Conceptual framework: Artificial intelligence and better entrepreneurial decision-making. The influence of customer preference, industry benchmark, and employee involvement in an emerging market

Amoako, George Kofi; Omari, Paul; Kumi, Desmond Kwadjo; Agbemabiase, George Cudjoe; Asamoah, George Article Conceptual framework: Artificial intelligence and better entrepreneurial decision-making. The influence of customer preference, industry benchmark, and employee involvement in an emerging market Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Amoako, George Kofi; Omari, Paul; Kumi, Desmond Kwadjo; Agbemabiase, George Cudjoe; Asamoah, George (2021) : Conceptual framework: Artificial intelligence and better entrepreneurial decision-making. The influence of customer preference, industry benchmark, and employee involvement in an emerging market, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 14, Iss. 12, pp. 1-20, https://doi.org/10.3390/jrfm14120604 This Version is available at: https://hdl.handle.net/10419/258707 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Risk and Financial Management Article Conceptual Framework—Artificial Intelligence and Better Entrepreneurial Decision-Making: The Influence of Customer Preference, Industry Benchmark, and Employee Involvement in an Emerging Market George Amoako 1,* , Paul Omari 1, Desmond K. Kumi 2, George Cudjoe Agbemabiase 1and George Asamoah 3   Citation: Amoako, George, Paul Omari, Desmond K. Kumi, George Cudjoe Agbemabiase, and George Asamoah. 2021. Conceptual Framework—Artificial Intelligence and Better Entrepreneurial Decision-Making: The Influence of Customer Preference, Industry Benchmark, and Employee Involvement in an Emerging Market. Journal of Risk and Financial Management 14: 604. https:// doi.org/10.3390/jrfm14120604 Academic Editors: David Roubaud and Thanasis Stengos Received: 22 September 2021 Accepted: 30 November 2021 Published: 13 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Department of Marketing, University of Professional Studies, Accra P.O. Box 149, Ghana; [email protected] (P.O.); [email protected] (G.C.A.) 2DAL Consultancy, Co. Ltd., Kwabenya, Accra Box KW 310, Ghana; [email protected] 3 Department of Marketing, Ghana Institute of Journalisms, Accra P.O. Box 667, Ghana; [email protected] *Correspondence: [email protected] Abstract: 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 entrepreneurial perspective. In addition, the study demonstrates that employees, as stakeholders, can moderate the relationship between Artificial intelligence systems and better decision-making for entrepreneurs with their involvement. Moreover, the study demonstrates that customer preference and industry benchmark can mediate the relationship between Artificial intelligence systems and better entrepreneur decision-making. Research limitations/implications : The study assumes a perfect ICT environment for the smooth operation of Artificial intelligence systems. However, this might not always be the case. The study does not consider the personal disposition of entrepreneurs in terms of ICT usage and adoption. Practical implications: This study proposes that entrepreneurial decisionmaking is enriched in an environment of Artificial intelligence systems, which is complemented by customer preference, industry benchmark, and employee involvement. This finding provides entrepreneurs with a possible technological tool for better decision-making, highlighting the endless options offered by Artificial intelligence systems. Social Implications : The introduction of AI in the business decision-making process comes with many social issues in relation to the impact machines have on humans and society. This paper suggests how this new technology should be used without destroying society. Originality/value : This conceptual framework serves as a valuable organizational spectrum for entrepreneurial development. In addition, this study makes a valuable contribution to entrepreneurial development through Artificial intelligence systems. Keywords: entrepreneurship; decision-making; artificial intelligence; employee involvement; customer preference 1. Introduction Entrepreneurial development studies are rapidly gathering momentum in the 21st century, due to the growing recognition of its potential in wealth creation and general J. Risk Financial Manag. 2021,14, 604. https://doi.org/10.3390/jrfm14120604 https://www.mdpi.com/journal/jrfm J. Risk Financial Manag. 2021,14, 604 2 of 20 economic development (Robson et al. 2009). Academics and practitioners acknowledge the immense benefits accrued from entrepreneurial development (Barringer and Ireland 2010). Entrepreneurs innovate, create new business ideas, and take financial risks in converting perceived opportunities to viable business ideas. As people who employ limited, heterogeneous resources under uncertain conditions in order to cater to customer preferences and make a profit, entrepreneurs engage in largely experimental processes, with context affecting this experimental process (Shane and Venkataraman 2000). Entrepreneurs can change the economicsituation of emerging economies with the right environment and technology tools. Entrepreneurs can utilize the appropriate technology tools to detect business trends and offer valuable insights, which are required for business decisions (Akinyemi and Adejumo 2018). The impact of technology on entrepreneurial development cannot be overstated, which explains the global commitment to entrepreneurial development. For instance, the Small Business Innovation Research (SBIR) program in the USA remarkably improved the survival and growth rates of Small and Medium Enterprises (SMEs) ( Akinyemi and Adejumo 2018 ). Similar policies have been implemented in European Union states to promote entrepreneurial activities and motivate SMEs to conduct business globally (Akinyemi and Adejumo 2018). Similar policies exist in varying degrees to promote entrepreneurial initiatives in emerging economies. For instance, Ghana has established the Ghana Enterprises Agency (GEA 2021) to improve the knowledge, skill, behavior, and attitudes of individuals and groups who aspire to be entrepreneurs (https://gea.gov.gh (accessed on 23 November 2021)). The contemporary social and business environment is experiencing an intensive wave of digitalization. Global economies have evolved from brick and mortar to convenience at the click of buttons. This modernization influences business functions and transforms enterprises and societies (Wirtz and Zeithaml 2018). The rapidly improving technology is transforming all business sectors, and as technology becomes better and more convenient, organizations seek ways to utilize it to gain a competitive advantage (Wirtz and Zeithaml 2018). Technology advancement and its resultant connectivity of the society and business environment continue to lead to the creation of massive volumes of data (Obschonka and Audretsch 2020). A significant feature of data in modern business is a constant exponential growth in volume. Over 90% of all existing data has been generated (IBM 2020) . Presently, businesses thrive on the foundations of data and the ability of business organizations to gather, interrogate, manage, and utilize data to differentiate their offerings (George et al. 2014) . Businesses use data to obtain a proper understanding of their operations, especially insights into the behavior of their customers, in order to facilitate strategic decision-making. The decision-making process in business involves understanding the trends and patterns in business growth, which are supported by data. The ability to transform collected data into value-for-economic benefit/profit is a skill (George et al. 2014). In other words, accurate data are not obtained solely for the creation of practical insight, but also for their implementation in strategic business decision-making. Therefore, this is expected of entrepreneurs due to the significance of making precise decisions under unpredictable conditions, in order to discover business opportunities (Shane and Venkataraman 2000). Entrepreneurs take high-risk decisions (Baron 2004) under highly unpredictable, ambiguous, time-constrained, and emotionally strained contexts. The efficient exploitation of data has accompanied the growth of information technology architecture, thereby influencing decisions concerning the desirability and viability of entrepreneurial ideas, making entrepreneurial decision-making primarily knowledgebased (Wiklund and Shepherd 2008). Therefore, knowledge-based information systems are valuable tools for entrepreneurs, enabling evidence-based decision-making in complex business situations. Moreover, artificial intelligence (AI) based applications are developing in a wide range of knowledge-based domains (Agrawal et al. 2019). The advancement of artificial intelligence (AI) has altered the dynamics of the business world. AI discussions J. Risk Financial Manag. 2021,14, 604 3 of 20 have centered on its transformational potential for efficiency in pursuing commercial value creation, which helps entrepreneurs evaluate, discover, and exploit opportunities and solutions under business uncertainties (Agrawal et al. 2019). Artificial intelligence comprises building computer systems that can perform tasks that demand human intelligence, for instance, making decisions. A significant application of AI results in improved decisionmaking and better business performance (Agrawal et al. 2018;Lévesque et al. 2020). In this paper, artificial intelligence (AI) systems comprising advanced data analysis, data mining, cloud computing, and machine learning technologies are discussed and posited to influence better entrepreneurial decision-making through customer preference and industry benchmark mediation. The study further posits that artificial intelligence (AI) systems directly influence better entrepreneurial decision-making and argues that employee involvement may moderate the direct influence of AI systems on better entrepreneurial decision-making. Employee involvement, encapsulated in the circular economy theory, is considered fundamental to the value delivery process and has consequences for firm performance (Payne et al. 2009;Prahalad and Ramaswamy 2004). 2. Literature Review 2.1. Artificial Intelligence Artificial intelligence (AI) refers to a software system designed to conduct tasks that require human intelligence (Huang and Rust 2018). In other words, it refers to a system that can imitate human intelligence in the execution of specific tasks, for example, visual insight, speech recognition, recommendation, categorization, and decision-making. AI has four key elements: 1. Expert systems; 2. Heuristic problem solving; 3. Natural language processing; and 4. Vision. Natural language processing offers an interaction between people and machines in their natural language. An expert system is a mechanical system where valuable human knowledge is embedded into machine memory to provide intelligent guidance, clarify, and justify its choices or needs. Expert systems handle situations and deliver performance by relying on a vast dataset of precise, specialized knowledge concerning a specific field of interest. Heuristic problem solving is intended to assess a limited scope of solutions and may comprise certain presumptions to find the best solutions. Vision is the capacity to identify shapes and features, to mention a few, automatically (Huang and Rust 2018; Guibao 2016). Artificial intelligence (AI) is a term propounded by John McCarthy in 1956. It is defined as “the science and engineering of making intelligent machines” (McCarthy 2000). Computer science focuses on the study and design of intelligent agents that notice their environment and take actions that increase their likelihood of success. Therefore, machines programmed to perform decision-making tasks demand artificial intelligence from human intelligence (Syam and Sharma 2018). The scientific goal of AI is to comprehend intelligence by designing computer software programs that display intelligence utilizing symbolic inference or cognition inside the machine. AI systems are designed to work with their own created programming language to employ information more efficiently (Syam and Sharma 2018) . These programming languages employ declarative knowledge, particularly with claims whose truth-value is autonomous of the algorithmic context. Additionally, the AI system can induce, abstract, and occasionally predict data. The AI system can reevaluate decisions using backtracking of solutions. In other words, the system includes a recollection of past experiences in providing good inference power and quick responses to enable better decision-making (Shankar 2018). In essence, the idea of AI is to integrate large volumes of data with rapid procedures and better algorithms. Eventually, this enables the systems to learn from patterns without the need for re-programming (Vasiljeva et al. 2021;Kaplan and Haenlein 2019). J. Risk Financial Manag. 2021,14, 604 4 of 20 Furthermore, by identifying patterns in data, AI systems can “reason” and efficiently recommend the ideal options for consumers’ specified needs. AI gathers information from unstructured data through personality and sentiment analyses (such as facial coding), which enables businesses to measure the affective state of consumers (Shankar 2018). Then, AI generates content through the following methods: Natural language generation (NLG): Businesses can utilize AI tools, for example, Wordsmith, for creating human-sounding and original content, from tailored messages to news articles or utilize AI for creating marketing content. Image generation: Creating realistic pictures and animated movies based on text descriptions. Speech generation: Offering meaningful voiceovers for business promotional campaigns. The AI system can make decisions based on priorities, and deal with complexity and uncertainty by relying on technologies, such as extensive data analysis, machine learning, data mining, and cloud computing, etc. (Davenport and Ronanki 2018). A study by Sivathanu and Pillai (2020) in India suggests that the technology orientation of entrepreneurs significantly influences sustainable enterprise performance. Additionally, AI depends not only on its fundamental technology, but also on its business use, such as industry benchmarks and consumers’ and employees’ engagement (Davenport and Ronanki 2018). By utilizing these tools, AI offers a credible approach to the correct interpretation of external data, demonstrates institutional memory, and exhibits better decision-making processes (Kaplan and Haenlein 2019). Adopting artificial intelligence (AI) seems likely to influence better entrepreneurial decision-making through a more efficient business automation process. AI is documented as one of the new technologies that businesses need to adapt in order to reduce costs, as well as increase performance and competitiveness in the market environment (Vasiljeva et al. 2021). AI has become integral to voice assistants, commonly manifested as Amazon’s Echo, Google’s Google Assistant, Microsoft’s Cortana, Apple’s Siri, etc. These have contributed to the changing consumer behavior, and 27% of the global online population regularly use voice assistants (McCue 2018). The use of voice assistants is projected to increase exponentially from 2018 to 2023 (Juniper Research 2018), which necessitates entrepreneurial AI orientation. This paper discusses Artificial Intelligence (AI), Big Data Analysis,Machine Learning, Data Mining, and Cloud Computing. 2.1.1. Big Data Analysis Data are an essential ingredient of the digital space. The routine capture of digital information through different applications creates massive data streams of customers, and the expected commercial usage of modern technologies has dramatically expanded the volume and scope of the data gathered by businesses (Obschonka and Audretsch 2020) . These data contain valuable customer information that can enhance the strategic development of businesses. Additionally, as data increasingly play a central role in business organizations, entrepreneurs aim to harness them for better decision-making (Obschonka and Audretsch 2020). The volume of acquired data enables entrepreneurs to recognize new business opportunities or future markets using, for instance, household consumer profiles. Big Data has evolved as a term that includes both the technical and commercial components of increased data collection activity (Nunan and Di Domenico 2019). Big Data is the fundamental notion of gathering vast amounts of data from consumers. In other words, it refers to the ability to aggregate and separate comprehensive datasets with very little manual labor. Big Data is the explosive growth of data, which is mainly due to advancements in data storage technology (Nunan and Di Domenico 2019). Big Data refers to vast amounts of data, which traditional data management approaches cannot handle and process due to their complexity and massiveness (Nunan and Di Domenico 2019). Arguably, Big Data has been described to have volume, velocity, and variety as its fundamental features and J. Risk Financial Manag. 2021,14, 604 5 of 20 ensures cost-effectiveness and innovative information processing techniques for improved understanding, decision-making, and process automation (Beyer and Laney 2012). The idea of Big Data has grown to comprise not only the size of datasets, but the dataset characteristics and data management methods (Ohlhorst 2013;Bi and Cochran 2014). Big Data refers to the real-time analysis of all parts of large data sizes. The decision-making process in an organization requires the assessment of large datasets to comprehend trends and developments in business growth. Therefore, Big Data analytics provides solutions for better entrepreneurial decision-making, enabling the achievement of good returns on investments (Ohlhorst 2013). Big Data analytics is the comprehensive method of gathering, capturing, and analyzing enormous and diverse datasets in order to find concealed patterns, unidentified correlations, market trends, and consumer preferences that can assist firms in making informed and better business decisions (Obschonka and Audretsch 2020). As a framework enhanced for obtaining, shaping, and stacking unstructured data into databases, Big Data analytics can recognize growth opportunities in new and existing businesses, predict customers’ behavior, and assist businesses in making better and more strategic business decisions (Obschonka and Audretsch 2020) . Big Data analytics can transform data into value, process, and evaluate how the data that can improve decision-making for the benefit of businesses are handled. Big Data analytics has an enormous potential for creating value for firms, particularly when properly aligned with business cycles and knowledge needs. It can substantially enhance performance and the nature of entrepreneurs’ decisions (Obschonka and Audretsch 2020) . Big Data analytics offers valuable insights that could improve entrepreneurial decisionmaking, particularly in recognizing growth patterns and creating growth opportunities for entrepreneurs (Obschonka and Audretsch 2020). Big Data analytics prepares entrepreneurs to capture, evaluate, store, and manage vast volumes of existing data. Business owners utilize Big Data analytics to discover weaknesses in their services and products, suppliers, and customers, as well as consumer intentions and preferences, to design new and improved products (Obschonka and Audretsch 2020). Big Data analytics can influence improvements in the efficiency of business operations by helping organizations in predicting unpredictable situations and improving their performance process through cost reduction, best operation plan, smaller inventory sizes, productive labor force, and removal of wastage (Hiba et al. 2017). Big Data analytics is fundamental in business decision-making and can help businesses achieve a competitive advantage (Hiba et al. 2017). Additionally, Big Data analytics can affect the operation process effectiveness and organizational performance (Ghasemaghaei et al. 2015). Utilizing Big Data analytics, entrepreneurs can predict customer behavior and design, as well as enhance marketing strategies and sales planning. Big Data analytics tools can promote innovation and growth that enable the informed decision-making in companies and can aid in offering new and existing companies unparalleled insight (Obschonka et al. 2020). Utilizing Big Data analytics tools makes entrepreneurs more knowledgeable and puts them in a position to make better decisions and invest wisely (Obschonka and Audretsch 2020). Big Data records are extracted from various applications and platforms and can alter development, as well as fast-track social and economic advancement. 2.1.2. Machine Learning Machine learning studies computational methods for enhancing performance by automating knowledge acquisition from experience (Brynjolfsson et al. 2018). The goal of machine learning is to offer increased levels of automation in the knowledge engineering process, removing laborious human activities and replacing them with automated methods that enhance accuracy or productivity. This is achieved through finding and using the appropriate regulations in data training. J. Risk Financial Manag. 2021,14, 604 6 of 20 Machine learning addresses the question of how to develop computers that automatically improve through experience. It is at the core of artificial intelligence and data science (Brynjolfsson et al. 2018). Machine learning refers to the use of artificial intelligence (AI) in order to provide machines with the capacity to automatically learn and upgrade without the direct intervention of humans (Brynjolfsson et al. 2018). In general, machine learning also refers to building and utilizing models based on recognized patterns. This enables the retrieval of important information from enormous data repositories. Machine learning platforms can help in recognizing and understanding trends or common conditions, as well as effectively predicting insights and reactions that help businesses understand key factors and the likelihood of recurrence of specific activities (Brynjolfsson et al. 2018). Machine learning (ML) also refers to a set of algorithms that enhances the performance of AI. The ML algorithms are mechanically produced from data, and the richer the dataset, the better the performance (Jordan and Mitchell 2015). In other words, ML entails the utilization of algorithms to analyze data, learn from it, and make a conclusion or forecast as a result. ML may involve basic learning and deep learning algorithms. Basic learning algorithms comprise one phase of learning and are appropriate for examining structured data, such as price, size or time, and for predicting results based on a set of inputs or grouping items per their features (Jordan and Mitchell 2015). Several examples include predicting a consumer’s churn, the possibility of default (credit scoring), and detecting fraud in financial transactions. However, deep learning algorithms comprise various learning stages that are systematized in a similar manner to the brain’s structure. They are appropriate for assessing unstructured data, including pictures, audio recordings or texts, and can be used for facial recognition, speech-to-text transcription or text reconstitution (Jordan and Mitchell 2015). In contrast to basic learning algorithms, deep learning algorithms fundamentally open new approaches for data-driven decision-making, since few alternative methods are available for processing unstructured data. For instance, to forecast whether a client is likely to churn, an ML method will initially train an algorithm to connect customers’ churning rates with their qualities on a subset of data (training dataset). After this step, another data subset will be utilized to authenticate the creation of the algorithm (validation dataset). Additional tests include the predicting ability of the final algorithm on another data subset (testing dataset), before predicting the likelihood of the churn. After the process of segmenting unstructured datasets, deep learning algorithms comprise many iterations (Jordan and Mitchell 2015). In AI, machine learning algorithms (for example, collaborative filtering, deep learning, unsupervised clustering, and k-nearest neighbors) have evolved as the favored technique for designing applications that comprehend consumer preferences (from their reviews, past product procurement, and use) to identify new products or services that they will possibly like (Pollack et al. 2019). Recommendation engines are typical machine learning applications where users are matched with products/services that they previously liked or may like in the future. These recommendations decrease users’ mental burden and assume the duty of finding the best options for consumers to search platforms. Similarly, AI can aid in predicting the customer lifetime value and conversion rate (Pollack et al. 2019) . Through assessing trends and learning from data about customers’ past behavior at the experimental stage of a product, AI can determine how likely a customer is to buy the premium version or forecast the future value of a specific user. In AI, machine learning has evolved as the preferred approach for designing practical software for computer vision, speech recognition, natural language processing, robot control, and other applications (Pollack et al. 2019). Several developers of AI systems currently acknowledge that, for various applications, it is simpler to train a system by presenting examples of the preferred input-output behavior than to program it manually by predicting the expected reaction for all of the probable inputs. Likewise, the impact of machine learning has been experienced extensively within computer science and various industries regarding data-intensive challenges, for example, consumer services, fault diagnosis in complex systems, and management of logistic chains (Pollack et al. 2019). J. Risk Financial Manag. 2021,14, 604 7 of 20 2.1.3. Data Mining Data mining is the study of gathering, cleaning, processing, evaluating, and acquiring valuable knowledge from data (Chung and Gray 1999). Many challenges exist in domains, applications, formulations, and data depictions of real applications. In contemporary times, practically all of the automated systems produce data for diagnostic or analysis purposes, resulting in a massive accumulation of data (Obschonka and Audretsch 2020). The raw data might be arbitrary, unstructured or in a form that is not instantly fit for computer processing. Data mining analysts utilize a processing pipeline in order to extract the existing data for application-specific objectives, where raw data are gathered, cleaned, and refined into a standard form. The data could be kept in a commercial database system and processed for insights using analytical methods. This processing pipeline is theoretically similar to mining from a mineral ore to a polished product (Chung and Gray 1999). The term “mining” stems from this analogy. Data mining aims to find valid, new, possibly valuable, and clear connections and patterns that are present in data (Chung and Gray 1999). Data mining aids firms in concentrating on the most valuable data present in their current databases. Data mining has provided value to a wide variety of industries and has been used to boost profits by decreasing costs and increasing revenue. Several firms utilize data mining to facilitate the customer life cycle management, including obtaining new customers, growing profits from existing customers, and keeping good customers (Chung and Gray 1999). When a firm knows the qualities of good customers (profiling), it can focus on potential customers with similar qualities. By profiling customers who purchased a specific product, a company can concentrate on customers with similar qualities who have not purchased that product (cross-selling) (Berry and Linoff 2000). Likewise, profiling helps them keep the customers who are at risk of leaving (decreasing churn or attrition), since it is generally far less expensive to keep a customer than to acquire a new one (Berry and Linoff 2000). 2.1.4. Cloud Computing Cloud computing uses a vast network of remote servers that are hosted on the Internet to store, manage, and process data. The data-sharing architecture of cloud computing is utilized for AI and non-AI-related purposes (Kumar 2016). AI joins an automated and data-driven learning process. In addition, cloud computing refers to storing and accessing data and programs online rather than on the PC’s hard drive (Kumar 2016). The cloud refers to the Internet. Effectively, with an online connection, cloud computing can be performed anywhere, anytime. Cloud computing is an Internet-based technology, in which data is stored on servers and provided as a service and on-demand to customers. It is defined as a vast pool of easily usable and accessible virtualized resources (for example, hardware, development platforms, and services) that can be dynamically reconfigured to adjust to a variable load (scale), enabling optimum resource use (Mirzayi and Rafe 2015). These resources are usually exploited by a pay-per-use model, in which the infrastructure provider offers guarantees through customized Service Level Agreements (Mirzayi and Rafe 2015). Companies can rent computing power (hardware and software, usually in their latest versions) and storage from the service provider and pay on-demand. This significantly affects the cost structure of companies (Karthic et al. 2012). Cloud computing characterizes a crucial change in how information technology (IT) services are designed, developed, deployed, scaled, updated, maintained, and purchased. Cloud computing delivers all the functionality of existing information technology services and drastically decreases the initial costs of computing that prevent several firms from implementing various advanced IT infrastructures (Mirzayi and Rafe 2015). Furthermore, cloud computing characterizes business agility, whereby IT can be utilized as a competitive business tool through fast deployment, parallel batch processing, compute-intensive business analytics, and mobile interactive applications that respond in real-time to user J. Risk Financial Manag. 2021,14, 604 8 of 20 needs (Bharathi and Neelamegam 2012). In effect, cloud computing enables firms to utilize computational tools that can be implemented and scaled quickly. Cloud computing can be encapsulated from a unique business perspective and its unique features from a technological perspective. It operates as an information technology service model where computing services (both hardware and software) are provided ondemand to users over a network in a self-service model, independent of device and location (Mirzayi and Rafe 2015). The resources needed to offer the necessary quality-of-service levels are shared, dynamically scalable, rapidly provisioned, virtualized, and released with little service provider interaction. Customers pay for the service as an operating cost without experiencing any substantial initial capital expenditure. The cloud services utilize a metering system that splits the computing resource into appropriate blocks (Mirzayi and Rafe 2015). 3. Development of Hypotheses and Conceptual Framework 3.1. Artificial Intelligence and Customer Preference Marketing comprises all of the activities, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers and all stakeholders, such as clients, partners, and society at large (AMA 2020). In effect, marketing is considered as treating customers well and meeting their needs satisfactorily. This is sufficiently possible when customers’ profiles are properly collated and analyzed. Research conducted in Mauritius, an emerging economy (Gungaphul and Boolaky 2009), shows that entrepreneurs regard marketing as an essential function in attaining their business goals. Customer preference refers to the subjective (individual) tastes, as measured by the utility of various bundles of goods and services (Venkatraman et al. 2012). These preferences enable the customer to rank bundles of products and services according to the levels of utility they deliver. Customer preference assumes that the customer can choose consistently and among the available alternative products or services. Understanding customer preferences and identifying their characteristics is crucial for the management of customers by entrepreneurs (Venkatraman et al. 2012). This knowledge is crucial in developing approaches to satisfying customers’ needs. Artificial intelligence enabled devices, such as mobile devices and social media platforms, enable the efficient generation of rich customer information that helps in discovering customer preferences (Kaplan and Haenlein 2019). From the preceding information, the following proposition is suggested: Artificial Intelligence (AI) systems have a direct and positive influence on customer preference. 3.2. Artificial Intelligence and Industry Benchmarking Benchmarking is a very general business concept used in many forms by businesses. It is a useful business tool that enables a company to systematically gain new useful business knowledge that can increase the quality of its decision-making. It is conceptualized as a systematic and continuous process involving the comparison of characteristics of the best products, services, and processes in order to improve business performance (Battagello et al. 2016). In other words, benchmarking involves the process of continuous searching for the best practices of competitors and other companies that lead to above-average performance when applied in an enterprise (Raybourn and Coers 2001). The main findings of benchmarking is the generation of business knowledge for the transformation, using comparison and analysis findings, in business decision-making. Industry benchmarking is more effective when defined by an organized intelligence system, which is crucial for choosing benchmarks and assessing the patterns of important, selected benchmarks. Intelligence systems, such as artificial intelligence (AI), enable benchmarking to be a continuous process requiring regular updates, which is adequately adaptable to incorporate and integrate new approaches of capturing data from the competitive environment (Rai 2019). J. Risk Financial Manag. 2021,14, 604 15 of 20 The exponential growth in the digital space has been accompanied by the exploitation of the data produced by a progressively technologically linked society (George et al. 2014). Artificial intelligence-based applications are actively created and utilized in different domains. These knowledge-based information systems are advanced tools for entrepreneurs, enabling evidence-based decision-making in complex business situations. With these technological tools, entrepreneurs stand a better chance of understanding their customers, helping them to optimize real practices in their decision-making process. Moreover, the notion of circular economy (CE) can be used to leverage AI to enhance society and human integration. According to Saiz-Alvarez (2020), CE is regarded as an enhanced system that can be used to stop the depletion of resources, close energy, and materials circles, as well as support sustainable development through its adaption at the micro (enterprises and customers), meso (economic agents joined into symbiosis), and macro (city, regions, and governments) levels. Digital changes in how businesses function are inevitable, and entrepreneurs must incorporate this innovation into the sustainable business environment. The impact of this innovation can be seen in each fragment of the business process. Given that entrepreneurship activities are highly uncertain, ambiguous, time-bound, emotionally intense, and involve high-risk decision-making, digital innovation facilitates better entrepreneurial decision-making and aids entrepreneurs in effectively adjusting their strategies based on shifting market requirements and stimuli. Although digital innovation is not a new concept, it appears that entrepreneurs do not give full attention to it, although experimentation and risks are integral to their work processes. However, technology systems, such as artificial intelligence can leverage digital innovation for better entrepreneurial decision-making. The research by Nusair et al. (2021) indicated that firms with better organizational capabilities tend to better satisfy their customers. This implies that firms that engage employees with higher capabilities might satisfy their customers better than firms that engage employees with lower capabilities, due to the fact that the higher capability can correspond to better decision-making. The proposed model suggests that employee involvement can influence entrepreneurial decision-making outcomes. This confirms an earlier study by Kassa and Raju (2015), which suggested that when the firm-specific entrepreneurial environment is favorable, workers would respond as innovative. However, entrepreneurs, managers, and policymakers have to note that AI systems have some challenges. According to Dwivedi et al. (2019), these challenges included the possibility for innate bias in AI algorithms and its implications for people who work closely with intelligent machines, and presented substantial issues with regard to trust, security, and ethical considerations. Moreover, entrepreneurs and managers must grasp the most fundamental AI concepts in order to determine when AI is appropriate for their needs. From a practical standpoint, there is a need for national policy to create entrepreneurfriendly legislations and regulations in order to provide incentives to aspiring entrepreneurs, as well as to provide incentives to existing entrepreneurs in order to expand the scope of their businesses. The conceptual framework clearly indicates a need for government policy toward updating and upgrading the expertise of aspiring and existing entrepreneurs. Furthermore, there is the need for deliberate government policy toward overcoming barriers to entrepreneurial development, such as lack of digital infrastructure, access to finances, and the development of relevant human capital. This study has significant implications for business practice among entrepreneurs in Ghana and other developing countries with similar circumstances. Its findings will enable entrepreneurs to better understand the changing trends in contemporary business practice, and offer a useful reference point for future empirical studies of entrepreneurship in the developing country context. J. Risk Financial Manag. 2021,14, 604 16 of 20 7. Conclusions, Limitations, and Directions for Future Research Understanding and creating better entrepreneurial decision-making is important, since it is widely accepted in the literature that better entrepreneurial decision-making significantly enhances business competitive advantage. This paper discovered the fact that contemporary entrepreneurs are confronted with numerous stakeholders. This requires the adoption of the appropriate tools and techniques in order to adequately harness these resources for the competitive advantage of the entrepreneur. Multinational companies are increasingly posing a competitive advantage to local entrepreneurs as a result of global attention on emerging markets. This necessitates the adoption and usage of modern technological equipment to remain competitive. Additionally, this paper discovered that presently businesses are dynamic and largely digital. Therefore, entrepreneurs benefit from this transformation. A contemporary business is established on data, since people are affected by the ability of businesses to collect, analyze, manage, and use data. Several online platforms provide avenues for data collection. Moreover, the ability to transform the collected data into value-for-economic gain is innovative, which is expected of the modern entrepreneur. The real data lie in the ability of entrepreneurs to develop actionable insight and apply it in strategic and better decisionmaking. Improved decision-making for entrepreneurs should be linked to their desired impact. Therefore, they should consider focusing on the theory of circular economy (CE), since the future of energy production and distribution can be improved with AI systems. Most businesses consciously gather and store data in large databases. Numerous businesses know the potential value of the data to their decision-making process. The rapidly growing demand for better decision-making can be met by increasing knowledge availability. For instance, from the industrial revolution, the advancement in technical innovation has transformed numerous manual tasks and processes and mechanized or automated them. Artificial intelligence (AI) possesses the same transformative power for growth, and possibly takes over some tasks and activities that are usually performed by people in different industrial, intellectual, and social fields. AI technology changes rapidly, presenting new innovations in algorithmic machine learning and autonomous decision-making, creating new opportunities for advancing innovation. The effect of AI significantly cuts across sectors, including finance, healthcare, manufacturing, retail, supply chain, logistics, and utilities. These sectors have been disrupted by AI technologies (Dwivedi et al. 2019) . Industry leaders and entrepreneurs must take advantage of the wide range of artificial intelligence (AI) applications in order to enhance their decision-making process for the benefit of society and firms. However, AI could, in theory, increase the disparity between the developing and developed markets, as well as the poor and wealthy (Bughin et al. 2018). From the review of extant literature in this paper, it is evident that most of the information on entrepreneurial development was based on data from developed nations. Therefore, there is a need to generate empirical evidence from developing economies to support opinions that might offer fresh insights into the entrepreneurial decision-making process. Moreover, the issue of culture has been known to influence entrepreneurial decision-making, but this was not added in the proposed model. Future studies can discuss culture as a factor in decision-making efforts, especially in Africa. For instance, the study by Liu et al. (2019) of research outcomes indicated that Tanzania’s culture affects entrepreneurs’ risk-taking behavior, which impacts their decision to exploit opportunities. Furthermore, the limitations of this paper are as follows: It is conceptual in nature and the proposed model has not been tested. Author Contributions: All authors contribute equally to this research work. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. J. Risk Financial Manag. 2021,14, 604 17 of 20 Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. References Acs, Zoltan J., and David B. Audretsch. 2005. Entrepreneurship, Innovation, and Technological Change. Delft: Now Publishers Inc., vol. 2105. African Union. 2014. Agenda 2063: The Africa We Want, 2nd ed. Addis Ababa: AU. 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