Investigating the business intelligence capabilities’ and network learning effect on the data mining for start-up's function
Introduction
Organizations, companies, and their environments are changing rapidly, fewer companies today behave traditionally, and most businesses have come to accept that continuous profitability alone does not mean survival and should be pursued (Goodyear et al., 2006). Competition and related tools, and in order to stay competitive in the market, they must learn new rules. Taking advantage of new opportunities and implementing effective strategies can bring a competitive market advantage and long-term sustainability (Kannan and Munday, 2018). Organizations and companies, when actively monitoring and monitoring the market, usually face inevitable changes, so they must always be properly prepared to respond to customer demand (Steeples and Jones, 2012).
Business intelligence is a set of methods, processes, and technologies used to turn raw data into meaningful results. There is always a deep gap between the information required by managers and the large amount of data that is collected during the daily operations of organizations in its various departments (Anders, 2018). Maybe some information is provided outside of operating systems and even outside the organization and through competitors' information. Business intelligence uses large amounts of information to identify and develop new opportunities (Caseiro and Coelho, 2019). The maximum benefit of business intelligence is that it provides direct access to data to decision makers at all levels of the organization. In this way, decision makers will be able to interact with the data and analyze it and be able to better manage the business, discover opportunities and improve efficiency (Kurnia, 2018, Rahardja and Harahap, 2019). Business intelligence has been a priority for IT executives for the favorable circumstances for companies looking to advance their business (Liang and Liu, 2018).
In the conventional concept of management, decision support has executed a significant part in the efficiency of organizations (Choi et al., 2020, Yiu et al., 2021). The favorable circumstances derived from info and their analysis have led to a great deal of interest in business intelligence and data analysis in organizations, and users to make business-related decisions in a timely manner (Bhatiasevi and Naglis, 2020).
Companies seeking internationalization use business intelligence to create strategy and technology, collect and analyze foreign market information, and predict future market attractiveness and new foreign markets (Sun et al., 2018, Trakadas et al., 2020). By employing business intelligence systems, companies are supported in creating important and vital information of their business and their transparent and intelligent processing. As a result, employees may be able to make finer decisions, reach the outcomes they need quicker, and continually advance them (Jayakrishnan et al., 2018, Moreno et al., 2019).
In the world today, the connection of intellectual capital to entrepreneurship is slowly expanding. Entrepreneurial genius plays an important role in creating new businesses (Verma et al., 2021, Jelonek et al., 2019, Jaklič et al., 2018, Huang et al., 2022). It can be argued that in addition to cost and quality, which are vital factors for competition, there are other factors that play an important role in achieving and maintaining a company's competitiveness (Caseiro and Coelho, 2018, Kurniawan et al., 2021, Nazari et al., 2022). To successfully evaluate the future of a new venture, stakeholders, campaign committees, and potential investors, such as bankers, explicitly need to provide a business model to consider financing a new business (Garbuio and Lin, 2019, Cautela et al., 2019, Tadayon et al., 2019, Khan et al., 2020). That's why business modeling is now one of the institutionalized activities for any new entrepreneur (Cheng et al., 2020, Jooste et al., 2018, Ramakrishnan et al., 2020).
Start-ups are a good opportunity to move between traditional and modern businesses, and improving their conditions by creating a culture and changing conditions, as well as increasing the workforce in technology development sectors, will increase the welfare of society and improve economic conditions, all of which They are deeply dependent on start-ups (Lederer and Schmid, 2021, Sanasi et al., 2019, Ilieva et al., 2021).
In the study of Rahiminasab et al. (2020) by employing multi-factor decision-making, a suitable cluster head was selected. Compared to BCDCP method, Energy reduction was 5% more, and packet loss rate was 25% less. In the study of (Ebadi and Shiri Shahraki, 2010) Banker's description of scale elasticity and returns to scale was changed. Farther, a suitable algorithm was employed to detect scale elasticity in duration of non-discretionary aspects. In the study of Heydarpour et al. (2020) an ordinary differential equations (ODEs) system was employed for prediction process. Farther, an artificial neural network (ANN) is applied to solve the ordinary differential equations system by minimizing the error function and developing parameters consisting of biases/weights. In the study of He et al. (2022) dynamical manner of model was tested numerically/analytically. the complexity analysis is employed by approximate entropy (ApEn) and C0 complexity to affirm the chaos being. In critically overloaded datasets, employing traditional binary or multi-class categorization usually cause bias towards the category with bigger values of occasions. In the study of Seliya et al. (2021) One-class classification (OCC) process was employed to expose abnormal info points compared to the effects of a known category.
The purpose of this study is to look deeper into how business startups should identify and prioritize issues and problems when launching a new product or service. First, startups and subject-related businesses reviewed and then novel and emerging issues, including the status of data on startups' performance, the topics of Business Intelligence (BI), innovation and networked learning, and also their role in startups business discussed.
Hypothesizes are: The characteristics of BI used affect the efficiency of start-up businesses; There is a significant relationship between innovation and start-up business performance; Network learning affects the efficiency of start-ups; The business intelligence features used influence the innovation of start-ups; The business intelligence features used influence the network learning of start-ups; There is a significant relationship between innovation and network learning in start-ups.
Section snippets
Type and Method of research, Validity, Statistical sample, and Sample size
The choice of research method is one of the important stages of research that depends on the objectives, nature of the subject and its implementation possibilities. A research method is a set of valid rules, tools, and methods for investigating facts, discovering unknowns, and finding solutions to problems. The purpose of this research is to examine the effect of BI capabilities on the efficiency of start-up businesses, which is descriptive in terms of the type of research.
In this study, a
Results
Today, the managers of any business need to collect and process information properly. The rapid growth of information and the expansion of its domains of influence in human life has left a vague space for scientists. The pace of change is such that thinkers try to evaluate and monitor the extent of its impact. The effects of this factor on the business environment have created an indescribable prosperity in the world.
Discussion
- •
The main benefits of business intelligence are better decision making, improving business processes and supporting the achievement of strategic business goals among competitors, and also by using business intelligence systems, organizations in creating important information and processing of their business, they are supported with transparency and intelligence. The results of this study also confirm the positive relationship between business intelligence and business performance. Therefore,
Conclusion
This study seeks to enhance start-up businesses' understanding of the impact of technology, specifically BI, on their business efficacy, and to assist research that seeks to understand the impact of this technology on such businesses. In this study, a total of six hypotheses were analyzed using descriptive and inferential statistical methods. The results of the hypothesis test are as follows:
- •
The characteristics of BI used affect the efficacy of start-up businesses.
- •
There is a significant
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
This work was sponsored in part by National Social Science Foundation of China: Research on the behavior and dynamic incentive mechanism of cross organizational knowledge sharing in the context of differentiation (20BGL126).
References (39)
Networked learning with professionals boosts students' self-efficacy for social networking and professional development
Computers & Education
(2018)- et al.
The influence of Business Intelligence capacity, network learning and innovativeness on startups performance
Journal of Innovation & Knowledge
(2019) - et al.
Facilitating speed of internationalization: The roles of business intelligence and organizational agility
Journal of Business Research
(2020) - et al.
Social media analytics and business intelligence research: A systematic review
Information Processing & Management
(2020) - et al.
Determination of scale elasticity in the existence of non-discretionary factors in performance analysis
Knowledge-Based Systems
(2010) - et al.
The Business Intelligence impact on the financial performance of start-ups
Information Processing & Management
(2022) - et al.
The role of compatibility in predicting business intelligence and analytics use intentions
International Journal of Information Management
(2018) Business intelligence model to analyze social media information
Procedia Computer Science
(2018)- et al.
Research landscape of business intelligence and big data analytics: A bibliometrics study
Expert Systems with Applications
(2018) - et al.
Artificial intelligence in marketing: Systematic review and future research direction
International Journal of Information Management Data Insights
(2021)
Elucidating the determinants of business intelligence adoption and organizational performance
Information Development
Business intelligence and competitiveness: the mediating role of entrepreneurial orientation
Competitiveness Review: An International Business Journal
The impact of artificial intelligence on design thinking practice: insights from the ecosystem of startups
Strategic Design Research Journal
Artificial intelligence as a growth engine for health care startups: Emerging business models
California Management Review
Developing social capital to achieve superior internationalization: A conceptual model
Journal of International Entrepreneurship
Fractional-Order Discrete-Time SIR Epidemic Model with Vaccination: Chaos and Complexity
Mathematics
Solving an optimal control problem of cancer treatment by artificial neural networks
International Journal of Interactive Multimedia & Artificial Intelligence
Modelling in support of decision making in business intelligence
Integration Challenges for Analytics, Business Intelligence, and Data Mining
Cited by (7)
Impact Of Business Intelligence (Bi) On E-Hrm Practices Within Ngos In Jordan
2024, Quality - Access to SuccessBusiness intelligence and its pivotal role in organizational performance: An exhaustive literature review
2024, Journal of Autonomous IntelligenceThe Impact of Business Intelligence Capabilities and Social Media Usage on Sustainable Entrepreneurship in Bahrain
2023, International Journal of Interdisciplinary Organizational StudiesThe Relationship Between Business Intelligence Capabilities and Business Outcomes of Small and Medium Sized Enterprises: The Moderating Role of Competitive Intelligence
2023, Journal of System and Management SciencesThe Level up of Digital Commerce Performance Model for University Digital Startup Ecosystem
2023, 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2023