that has the largest volume of exchanges in the cryptocurrency market and between businesses. The feature of the possibility of online payments between individuals and businesses directly and without referring to the financial institution ...
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Nowadays, Bitcoin is one of the most important cryptocurrencies that has the largest volume of exchanges in the cryptocurrency market and between businesses. The feature of the possibility of online payments between individuals and businesses directly and without referring to the financial institution has made the price of these cryptocurrencies important for businesses and traders and the basis for decision making. Therefore, the issue of price predictability is an important issue that can be affected by search volume. The purpose of this research is studying and investigating the relationship between the volume of Internet searches and its effect on the price of these cryptocurrencies. In addition, another goal of this article is to introduce Google Trends (GT) as a tool for accessing big data for business researches. The required data was extracted from Google Trends in the period 2016 to 2021. The volume of data was 5742 and the whole statistical population was used. The research method is descriptive-exploratory with the aim of explaining the relationship between "Google search volume index" and "bitcoin price". Data were analyzed using Spearman correlation test. Findings indicate a strong and very strong relationship between the studied indicators, which is explained.
Management approaches in the field of smart
fahime mahavarpour; feiz davood; Morteza Maleki MinBashRazgah
Abstract
Augmented Reality (AR) is an emerging topic for managers across different disciplines While augmented reality technology literature is growing, there is no comprehensive analysis of augmented reality technology in marketing transformation The research aims to bridge the knowledge gap by providing a multifaceted ...
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Augmented Reality (AR) is an emerging topic for managers across different disciplines While augmented reality technology literature is growing, there is no comprehensive analysis of augmented reality technology in marketing transformation The research aims to bridge the knowledge gap by providing a multifaceted bibliographic overview of augmented reality technology literature in marketing and reveal its trends, areas of focus and intellectual foundations. The study is based on 496 articles published on Web of Science between 1996 and 2023. According to the findings, the concept mainly revolves around seven main areas: human-machine interaction in future of digital marketing and Metaverse, advertising and customer response in online purchases, marketing challenges in industry 4 and new technologies, the effect of virtual technology on customer loyalty in retail, adoption of behavioral technology of Tourism customers, augmented reality technology marketing in the decision-making process of buyers and brands, and finally the richness of social media in e-commerce in Covid 19. While priorities and research topics have evolved over time, key concepts such as buying experience, shopper behavior, buying decision making, technology adoption have been repeated. The three influential schools of augmented reality technology in marketing are associated with integrated theory, planned behavior theory (TPB) and cognitive evaluation theory that have shaped the intellectual foundations of the discipline but we believe that a greater diversity of fields is needed to examine and describe augmented reality technology in marketing transformation.
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.
Data science, intelligence and future analysis
Abbas Bagherian Kasgari; Iman Raeesi Vanani; Maghsoud Amiri; Saeid Homayoun
Abstract
Most traditional fraud detection systems primarily focus on financial criteria to identify financial fraud, often overlooking the potential for fraudulent companies to engage in various types of non-financial misconduct. Recent studies have predominantly highlighted the significance of financial data ...
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Most traditional fraud detection systems primarily focus on financial criteria to identify financial fraud, often overlooking the potential for fraudulent companies to engage in various types of non-financial misconduct. Recent studies have predominantly highlighted the significance of financial data as the sole indicator of fraud, neglecting the exploration of non-financial or Environmental, Social, and Governance (ESG) metrics as supplementary predictors. This research aims to enhance fraud prediction by integrating financial and ESG data through sophisticated machine learning and deep learning models. It examines the effectiveness of supervised machine learning and deep learning algorithms in detecting financial fraud over a 10-year period ending in 1401. This study innovatively demonstrates that a hybrid model, which combines financial and non-financial criteria, yields superior predictive accuracy for financial fraud than models based solely on financial data. The results of this study, addressing the first research question, indicate that among various machine learning and deep learning algorithms, the classification or bagging algorithm demonstrated superior efficiency. Furthermore, in response to the second research question, it was found that the dataset encompassing all features—integrating both financial and non-financial data—outperformed those datasets limited to either financial or non-financial data alone. The research results indicated that the bagging machine learning algorithms act the best with combined feature set including financial and ESG metrics combined. The adoption of the proposed model significantly improves the accuracy and effectiveness of fraud detection systems.
Management approaches in the field of smart
Peyman Ghafari Ashtiani; Maryam Ghiasaadi Farahani
Abstract
The current research has identified the factors affecting the management and organization of businesses based on e-commerce through a meta-combination approach. The research is applied in terms of purpose and qualitative in nature with an exploratory approach. The community of this research is the articles ...
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The current research has identified the factors affecting the management and organization of businesses based on e-commerce through a meta-combination approach. The research is applied in terms of purpose and qualitative in nature with an exploratory approach. The community of this research is the articles in the field of managing and organizing e-commerce jointly or separately. The method of collecting information is the document-library method with the efficiency of the meta-combination method for extracting factors. The sampling method is also selected based on the entry and exit criteria of the prism method. In order to answer the research questions, 96 articles were examined and analyzed, which led to the extraction of 15 categories and 95 meaningful codes. The results showed that business management based on e-commerce includes the components of online sales, digital marketing, suppliers, leadership, product or service, human resources, organizational culture, customer, market, organizational structure, and organizing business based on E-commerce includes the components of technical dimension, organizational dimension, environmental dimension, economic dimension and financial dimension.
