Mehdi Dadkhaha, Mohammad Lagziana, Fariborz Rahim-niaa, Khalil Kimiafar
Finding the right experts for data gathering through interview serves as a key for particular research works. However, most expert finding methods in the literature require great deals of technical knowledge, making them somewhat impracticable for business researchers without deep technical knowledge. Accordingly, there is a need for an expert finding solution for researchers without a deep technical background. As business researchers may have knowledge about business intelligence and its tools, the use of business intelligence tools can be used to solve such issue. The present paper discusses the process of using business intelligence tools to find potential experts for example topics. Subsequently, based on a literature review, criteria are presented for distinguishing different experts. Finally, the analytic hierarchy process is discussed for assigning weights to both selection criteria and potential experts. The audience of this paper is researchers who are familiar with business intelligence tools or would like to learn how to work with them
{"title":"The potential of business intelligence tools for expert finding","authors":"Mehdi Dadkhaha, Mohammad Lagziana, Fariborz Rahim-niaa, Khalil Kimiafar","doi":"10.37380/jisib.v9i2.471","DOIUrl":"https://doi.org/10.37380/jisib.v9i2.471","url":null,"abstract":"Finding the right experts for data gathering through interview serves as a key for particular research works. However, most expert finding methods in the literature require great deals of technical knowledge, making them somewhat impracticable for business researchers without deep technical knowledge. Accordingly, there is a need for an expert finding solution for researchers without a deep technical background. As business researchers may have knowledge about business intelligence and its tools, the use of business intelligence tools can be used to solve such issue. The present paper discusses the process of using business intelligence tools to find potential experts for example topics. Subsequently, based on a literature review, criteria are presented for distinguishing different experts. Finally, the analytic hierarchy process is discussed for assigning weights to both selection criteria and potential experts. The audience of this paper is researchers who are familiar with business intelligence tools or would like to learn how to work with them","PeriodicalId":43580,"journal":{"name":"Journal of Intelligence Studies in Business","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47139291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Soumaya Lamrharia, Hamid Elghazi, Abdellatif El Faker
Today, understanding customer satisfaction is becoming a difficult and complex task for companies due to the explosive growth of the voice of the customer in online reviews. This has pushed companies to rethink their business strategies and resort to business intelligence techniques in order to help them in analyzing customer requirements and market trends. This paper proposes a decision support framework for dynamically transforming the voice of the customer data into actionable insight. The framework measures the customer satisfaction by extracting key products’ aspects along with customers’ sentiments from online reviews using a text mining technique: the latent Dirichlet allocation approach. We apply the Fuzzy-Kano model to classify the real customer requirements, then, map them dynamically to the SWOT matrix. The proposed approach is extensively tested on an empirical dataset based on several performance metrics including accuracy, precision, recall, and F-score. The reported results showed that latent Dirichlet allocation approach has correctly extracted aspects with 97.4% accuracy and 92.4 % precision.
{"title":"Business intelligence using the fuzzy-Kano model","authors":"Soumaya Lamrharia, Hamid Elghazi, Abdellatif El Faker","doi":"10.37380/jisib.v9i2.468","DOIUrl":"https://doi.org/10.37380/jisib.v9i2.468","url":null,"abstract":"Today, understanding customer satisfaction is becoming a difficult and complex task for companies due to the explosive growth of the voice of the customer in online reviews. This has pushed companies to rethink their business strategies and resort to business intelligence techniques in order to help them in analyzing customer requirements and market trends. This paper proposes a decision support framework for dynamically transforming the voice of the customer data into actionable insight. The framework measures the customer satisfaction by extracting key products’ aspects along with customers’ sentiments from online reviews using a text mining technique: the latent Dirichlet allocation approach. We apply the Fuzzy-Kano model to classify the real customer requirements, then, map them dynamically to the SWOT matrix. The proposed approach is extensively tested on an empirical dataset based on several performance metrics including accuracy, precision, recall, and F-score. The reported results showed that latent Dirichlet allocation approach has correctly extracted aspects with 97.4% accuracy and 92.4 % precision.","PeriodicalId":43580,"journal":{"name":"Journal of Intelligence Studies in Business","volume":"139 5-6","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41297872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Companies market their services and products on social media platforms with today's easy access to the internet. As result, they receive feedback and reviews from their users directly on their social media sites. Reading every text is time-consuming and resourcedemanding. With access to technology-based solutions, analyzing the sentiment of all these texts gives companies an overview of how positive or negative users are on specific subjects will minimize losses. In this paper, we propose a deep learning approach to perform sentiment analysis on reviews using a convolutional neural network model, because that they have proven remarkable results for text classification. We validate our convolutional neural network model using large-scale data sets: IMDB movie reviews and Reuters data sets with a final accuracy score of ~86% for both data sets.
{"title":"A new corpus-based convolutional neural network for big data text analytics","authors":"Wedjdane Nahilia, Kahled Rezega, Okba Kazara","doi":"10.37380/jisib.v9i2.469","DOIUrl":"https://doi.org/10.37380/jisib.v9i2.469","url":null,"abstract":"Companies market their services and products on social media platforms with today's easy access to the internet. As result, they receive feedback and reviews from their users directly on their social media sites. Reading every text is time-consuming and resourcedemanding. With access to technology-based solutions, analyzing the sentiment of all these texts gives companies an overview of how positive or negative users are on specific subjects will minimize losses. In this paper, we propose a deep learning approach to perform sentiment analysis on reviews using a convolutional neural network model, because that they have proven remarkable results for text classification. We validate our convolutional neural network model using large-scale data sets: IMDB movie reviews and Reuters data sets with a final accuracy score of ~86% for both data sets.","PeriodicalId":43580,"journal":{"name":"Journal of Intelligence Studies in Business","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43684820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The problem we want to solve is to find out what is new in the collective intelligence literature and how it is to be understood alongside other social science disciplines. The reason it is important is that collective intelligence and problems of collaboration seem familiar in the social sciences but do not necessarily fit into any of the established disciplines. Also, collective intelligence is often associated with the notion of wisdom of crowds, which demands scrutiny. We found that the collective intelligence field is valuable, truly interdisciplinary, and part of a paradigm shift in the social sciences. However, the content is not new, as suggested by the comparison with social intelligence, which is often uncritical and lacking in the data it shows and that the notion of the wisdom of crowds is misleading (RQ1). The study of social systems is still highly relevant for social scientists and scholars of collective intelligence as an alternative methodology to more traditional social science paradigms as found, for example, in the study of business or management (RQ2).
{"title":"Making sense of the collective intelligence field: A review","authors":"Klaus Solberg Søilen","doi":"10.37380/jisib.v9i2.465","DOIUrl":"https://doi.org/10.37380/jisib.v9i2.465","url":null,"abstract":"The problem we want to solve is to find out what is new in the collective intelligence literature and how it is to be understood alongside other social science disciplines. The reason it is important is that collective intelligence and problems of collaboration seem familiar in the social sciences but do not necessarily fit into any of the established disciplines. Also, collective intelligence is often associated with the notion of wisdom of crowds, which demands scrutiny. We found that the collective intelligence field is valuable, truly interdisciplinary, and part of a paradigm shift in the social sciences. However, the content is not new, as suggested by the comparison with social intelligence, which is often uncritical and lacking in the data it shows and that the notion of the wisdom of crowds is misleading (RQ1). The study of social systems is still highly relevant for social scientists and scholars of collective intelligence as an alternative methodology to more traditional social science paradigms as found, for example, in the study of business or management (RQ2).","PeriodicalId":43580,"journal":{"name":"Journal of Intelligence Studies in Business","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44836861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Open data are information entities that are of significant importance for many institutions, businesses and even citizens as the part of the digital transformation within many fields in our society. The aim of this paper is to provide a competitive environment analysis method using open source intelligence within the pharmaceutical sector and to design the optimal data structure for this purpose. Firstly, we have described the state-of-the-art of open human medicine data within the European Union with a focus on antidepressants and we have chosen the Czech Republic as the primary research territory for demonstrating competitive intelligence analysis. Secondly, we have identified the competitive intelligence and open source intelligence relationship with a new possible contextual analysis method using open human medicine data and Google Search data. Finally, this paper shows the potential of open deep web data within competitive intelligence activities, together with surface web data entities as a lowcost approach with high intelligence value focused on the pharmaceutical market.
{"title":"Using open data and Google search data for competitive intelligence analysis","authors":"Jan Černýa, Martin Potančoka, Zdeněk Molnára","doi":"10.37380/jisib.v9i2.470","DOIUrl":"https://doi.org/10.37380/jisib.v9i2.470","url":null,"abstract":"Open data are information entities that are of significant importance for many institutions, businesses and even citizens as the part of the digital transformation within many fields in our society. The aim of this paper is to provide a competitive environment analysis method using open source intelligence within the pharmaceutical sector and to design the optimal data structure for this purpose. Firstly, we have described the state-of-the-art of open human medicine data within the European Union with a focus on antidepressants and we have chosen the Czech Republic as the primary research territory for demonstrating competitive intelligence analysis. Secondly, we have identified the competitive intelligence and open source intelligence relationship with a new possible contextual analysis method using open human medicine data and Google Search data. Finally, this paper shows the potential of open deep web data within competitive intelligence activities, together with surface web data entities as a lowcost approach with high intelligence value focused on the pharmaceutical market.","PeriodicalId":43580,"journal":{"name":"Journal of Intelligence Studies in Business","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41845533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aim of any social science to develop theories and/or models to better understand the business reality. We are happy to see that a majority of contributions this time do exactly that. The first article by Nuortimo is entitled “Exploring new ways to utilise market intelligence (MI) function in corporate decisions: Case opinion mining of nuclear power”. It is an in-depth case study about the monitoring of technology sentiment based on business environment scanning. Results show how media sentiment towards nuclear power has been mostly negative, particularly in social media. However, results from similar analyses of the image for the companies currently deploying these technology are less negative, suggesting the importance of companies’ communication and branding activities. The paper shows how technology’s media sentiment can influence a company’s brand image and marketing communications. It concludes that there is a need for better co-operation between different corporate functions, namely technology management, MI, and marketing and strategic planning. The second paper, by Bleoju and Capatina, entitled “Enhancing competitive response to market challenges with a Strategic Intelligence maturity model” shows a way to gain robustness in confronting unexpected events in real markets by adopting a wider unstructured learning perspective with the help of maturity assessment tools. This helps to pool strategic intelligence skills. The theoretical contribution is called the Strategic Intelligence Capability Maturity Model. The article by Solberg Söilen is entitled “How managers stay informed about the surrounding world”. It’s a survey of managers and knowledge workers to find out exactly what sources of information they gather to help their organization stay competitive. Conclusions from the data are drawn and a model presented that brings together previous theory with new empirical findings. The first issue of 2019 was delayed primarily due to the journal’s involvement as co-sponsor of the ICI Conference in Luxembourg in May. As always, we would above all like to thank the authors for their contributions to this issue of JISIB. Thanks to Dr. Allison Perrigo for reviewing English grammar and helping with layout design for all articles and to the Swedish Research Council for continuous financial support. We hope to see as many as possible at the ICI Conference in Bad Nauheim in May, 2020. On behalf of the Editorial Board, Sincerely Yours, A list of authors relevant contributions are included.
{"title":"Developing new models for intelligence studies","authors":"Klaus Solberg Søilen","doi":"10.37380/JISIB.V9I1.400","DOIUrl":"https://doi.org/10.37380/JISIB.V9I1.400","url":null,"abstract":"The aim of any social science to develop theories and/or models to better understand the business reality. We are happy to see that a majority of contributions this time do exactly that. The first article by Nuortimo is entitled “Exploring new ways to utilise market intelligence (MI) function in corporate decisions: Case opinion mining of nuclear power”. It is an in-depth case study about the monitoring of technology sentiment based on business environment scanning. Results show how media sentiment towards nuclear power has been mostly negative, particularly in social media. However, results from similar analyses of the image for the companies currently deploying these technology are less negative, suggesting the importance of companies’ communication and branding activities. The paper shows how technology’s media sentiment can influence a company’s brand image and marketing communications. It concludes that there is a need for better co-operation between different corporate functions, namely technology management, MI, and marketing and strategic planning. The second paper, by Bleoju and Capatina, entitled “Enhancing competitive response to market challenges with a Strategic Intelligence maturity model” shows a way to gain robustness in confronting unexpected events in real markets by adopting a wider unstructured learning perspective with the help of maturity assessment tools. This helps to pool strategic intelligence skills. The theoretical contribution is called the Strategic Intelligence Capability Maturity Model. The article by Solberg Söilen is entitled “How managers stay informed about the surrounding world”. It’s a survey of managers and knowledge workers to find out exactly what sources of information they gather to help their organization stay competitive. Conclusions from the data are drawn and a model presented that brings together previous theory with new empirical findings. The first issue of 2019 was delayed primarily due to the journal’s involvement as co-sponsor of the ICI Conference in Luxembourg in May. As always, we would above all like to thank the authors for their contributions to this issue of JISIB. Thanks to Dr. Allison Perrigo for reviewing English grammar and helping with layout design for all articles and to the Swedish Research Council for continuous financial support. We hope to see as many as possible at the ICI Conference in Bad Nauheim in May, 2020. On behalf of the Editorial Board, Sincerely Yours, A list of authors relevant contributions are included. ","PeriodicalId":43580,"journal":{"name":"Journal of Intelligence Studies in Business","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46956616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The challenge in today’s corporations is that even though the technology portfolio of a company plays a crucial role in delivering revenue—falling as a topic mainly under the area of technology management—technology may have a negative image due to observed risks or failing the sustainability criteria. It may influence the company’s image and brand image, possibly also influencing decisions at corporate level. The monitoring of technology sentiments is therefore emphasized, benefiting from the advanced methods for business environment scanning, namely market and competitor intelligence functions. This paper utilizes a new big data based method, mostly utilized in market(MI)/competitor intelligence(CI) functions of the company, opinion mining, to analyse the global media sentiment of nuclear power and projects deploying the technology. With this approach, it is easier to understand the linkage to corporate images of companies deploying the technology and also related corporate decisions, mainly done in the areas of technology market deployment, marketing and strategic planning. The results indicate how the media sentiment towards nuclear power has been mostly negative globally, particularly in social media. In addition, results from similar analyses from a single company’s images for the companies currently deploying the technology are seemingly less negative, indicating the influence of company’s communication and branding activities. This paper has implications showing that a technology’s media sentiment can influence a company’s brand image, marketing communications and the need for actions when technology is deployed. In conclusion, there seems to be a need for better co-operation between different corporate functions, namely technology management, MI, marketing and strategic planning, in order to indicate technology image impacts and also counteract firestorms from social media.
{"title":"Exploring new ways to utilise the market intelligence (MI) function in corporate decisions: Case opinion mining of nuclear power","authors":"K. Nuortimo, Janne Harkonen","doi":"10.37380/JISIB.V9I1.401","DOIUrl":"https://doi.org/10.37380/JISIB.V9I1.401","url":null,"abstract":"The challenge in today’s corporations is that even though the technology portfolio of a company plays a crucial role in delivering revenue—falling as a topic mainly under the area of technology management—technology may have a negative image due to observed risks or failing the sustainability criteria. It may influence the company’s image and brand image, possibly also influencing decisions at corporate level. The monitoring of technology sentiments is therefore emphasized, benefiting from the advanced methods for business environment scanning, namely market and competitor intelligence functions. This paper utilizes a new big data based method, mostly utilized in market(MI)/competitor intelligence(CI) functions of the company, opinion mining, to analyse the global media sentiment of nuclear power and projects deploying the technology. With this approach, it is easier to understand the linkage to corporate images of companies deploying the technology and also related corporate decisions, mainly done in the areas of technology market deployment, marketing and strategic planning. The results indicate how the media sentiment towards nuclear power has been mostly negative globally, particularly in social media. In addition, results from similar analyses from a single company’s images for the companies currently deploying the technology are seemingly less negative, indicating the influence of company’s communication and branding activities. This paper has implications showing that a technology’s media sentiment can influence a company’s brand image, marketing communications and the need for actions when technology is deployed. In conclusion, there seems to be a need for better co-operation between different corporate functions, namely technology management, MI, marketing and strategic planning, in order to indicate technology image impacts and also counteract firestorms from social media.","PeriodicalId":43580,"journal":{"name":"Journal of Intelligence Studies in Business","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44946146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we look at how managers and knowledge workers stay informed about the events in the outside world that affect their organizations. Data was collected using a survey of 308 subjects from around the world. A model for how managers stay informed is presented. We introduce the idea of the proprietary cloud. The findings have implications for managers who want to compare their own sources of information and improve routines for information gathering.
{"title":"How managers stay informed about the surrounding world","authors":"Klaus Solberg Søilen","doi":"10.37380/JISIB.V9I1.403","DOIUrl":"https://doi.org/10.37380/JISIB.V9I1.403","url":null,"abstract":"In this paper we look at how managers and knowledge workers stay informed about the events in the outside world that affect their organizations. Data was collected using a survey of 308 subjects from around the world. A model for how managers stay informed is presented. We introduce the idea of the proprietary cloud. The findings have implications for managers who want to compare their own sources of information and improve routines for information gathering.","PeriodicalId":43580,"journal":{"name":"Journal of Intelligence Studies in Business","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41412967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tracking meaningful insights about companies’ exposures to high risk of failure in competitive markets, intelligence studies in business should listen to practitioners’ signals and act in providing decision making support to systematic scanning for valuable information. In order to gain robustness in confronting unexpected events in real markets, companies should adopt an unstructured learning perspective with maturity assessment tools, while purposely pooling strategic intelligence (SI) skills. By bridging organizational maturity modeling with a future orientation stream of literature and intelligence studies in business, this conceptual research aims to highlight a genuine Strategic Intelligence Capability Maturity Model (SI CMM), capable of purposely addressing the challenge of aligning detective and anticipatory organizational capabilities. The conceptual model highlights the degree of preparedness of four SI profiles behaviors (intelligence provider, vigilant learner, opportunity captor and opportunity defender – previously developed by the authors) against seven levels of maturity. The SI CMM framework outlines both conditioned scanning capabilities (the first five SI readiness levels) and enablers to anticipate future market trends (the last two SI readiness levels). The novel approach of the strategic intelligence readiness framework supplies companies with a valuable organizational learning tool to close the skills gap through an opportunity provider profile. The main features lie in coordination and sharing SI common knowledge to enhance preparedness in forward-looking competitive pressures. The conceptual framework invites academia and the community of intelligence experts in business to evaluate the relevance of the new conceptualization, clarity of constructs and complementary nature of correlation and causation with the proposed SI CMM model
{"title":"Enhancing competitive response to market challenges with a strategic intelligence maturity model","authors":"Gianita Bleoju, A. Căpățînă","doi":"10.37380/JISIB.V9I1.402","DOIUrl":"https://doi.org/10.37380/JISIB.V9I1.402","url":null,"abstract":"Tracking meaningful insights about companies’ exposures to high risk of failure in competitive markets, intelligence studies in business should listen to practitioners’ signals and act in providing decision making support to systematic scanning for valuable information. In order to gain robustness in confronting unexpected events in real markets, companies should adopt an unstructured learning perspective with maturity assessment tools, while purposely pooling strategic intelligence (SI) skills. By bridging organizational maturity modeling with a future orientation stream of literature and intelligence studies in business, this conceptual research aims to highlight a genuine Strategic Intelligence Capability Maturity Model (SI CMM), capable of purposely addressing the challenge of aligning detective and anticipatory organizational capabilities. The conceptual model highlights the degree of preparedness of four SI profiles behaviors (intelligence provider, vigilant learner, opportunity captor and opportunity defender – previously developed by the authors) against seven levels of maturity. The SI CMM framework outlines both conditioned scanning capabilities (the first five SI readiness levels) and enablers to anticipate future market trends (the last two SI readiness levels). The novel approach of the strategic intelligence readiness framework supplies companies with a valuable organizational learning tool to close the skills gap through an opportunity provider profile. The main features lie in coordination and sharing SI common knowledge to enhance preparedness in forward-looking competitive pressures. The conceptual framework invites academia and the community of intelligence experts in business to evaluate the relevance of the new conceptualization, clarity of constructs and complementary nature of correlation and causation with the proposed SI CMM model","PeriodicalId":43580,"journal":{"name":"Journal of Intelligence Studies in Business","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47963682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Selma Letícia Capinzaiki Ottonicar, Marta Lígia Pomim Valentim, Elaine Mosconi
This paper investigated how information literacy and competitive intelligence areconnected in business management and information science fields. It demonstrates thecontribution of information literacy in the phases of the competitive intelligence process. Thispaper is relevant, since the model supports creativity and collaborative innovation in smallbusinesses in the context of Industry 4.0. Furthermore, it contributed to connect the informationscience and business management fields, so it is multidisciplinary. It also proposes a theoreticalmodel of information literacy and competitive intelligence in the context of Industry 4.0, whichcan be used for applied research. The methodology was developed based on a systematicliterature review (SLR) of information literature and competitive intelligence. These conceptscontribute to the development of a framework and a conceptual model in which the three themesare interconnected and demonstrate that information literacy can efficiently contribute to thecompetitive intelligence process, especially in the context of the Fourth Industrial Revolution.
{"title":"A competitive intelligence model based on information literacy: organizational competitiveness in the context of the 4th Industrial Revolution","authors":"Selma Letícia Capinzaiki Ottonicar, Marta Lígia Pomim Valentim, Elaine Mosconi","doi":"10.37380/JISIB.V8I3.366","DOIUrl":"https://doi.org/10.37380/JISIB.V8I3.366","url":null,"abstract":"This paper investigated how information literacy and competitive intelligence areconnected in business management and information science fields. It demonstrates thecontribution of information literacy in the phases of the competitive intelligence process. Thispaper is relevant, since the model supports creativity and collaborative innovation in smallbusinesses in the context of Industry 4.0. Furthermore, it contributed to connect the informationscience and business management fields, so it is multidisciplinary. It also proposes a theoreticalmodel of information literacy and competitive intelligence in the context of Industry 4.0, whichcan be used for applied research. The methodology was developed based on a systematicliterature review (SLR) of information literature and competitive intelligence. These conceptscontribute to the development of a framework and a conceptual model in which the three themesare interconnected and demonstrate that information literacy can efficiently contribute to thecompetitive intelligence process, especially in the context of the Fourth Industrial Revolution.","PeriodicalId":43580,"journal":{"name":"Journal of Intelligence Studies in Business","volume":" ","pages":""},"PeriodicalIF":0.9,"publicationDate":"2019-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43231157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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