USAGE OF MACHINE LEARNING IN INTERNATIONAL ENTREPRENEURSHIP

Innovation Management, Entrepreneurship and Sustainability (IMES 2020), 2020

Purpose: The purpose of this study is to explore the perceptions, views, and opinions of chief technology officers (CTO) of software development firms (SDF) about how and why machine learning (ML) methodologies might be used to support foreign market evaluation and selection decisions. Design/methodology/approach: A qualitative research was conducted. The research design is a multiple case study with six semi-structured, in depth interviews with CTOs of SDFs and corporate documents about ML applications from the case study firms as sources of evidence. Findings: The results of this multiple case study suggest the following four findings: 1) The usage of ML to support foreign market evaluation and selection decisions has the potential to improve quality and efficiency, 2) data availability is a key factor of ML to support foreign market evaluation and selection decisions, 3) "easy to use" and "easy to interpret" machine learning supervised methods are the most suitable to support foreign market evaluation and selection decisions, and 4) existing ML development methodologies can be applied to support market evaluation and selection decisions. These findings have a limited generalizability due to the research methodology and are valid only for these case study firms. Research/practical implications: The results of this study might be relevant for researchers who are interested in a further digitalization of decision-making processes. The results might also be relevant for practitioners to better understand the use of ML methodologies in complex and financially important decision-making processes like the evaluation and selection of foreign markets. Originality/value: This work integrated fundamental theories of internationalization based on the works of Johanson and Vahlne in the Uppsala Internationalization Process Model with the concepts and methodologies of machine learning, whose relationship is yet not covered by the academic discourse.

Materials Today: Proceedings, Elsevier, ISSN: 2214-7853, 2021

In the modern era, Machine learning and Artificial intelligence remain the most remarkable IT application, a technology that has seen unprecedented progress in recent decades. AI and machine learning are all closely linked with using a computer to simulate intelligent behaviour with little human interaction. It has been noticed that many large organisations are implementing technologies such as ML and AI to improve their performance and productivity in the business. Companies can create better, more customised and engaging campaigns as AI becomes increasingly adept at collecting massive data faster than ever before. As AI and ML technologies become more widely used, being a technology-powered company will undoubtedly be a must for survival. AI and ML are guiding on everything from production to delivering products and services to customers. Machine translation, chatbots, and self-learning algorithms are examples of AI technology that may help people better comprehend their surroundings and respond appropriately. Organisations have been embracing artificial intelligence technology advancements to create and maximising their strategic and competitive advantages. Hence, businesses rely heavily on AI to enhance their performance and develop new services to boost productivity and generate new offerings. In addition, they are fundamentally reshaping companies' business and operation processes to better meet customers' requirements and expectations, resulting in increased efficiency. Machine learning and artificial intelligence are broad concepts that will be examined further in this article. The report aims to understand the relationship between Machine Learning and Artificial Intelligence in large and diversified business organisations. The researcher used a study methodology oriented toward a more inclusive and comprehensive approach to better account for the intangible aids of ML and AI within organisations.

2019

Machine learning (ML) stands out as one of the most successful advanced analytics for dealing with big data. However, as a quite recent tool amongst organizations, there are some doubts hanging over this technology. Through an original lens, we expect to substantiate how organizations can sustained ML business value. We developed a conceptual model, grounded on the resource-based view, that aims to validate key antecedents of ML business value. Through a positivist approach, we imply ML use, big data analytics maturity, top management support and process complexity enhance ML business value, in terms of firm performance. Due to the pioneering nature of our research model, we expect to support our data analysis with the partial least squares. To the authors' best knowledge, this represents the first study aiming such findings on the ML discipline.

Journal of Risk and Financial Management, 2021

Purpose: Technology initiatives are now incorporated into a wide range of business domains. The objective of this paper is to explore the possible effects that Artificial intelligence systems have on entrepreneurs’ decision-making, through the mediation of customer preference and industry benchmark. Design/methodology/approach: This is a non-empirical review of the literature and the development of a conceptual model. Searches were conducted in key academic databases, such as Emerald Online Journals, Taylor and Francis Online Journals, JSTOR Online Journals, Elsevier Online Journals, IEEE Xplore, and Directory of Open Access Journals (DOAJ) for papers which focused on Artificial intelligence (AI), Entrepreneurial decision-making, Customer preference, Industry benchmarks, and Employee involvement. In total, 25 articles met the predefined criteria and were used. Findings: The study proposes that Artificial intelligence systems can facilitate better decision-making from the entrepreneu...

2018

The paper aims to understand the impact of business intelligence on Paraguayan ICT firms' export performance. It adopted a multiple case-study research design using different sources of evidence, including 15 responses from subject-matter experts (SMEs). They were selected using a purposive sampling method. Data collected in November 2017 were analyzed using grounded theory to develop patterns and categories, and to understand differences and consistencies. The revised Uppsala internationalization process model is used as a theoretical framework. This paper provides empirical insights on the impact of business intelligence on the export performance of ICT firms in Paraguay. Although only a few SMEs currently use business intelligence solutions to support international strategic decision-making processes, the majority indicate their intention to use them in expectation of positive impact on export performance and international competitiveness. The main factors for selecting a business intelligence solution are transparency of cost and benefits, an excellent client service and an attractive pricing model. The study results are relevant for all stakeholders, who support the impact of business intelligence systems on the export performance of Paraguayan ICT firms. Future scholarly work should include quantitative assessments of SME perceptions and quantitative data to provide greater clarification of the statistical significance of the variables of this study or to replicate it with other SMEs from different industries and countries. The paper fulfils an identified need and a call for research to study the use and impact of business intelligence on export performance and the competence to globalize Paraguayan ICT firms.

2021

Research background: Through the ongoing trend of digitalization, organizations competing in international markets are getting more exposed to different technology related risks. Globalization and technology support enabled small tech-based companies to scale and expand their business. On the other hand, this has also led to a significant rise of different types of threats. Companies engaged in the process of internalization are more exposed to digital risks than companies competing on the local market. In order to help their companies to manage digital risks, governments use relevant institutions and resources. However, many organizations still largely depend on their own capabilities. A growing number of organizations uses artificial intelligence in business models as a new type of response to digital risks. Artificial intelligence could be the missing link that will help connect organizational and government resources for successful management of digital risks.Purpose of the arti...

2018

The article is written with the aim of understanding how well software firms in emerging economies perform when exporting their goods. Focusing on Paraguay as a representative context, a multiple-case-study research design was adopted using different sources of evid- ence, including 15 in-depth interviews with founders, shareholders, and CEOs. The data were analyzed using grounded theory in order to develop patterns and categories, and to understand differences and regularities. The revised Uppsala internationalization process model was used as a theoretical framework. This article highlights the experts’ views of the impact of business intelligence on the export performance of software firms in Paraguay. Al- though only a few of the interviewees currently use business intelligence solutions to sup- port international strategic decision-making processes, most of them reveal a desire to use them because they expect it will have a positive impact on export performance and interna- tional competitiveness. The main factors for selecting a business intelligence solution are transparency of cost and benefits, excellent client service, and an attractive pricing model. The study results apply to all stakeholders who support the impact of business intelligence systems on the export performance of software firms in emerging economies. The article ful- fils an identified need and call for research to study the use and impact of business intelli- gence on the way an emerging country’s exportation of goods actually performs, and the ability of its software firms to globalize successfully.

2024

The article explores the vital role of machine learning (ML) and data science in advancing business efficiency, especially under crisis conditions like the ongoing conflict in Ukraine. It discusses how digital transformation through these technologies is crucial for maintaining competitiveness and operational resilience. As part of the research, it was conducted deep analysis of existing works. It was identified gap of comprehensive studies on the strategic application of cloud-based ML and data science solutions during crises. This study Highlights the increasing accessibility of ML and data science tools due to technological advancements, fostering a competitive business landscape. The study emphasizes the democratization of advanced technologies facilitated by cloud platforms like Microsoft Azure, Google Cloud Platform (GCP), and Amazon Web Services (AWS), making sophisticated tools accessible to smaller companies. Article concludes that strategic use of ML and data science significantly bolsters business resilience and efficiency, especially in challenging environments like Ukraine. Article examines ML tools and services provided by AWS, Azure, and GCP. As an assessment criterion it was chosen features, integration capabilities, innovation, pricing structures, computing capabilities, and security measures. In scope of this research it was defined that each platform offers robust ML solutions with unique strengths tailored to different business needs. For example, AWS excels in specialized tools, Azure in integration within its ecosystem, and GCP in sustainability and advanced technologies. Article provides recommendations for selecting cloud-based ML and data science solutions that align with operational strategies and crisis management needs. It encourages ongoing research to explore the long-term impacts of these technologies on business innovation and market dynamics. Highlights the need for further studies into the socio-economic impacts of ML and data science, including addressing privacy, security, and ethical concerns. Article provides tailored advice on choosing appropriate ML and data science tools to support their specific needs during the ongoing crisis. Also, it suggests broader adoption of cloud-based ML and data science technologies for enhanced decision-making and operational efficiency.

The researcher focused on the usage of machine learning (ML) in business industries and its significant impact with respect to extracts meaningful insights from raw data to quickly solve complex, data-rich business problems.ML algorithms learn from the data iteratively and allow computers to find different types of hidden insights without being explicitly programmed to do so. ML is evolving at such a rapid rate and is mainly being driven by new computing technologies.Machine learning in business helps in enhancing business scalability and improving business operations for companies across the globe. Artificial intelligence tools and numerous ML algorithms have gained tremendous popularity in the business analytics community. Factors such as growing volumes, easy availability of data, cheaper and faster computational processing, and affordable data storage have led to a massive machine learning boom. Therefore, organizations can now benefit by understanding how businesses can use machine learning and implement the same in their own processes.

Vidyabharati International Interdisciplinary Research Journal , 2021

In this study, a brief detailed description of the significance of Artificial intelligence (AI) and machine learning (ML) in businesses are described. AI is the emerging trend and technology of the modern world and provides various benefits to firms. AI and ML reduce the overall cost of the business operations and save the money of the organization. It also helps the businesses to make smarter decisions in the business processes and also able to provide solutions to the business problems effectively. The chat bots developed on the basis of AI can communicate with the customers anytime 24/7 and solve the queries of the customers regarding any product or business. ML creates opportunities for businesses based on business operations and also makes the process fully automated. In addition to this, the ML also improves the cognitive engagement between customers and employees effectively and provides solutions to problems of customers like password issues and many others. Along with that, the study also contains the methods and techniques used for the completion of the study. The researcher has used the secondary qualitative and quantitative data collection methods in this study. In order to implement AI and ML in business operations, companies need to understand the augmentation and automation process.