Literature Review
Artificial Intelligence (AI) has revolutionized various business management activities improving service delivery and product or service quality. Various scholars have written on the impact of AI on business management activities and have conducted conclusive research on specific business management areas of activities. AI has impacted the six areas of business management activities: finance, accounting, marketing, sales, human resource, and customer service (Ionescu, 2019). Although various scholarly research articles exist on the AI impact on business management activities, AI’s impact on employee motivation remains a conundrum and has been less covered. Understanding AI’s impact on the six basic business activities enables an in-depth analysis of AI’s impact on employee motivation (Modgil, Singh, and Hannibal, 2021).
This literature review defines important terms within this research, explores AI’s impact on the six business activities, and identifies an existing gap within the research topic. The literature review examines the meaning of business activities, AI, and employee motivation. The different forms of employee motivation will also be discussed in the literature. Furthermore, the review will explore the various management activities enhanced within the business activities by AI integration. This literature review explores the main management activities: planning and decision-making, finance management, organization, business leadership, and monitoring and evaluation. Business management activities should be distinguished from business activities since management activities are the basic operations of business activities. Although AI has impacted other business activities and operations, this review will focus on the six business activities and analyze the AI impact on employee motivation.
Furthermore, this literature review will explore and examine the various gaps in the existing literature. The review will analyze the content of the various scholarly works and identify the crucial topics the existing literature ignores. Since the AI impact on business and management activities is many, the literature review will be constrained to the impact of AI on specific business activities and management activities. The review will discuss the interrelationship between AI’s impact on business and management activities.
Terminological Delineation of Business Activities
Businesses are formed to make profits and engage in any activity that allows profit-making. Therefore, the many activities that the various business stakeholders engage in can be termed business activities (Farooq et al., 2021). The various business stakeholders include all persons with a vested interest in any business or company, which can affect the business performance and operations (Morea, Fortunati, and Martiniello, 2021). Although various scholars have tried to define “business activities,” there is no singular and widely accepted definition due to the wide scope of business operations. The business operations involve activities from the formation of a business entity, activities during the active business period, and those after the liquidation of the business entity.
Business activities involve those activities done within a business entity at local and international levels. International business involves the movement of goods from one country to another: importation, expiration, and trade (Wettstein et al., 2019). Furthermore, international business activities include contractual agreements that allow foreign companies to use products and services from other countries (Contractor, 2021). Like any other business activity, an international business activity involves management activities such as planning, organization, management, and leadership. Therefore, international business activities involve managing all activities involving selling goods or services and contractual agreements among companies in different countries (Sharma et al., 2020). Business activities also involve business entities carried out at local levels.
The majority of business activities are carried out within the business organization. The companies organize and plan human resources, planning, marketing, finance, customer service, and sales. The existing business activities’ definitions focus on the activities carried out within the organizations and in the operational countries ignoring the international business activities aspect. Business activities involve the activities that businesses engage in. The local business activities include operations, investments, and other ongoing activities to create value for shareholders. Like international business activities, local business activities involve planning, operations, and leadership. Therefore, business management activities include international and local activities carried out by an organization to make profits.
Terminological Delineation of Employee motivation
The employees are the backbone of any organization since they take part in its day-to-day activities. Therefore, the employees need motivation and an environment supporting their optimal performance (Norbu and Wetprasit, 2021). Various scholars have defined employee motivation and have emphasized the crucial role of employees for optimal business performance. Employee motivation can be succinctly described as the daily enthusiasm, commitment, and creativity that employees contribute to an organization (Riyanto, Endri, and Herlisha, 2021). Derived from the Latin word movere, motivation means movement and can be described as a behavior needed to achieve intended goals and results (Bushi, 2021). Therefore, employee motivation involves employees’ engagement in line with the organization’s goals and empowerment.
Scholars have categorized employee motivation between intrinsic and extrinsic motivation. Intrinsic motivation involves a personal desire to provide excellent services or goods in tandem with an employee’s belief system (Guul, Pedersen, and Petersen, 2021). Intrinsic motivation is increased by praising employees for their performance within a company (Nguyen, 2019). Although scholars have defined intrinsic motivation succinctly, factors that build up a personal belief system have been ignored. For instance, a Muslim would be less motivated to work in a pork processing company, and a computer nerd would be highly motivated to work in a technological company. Therefore, various factors influence employees’ belief systems: religion, educational level, and social status, among others. Motivated employees actively participate in various business management activities in marketing, sales, finance, accounting, customer service, and human resource (De Bruyn et al., 2020). While intrinsic motivation is solely based on personal motivation, extrinsic motivation is based on external factors, including the business environment.
Rewards play a crucial role in improving employees’ willingness to learn new skills and optimize production. Extrinsic motivation involves bonuses, perks, and awards that motivate people leading to productive performance (Ali et al., 2020). Some employees may not be motivated by personal belief systems, but external factors may work for them (Aguinis, 2019). When giving rewards, managers should be cautious since over-rewarding may lead to poor performance in the absence of the same. Although the initial theoretical research has focused on employee motivation, the research has ignored the role of AI in employee motivation. However, the initial theoretical research gives background information on the definition and factors influencing employee motivation.
Terminological Delineation of AI
Computer science has developed intelligent technology that outwits a natural human performance. Technology application in various sectors like health, manufacturing, education, and transport has catapulted output and efficiency. Scholars in business and technology fields have researched and written about the important role of technology in human life. The hallmark of technology was the invention of AI. AI, a computer science branch, involves smart machine building with the capacity to perform tasks requiring human intelligence (Velikorossov et al., 2020). AI has revolutionized business activities and output through enhanced manufacturing processes, data management, product promotion, and human resource (Tao et al., 2018). The existing research on AI is purely technical, eliminating AI’s role in social activities such as marketing, sales, customer service, and human resource. Intelligent technology has enhanced business activities in various dimensions, including employee motivation through automation and digitization.
Automation and digitization are the key features of AI that have enhanced efficient business activities. Automation is a technological application in which human input is minimized by using intelligent technology, including business process automation (BPA) (Hyun et al.,2021). BPA includes automated manufacturing and marketing processes, among other processes such as payroll processing, that are automated within an organization. Many people conflate between digitization, digitalization, and digital transformation. However, scholars have defined digitization as creating physical objects’ digital presentation (Hendriato, 2021). Digitization in business includes creating flyers, online campaigns, and other digital attributes. The digitized media content is attractive and contains messages which are audience-specific. Digitized systems combine various technological aspects, including automation to convince the potential consumer to buy specific products based on their behaviors (Yogeshi, Sharaha, and Roopan, 2019). AI has transformed business activities through automation and digitization, allowing efficient business management.
Automation and digitization have revolutionized manufacturing processes and the product output. Automation in manufacturing involves high volume production of new products in an integrated modeling environment (Bag, Gupta, and Kumar, 2021). Automating manufacturing processes leads to high production rates, efficient material use, high product quality, improved safety, reduced work weeks, and reduced factory lead times (Rese, Ganster, and Baier, 2020). The automated manufacturing system utilizes intelligent technology to mix proportional raw materials amounts which could be subject to human error (Bag, Gupta, and Kumar, 2021). Therefore, businesses utilizing automation have assured product quality, maintaining their brand value. Moreover, businesses can adopt a value-based pricing strategy, which is profitable.
The Role of AI in Business Activities
Recent technological advancements have led to the integration of AI in various business activities. The AI integration has made work easier within the various organizational departments leading to quality work and improved employee attitude (Madakam, Holmukhe, and Jaiswal, 2019). Marketing, accounting, sales, human resource, finance, and customer service activities have been exponentially improved through AI integration. Therefore, AI integration remains crucial in business management activities since it enhances product and service quality.
AI integration in Marketing
Marketing plays an important role in identifying potential clients and new business ventures. Traditional marketing involves reaching an audience offline through direct and print media activities (Yogeshi, Sharaha, and Roopan, 2019). However, marketing can be done over the internet with AI integration to reach out to millions of potential consumers. AI marketing involves leveraging technology to improve potential customer reach (Campbell et al., 2020). Furthermore, AI marketing improves an organization’s return on investment (ROI) (Ikumoro and Jawad, 2019). Organizations use big data analytics, machine learning, and other process to gain insight into their target audience (Gillath et al., 2021). The organizations create effective consumer touchpoints and eliminate the guesswork in customer interactions.
AI integration in marketing involves content curation and generation, enhanced digital advertising, chatbots, behavior, and predictive analysis. AI automates content generation on basic levels and allows the creation of high-quality advertising content (Risi and Togelius, 2020). Marketers map out an end-to-end content strategy through AI and could be used to generate comprehensive reporting with little human intervention (Mugambi et al., 2018). Enhanced digital marketing involves using autonomous systems that place the right kinds of ads in front of the right audience (Han et al., 2021). Digital marketing may also involve electronic media like billboards with automated messages (Tschang and Almirall, 2021). Organizations optimize their ROI by reaching out to the relevant audience with a relevant message (Risi and Togelius, 2020). Chatbots have automated marketing processes by automatically answering customers’ questions without human intervention (Tschang and Almirall, 2021).
Furthermore, marketing departments use data scientists and experienced programmers to predict a product’s future performance using intelligent technologies. Machine learning and big data analysis allow organizations to get deep insights into their consumers’ behaviors and predict their future performance (Tschang and Almirall, 2021). AI integration in marketing allows ease of planning, expeditious decision-making, and automated monitoring of marketing activities since the processes are automated (Yogeshi, Sharaha, and Roopan, 2019). Although the existing literature explores the various uses of AI in marketing, little attention is paid to the effects of AI on marketers’ motivation.
AI integration in Sales
Sales involve the routine transactions that businesses engage in and may involve exchanging money or value. Automation has revolutionized sales since organizations facilitate the rise of leads, allow costs to drop, and enable easy ups-selling and cross-selling (Ancillai et al.,2020). AI saves time reaching targeted and potential prospects through automated data consolidation and automation (Tschang and Almirall, 2021). Selling activities at low levels are automated, saving on running costs, and the energy that could be spent is channeled to other profitable activities (Ancillai et al.,2020). Furthermore, automated and digitized activities such as invoice generation and payment methods allow businesses to exchange value and services without the risk of carrying hard cash for exchange (Tripoli and Schmidhuber, 2018, p.32). Therefore, AI has made selling activities simple, secure, and efficient among businesses and their customers.
AI integration in Customer Service
Digitization and automation have revolutionized customer service since companies avoid face-to-face customer services and adopt online responses. AI helps companies gain insights across customer contact channels, optimize agent availability, escalate customer complaints through predictive analytics, and deliver personalized service anywhere (Panda, Upadhyay, and Khandelwal, 2019, p.202). Customer service is essential in determining a company’s current and future performance (Zhou et al., 2018). Furthermore, efficient customer service helps build consumer brand equity, boosting sales (Zhou et al., 2018, p.521). AI integration through Chatbots helps companies promptly respond to their clients (Panda, Upadhyay, and Khandelwal, 2019, p.200). Many companies have integrated their websites with Chatbots allowing customer services (Rese, Ganster, and Baier, 2020).
Furthermore, companies have adopted digital and automated customer service systems through social media. The systems respond to client’s inquiries about the company, products, and performance (Rese, A., Ganster, and Baier, 2020). For instance, chatbots respond to any inquiry regarding product price and category available (Tschang and Almirall, 2021). Logistics companies allow customers to place their orders and track product delivery through automated geolocation systems (Ding et al., 2021, p.336). Furthermore, e-commerce platforms have integrated features that allow consumers to place their preferred orders and track their package movement (Boysen, Fedtke, and Schwerdfeger, 2021). AI integration in customer service has various advantages for companies and customers.
Automated customer services help businesses stay afloat since sensitive customer complaints can be identified (Boysen, Fedtke, and Schwerdfeger, 2021). Chatbots save time wasted by customer service agents who can only attend to one client at a time. Therefore, Chatbots allow companies to maintain their existing customers and attract potential customers through accurate and prompt responses (Ding et al., 2021). Furthermore, automated customer services help companies save on costs since not many customer service agents are needed (Ding et al., 2021, p.337). Addressing sensitive customer complaints through predictive systems allows companies to reduce business risk and maintain brand image and a good reputation (Rese, Ganster, and Baier, 2020). AI plays a crucial role in ensuring efficient customer services helping businesses avoid risks and maintain their reputation.
AI integration in accounting and finance
Accounting and finance are crucial in running businesses since it helps in tracking income expenditures, ensuring statutory compliance, and providing other financial information crucial for decision-making. Various scholars have explored the importance of AI in accounting and finance (Moll and Yigitbasioglu, 2019). In accounting, AI helps identify hidden fraud insights, provides better financial forecasts and data input automation, and enhances better audits and risk assessment (Ünal, Urbinati, and Chiaroni, 2019). The accounting and finance department utilizes automation and complex computerized algorithms to predict a company’s future financial performance (Moll and Yigitbasioglu, 2019). The existing literature focuses on the importance of AI in accounting and finance.
The finance and accounting department is sensitive and must comply with existing statutory requirements. AI, through specialized systems, allows a company to calculate the amount of tax due and automate payments on time (Ünal, Urbinati, and Chiaroni, 2019). Furthermore, AI adoption allows accountants to detect fraud since the automated system provides expenditure reports and suspicious payments (Bao, Hilary, and Ke, 2022). Therefore, AI helps companies avoid criminal liability risks by eliminating all fraudulent causing within an organization’s finance department (Bao, Hilary, and Ke, 2022). Automated reports also allow companies to conduct better audits and avoid non-compliance within the various departments of a company (Valeri, 2021). A proper audit enables companies to gain credibility, set financial statements, and give shareholders confidence in trusting accounting reports (Bao, Hilary, and Ke, 2022). Furthermore, the accounting and finance departments improve internal controls and systems (Ünal, Urbinati, and Chiaroni, 2019). AI further allows keeping and automating salary payments, saving time spent during salary allocation.
AI utilizes dependable databases that are automatically updated and backed up. Therefore, AI eliminates the need for physical back-ups cumbersome and space-wasting in an organization (Tadapaneni, 2020). The accountants’ tasks are eased since they do not have to do repetitive processes like payroll system updates, which AI automates. Furthermore, AI’s big data analytics and predictive analysis allow companies to predict future financial performance (Dubey et al., 2020). The systems are armed with complex formulae to predict future financial performance based on the companies’ current performance (Rahman et al., 2019). AI help solve businesses’ financial problems by task for the human brain (Dubey et al., 2020). Although the existing literature provides insight into AI and finance, it conflates the impact of AI on employee performance. The literature does not explore how accountants are motivated and demotivated by AI.
AI Integration in Human Resource
Human resource management involves employing people, training, compensating, and developing employee policies. AI plays a significant role in human resource management for the department’s efficiency (Yawalkar, 2019). Through pre-programmed algorithms, AI helps make real-time decisions based on the company’s performance (Ghoshal, 2020). AI has various applications in HR, including talent acquisition and recruitment, recruits’ orientation, employee training, and enhancing employee experience (Oswal, Rajput, and Seth, 2022). AI makes human resource activities easier and more productive, leading to efficient employee performance.
Talent acquisition involves identifying skilled workers who meet a company’s needs. The HR team is involved in identifying, acquiring, assessing, and hiring candidates who can fill the existing open positions within an organization (Hamza et al., 2021). AI, through specialized programs, helps in screening applicants, scheduling interviews, and resolving contestants’ queries during the hiring process (Anitha and Shanthi, 2021). The most suitable candidates are reached out and communicated to by Chatbots that automatically update their profiles (Hamza et al., 2021). Therefore, by using AI, HR saves time and acquires the most suitable candidates.
Upon hiring, the AI systems are used in training the recruits by use of data analytics systems. The recruits get information about the company, job profile, team member information, and business regulations, among other activities, from highly specialized AI systems (Graesch, Hensel-Börner, and Henseler, 2021). Furthermore, specialized systems help verify the recruits’ legal documents, such as licenses, passports, and certificates (Anitha and Shanthi, 2021). Consequently, the systems ensure that the recruits are genuine and have crucial information about their job description. Furthermore, AI can be used for employee training, especially in risky tasks such as handling dangerous chemicals (Yawalkar, 2019). AI helps improve the employee experience through automated systems that make their work easier. Although AI has revolutionized human resources, many people have lost their jobs to machines and specialized programs (Tri, Hoang, and Dung, 2021). Human resource is a significant department whose efficiency has been improved by acquiring competent employees and employee training that human beings cannot do.
Automated Business Activities and Business Management Activities
Business management activities involve those activities such as planning, decision-making, and coordination within an organization. Although various scholars have written on business management activities, there is no single and accepted definition of the term. However, business management activities can be defined as the coordination and organization of business activities, including material production, money, innovation, and marketing (Onufrey and Bergek, 2021). Management activities can also be described as the discipline coordinating all firm’s phases from operation to planning (Tarigan and Siagian, 2021). Business management is typically concerned with income and, therefore, a company’s profitability (Kuo, Lin, and Chien, 2021). AI plays a crucial role in enhancing organizations’ various business management activities. According to the existing literature, the basic business management activities impacted by AI are planning and decision-making, coordination, business leadership, and monitoring and evaluation activities.
Planning and Decision Making
Businesses are involved in planning activities to remain sustainable and avoid future risks. The managers and supervisors are often involved in preparing future tasks among other employees (Jimmieson et al., 2021). The six basic business activities involve planning to ensure effectiveness and optimized output (Niu et al., 2021). Marketing involves the creation of a marketing plan which includes the target audience, marketing duration, and budget (Stevens, 2021). Accounting involves creating spending plans, including future payments, due dates, and future earnings (Stevens, 2021). AI allows managers and business leaders to develop plans for various business activities (Dubey et al., 2020). Proper plans enable the business stakeholders to make crucial decisions. Business decisions determine business sustainability and future performance; therefore, the business plans must be well-informed to avoid future risks. AI, through analytics, allows the manager to make accurate decisions and workable plans.
AI enables business managers to make accurate, consistent, and fast decisions. AI systems can analyze large and complex data sets (Gutierrez-Gutierrez, Barrales-Molina, and Kaynak, 2018). In marketing, AI systems analyze the market and use natural language to understand consumer needs and brand recognition (Mustak, Salminen, and Wirtz, 2021). The managers use the system’s results in making marketing plans and deciding on budget allocation. Furthermore, AI helps companies understand their customers and make plans (Mustak, Salminen, and Wirtz, 2021). For instance, Chatbots, algorithms, and machine learning provide a deep understanding of customers’ pain, expectations, and satisfaction levels (Gutierrez-Gutierrez, Barrales-Molina, and Kaynak, 2018). The collected data can determine suitable strategies that would sustain the customers and attract more customers.
Activities’ Coordination
Businesses involve a complex and wide range of activities, from manufacturing, marketing, advertising, and procurement. Business managers find themselves in limbo if they are improperly coordinated and planned (Chechile, 2021). Technological advancements have led to the invention of intelligent coordinating system applications. The applications include asset management software, business invoicing programs, and database software that collects and organizes the companies’ information (Daneci-Patrau and Jenaru, 2021, p.63). Furthermore, project management software such as Trello and Wrike enables managers to coordinate and assign various tasks according to priority (Savio, 2021, p.8). The systems use complex algorithms and machine language to prioritize business activities and assign them to the most appropriate employee (Savio, 2021, p.9). The literature focuses on applying AI in coordinating business activities, assuming the impacts of the same on employee motivation, especially the business managers.
Business Leadership
Business leadership involves all the persons responsible for the business operations and includes the board of directors and managers. The leaders are involved in the decision-making processes in the business (Tang et al., 2020). They make financial decisions and other decisions regarding business performance and specific employee performance. AI plays a significant role in helping business management make important and wise decisions (Tang et al., 2020). The use of big data analytics and predictive analysis allows the management to comprehend and decide on the most appropriate business decisions (Yarlagadda, 2018).
Furthermore, the emergence of natural pandemics such as covid 19 made it difficult for the various management to meet physically (Sipior, 2020). However, AI integration in the communication systems allows leaders to meet virtually and make business decisions (Sipior, 2020). The impact of AI on leadership can be understood better from the planning and decision-making management activity.
Monitoring and Evaluating (M&E) Activities
Big data is the most fundamental AI invention that has allowed companies to keep track and evaluate performance at different levels. M & E activities involve interpreting the data containing greater variety, which arrives in increasing volume. Data science allows businesses to conduct a survey and interpret the data collected to optimize their routine activities. Programming languages like Python and R are used to interpret the collected data using text analytics: scraping and transforming texts into meaningful insights (Savio, 2021, p.9). Key decision-makers use the interpreted data to monitor and evaluate business performance (Gutierrez-Gutierrez, Barrales-Molina, and Kaynak, 2018). Moreover, AI systems evaluate employee performance and make necessary promotions. M & E is also conducted at the manufacturing level to determine product quality.
Product quality is a key attribute that attracts more consumers and helps build brand image. Complex systems are advanced at the manufacturing stages for quality control (Mustak, Salminen, and Wirtz, 2021). AI-enabled software, such as smart cameras, helps businesses improve product quality inspection (Gutierrez-Gutierrez, Barrales-Molina, and Kaynak, 2018). Companies that use AI in quality control have various advantages: saving on costs, attracting more customers, have an excellent brand image, and recording increased profits (Boysen, Fedtke, and Schwerdfeger, 2021). Although AI presents advantages over traditional quality controls, acquiring the system is very expensive.
The gaps in the Existing Literature
The existing literature presents several gaps that this research will try to fill. AI integration impacts businesses in various dimensions, including employee proactiveness. However, the existing research emphasizes AI’s impact on business and management activities. Much of the existing literature focuses on general business and management activities, ignoring specific activities such as marketing and procurement. This research will focus on the impact of AI on employee motivation since employees play a significant role in ensuring AI performance. The study will examine the various factors attributed to AI that motivate employees at different levels of business activities.
Furthermore, the existing literature emphasizes the advantages of AI on business activities, ignoring the same setbacks. This study will fill that gap by exploring the various disadvantages of AI on employees. The study will focus on the negative AI effects on employee proactiveness and answer whether employees are willing to embrace AI, given that their jobs may be at risk. Therefore, this study is significant and will contribute to the existing literature.
References
Aguinis, H., 2019. Performance management for dummies. John Wiley & Sons.
Anitha, K. and Shanthi, V., 2021. A Study on Intervention of Chatbots in Recruitment. In Innovations in Information and Communication Technologies (IICT-2020) (pp. 67-74). Springer, Cham. Web.
Ali, AAAM, Kee, D.M.H., Singh, A., Pandey, R., Alhamlan, H., Ahmad, K.M., Quttainah, M.A., Siw, M.Y., Ong, R.S. and Narayanan, Y., 2020. Does Motivation Improve Employee’s Job Performance? A Case of Absolute Hotel Services. International Journal of Tourism and hospitality in Asia Pasific (IJTHAP), 3(3), pp.9-21. Web.
Ancillai, C., Terho, H., Cardinali, S. and Pascucci, F., 2019. Advancing social media driven sales research: Establishing conceptual foundations for B-to-B social selling. Industrial Marketing Management, 82, pp.293-308. Web.
Bag, S., Gupta, S. and Kumar, S., 2021. Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development. International journal of production economics, 231, p.107844. Web.
Bao, Y., Hilary, G. and Ke, B., 2022. Artificial intelligence and fraud detection. In Innovative Technology at the Interface of Finance and Operations (pp. 223-247). Springer, Cham. Web.
Boysen, N., Fedtke, S. and Schwerdfeger, S., 2021. Last-mile delivery concepts: a survey from an operational research perspective. Or Spectrum, 43(1), pp.1-58. Web.
Bushi, F., 2021. An Overview of Motivation Theories: The Impact of Employee Motivation on Achieving Organizational Goals. Quality-Access to Success, 22(183).
Campbell, C., Sands, S., Ferraro, C., Tsao, H.Y.J. and Mavrommatis, A., 2020. From data to action: How marketers can leverage AI. Business Horizons, 63(2), pp.227-243. Web.
Chechile, I., 2021. NewSpace Systems Engineering. Springer International Publishing.
Contractor, F.J., 2021. The world economy will need even more globalization in the post-pandemic 2021 decade. Journal of International Business Studies, pp.1-16. Web.
Daneci-Patrau, D. and Jenaru, A., 2021. Information flow regarding invoices and outstanding beneficiaries in an “Access” application. Journal of Social Sciences, 1(4), pp.58-65. Web.
De Bruyn, A., Viswanathan, V., Beh, Y.S., Brock, J.K.U. and von Wangenheim, F., 2020. Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51, pp.91-105. Web.
Ding, Y., Jin, M., Li, S. and Feng, D., 2021. Smart logistics based on the internet of things technology: an overview. International Journal of Logistics Research and Applications, 24(4), pp.323-345. Web.
Dubey, R., Gunasekaran, A., Childe, S.J., Bryde, D.J., Giannakis, M., Foropon, C., Roubaud, D. and Hazen, B.T., 2020. Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organizations. International Journal of Production Economics, 226, p.107599. Web.
Farooq, Q., Fu, P., Liu, X. and Hao, Y., 2021. Basics of macro to microlevel corporate social responsibility and advancement in triple bottom line theory. Corporate Social Responsibility and Environmental Management, 28(3), pp.969-979. Web.
Ghoshal, ND, 2020. Digital marketing: the opportunity and the imperative. International Journal of Public Sector Performance Management, 6(2), pp.246-259. Web.
Gillath, O., Ai, T., Branicky, M.S., Keshmiri, S., Davison, R.B. and Spaulding, R., 2021. Attachment and trust in artificial intelligence. Computers in Human Behavior, 115, p.106607.
Graesch, J.P., Hensel-Börner, S. and Henseler, J., 2021. Information technology and marketing: an important partnership for decades. Industrial Management & Data Systems. Web.
Gutierrez-Gutierrez, L.J., Barrales-Molina, V. and Kaynak, H., 2018. The role of human resource-related quality management practices in new product development: a dynamic capability perspective. International Journal of Operations & Production Management. Web.
Guul, T.S., Pedersen, M.J. and Petersen, N.B.G., 2021. Creaming among Caseworkers: Effects of Client Competence and Client Motivation on Caseworkers’ Willingness to Help. Public Administration Review, 81(1), pp.12-22. Web.
Han, R., Lam, H.K., Zhan, Y., Wang, Y., Dwivedi, YK and Tan, K.H., 2021. Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions. Industrial Management & Data Systems. Web.
Hamza, P.A., Othman, B.J., Gardi, B., Sorguli, S., Aziz, H.M., Ahmed, S.A., Sabir, B.Y., Ismael, N.B., Ali, B.J. and Anwar, G., 2021. Recruitment and Selection: The Relationship between Recruitment and Selection with Organizational Performance. International Journal of Engineering, Business and Management, 5(3), pp.1-13. Web.
Hendriarto, P., 2021. Understanding of the role of digitalization to business model and innovation: economics and business review studies. Linguistics and Culture Review, 5(S1), pp.160-173. Web.
Hyun, Y., Lee, D., Chae, U., Ko, J. and Lee, J., 2021. Improvement of Business Productivity by Applying Robotic Process Automation. Applied Sciences, 11(22), p.10656. Web.
Ikumoro, A.O. and Jawad, M.S., 2019. Assessing Intelligence Conversation Agent Trends-Chatbots-AI Technology Application for Personalized Marketing.
Ionescu, L., 2019. Big data, blockchain, and artificial intelligence in cloud-based accounting information systems. Analysis and Metaphysics, (18), pp.44-49. Web.
Jimmieson, N.L., Bergin, A.J., Bordia, P. and Tucker, M.K., 2021. Supervisor strategies and resources needed for managing employee stress: A qualitative analysis. Safety science, 136, p.105149. Web.
Kuo, Y.F., Lin, Y.M. and Chien, H.F., 2021. Corporate social responsibility, enterprise risk management, and real earnings management: Evidence from managerial confidence. Finance Research Letters, 41, p.101805. Web.
Madakam, S., Holmukhe, R.M. and Jaiswal, D.K., 2019. The future digital work force: robotic process automation (RPA). JISTEM-Journal of Information Systems and Technology Management, 16. Web.
Modgil, S., Singh, R.K. and Hannibal, C., 2021. Artificial intelligence for supply chain resilience: learning from Covid-19. The International Journal of Logistics Management. Web.
Moll, J. and Yigitbasioglu, O., 2019. The role of internet-related technologies in shaping the work of accountants: New directions for accounting research. The British Accounting Review, 51(6), p.100833. Web.
Morea, D., Fortunati, S. and Martiniello, L., 2021. Circular economy and corporate social responsibility: Towards an integrated strategic approach in the multinational cosmetics industry. Journal of Cleaner Production, 315, p.128232. Web.
Mugambi, M.L., Peter, T., Martins, S.F. and Giachetti, C., 2018. How to implement new diagnostic products in low-resource settings: an end-to-end framework. BMJ global health, 3(6), p.e000914. Web.
Mulyati, E. and Harieti, N., 2018. Model of business activities of microfinance institutions in Indonesia. In IOP Conference Series: Earth and Environmental Science (Vol. 175, No. 1, p. 012194). IOP Publishing. Web.
Mustak, M., Salminen, J., Plé, L. and Wirtz, J., 2021. Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research, 124, pp.389-404. Web.
Nguyen, T.M., 2019. Do extrinsic motivation and organizational culture additively strengthen intrinsic motivation in online knowledge sharing? An empirical study. VINE Journal of Information and Knowledge Management Systems. Web.
Niu, Y., Ying, L., Yang, J., Bao, M. and Sivaparthipan, C.B., 2021. Organizational business intelligence and decision making using big data analytics. Information Processing & Management, 58(6), p.102725. Web.
Norbu, J. and Wetprasit, P., 2021. The study of job motivational factors and its influence on job satisfaction for hotel employees of Thimphu, Bhutan. Journal of Quality Assurance in Hospitality & Tourism, 22(2), pp.245-266. Web.
Onufrey, K. and Bergek, A., 2021. Transformation in a mature industry: The role of business and innovation strategies. Technovation, 105, p.102190. Web.
Oswal, J., Rajput, N. and Seth, S., 2022. Managing Human Resources in Artificial Intelligence Era 5.0. In Handbook of Research on Innovative Management Using AI in Industry 5.0 (pp. 150-164). IGI Global. Web.
Panda, G., Upadhyay, A.K. and Khandelwal, K., 2019. Artificial intelligence: A strategic disruption in public relations. Journal of Creative Communications, 14(3), pp.196-213. Web.
Rahman, M.H., Tumpa, T.J., Ali, S.M. and Paul, S.K., 2019. A grey approach to predicting healthcare performance. Measurement, 134, pp.307-325. Web.
Rese, A., Ganster, L. and Baier, D., 2020. Chatbots in retailers’ customer communication: How to measure their acceptance?. Journal of Retailing and Consumer Services, 56, p.102176. Web.
Risi, S. and Togelius, J., 2020. Increasing generality in machine learning through procedural content generation. Nature Machine Intelligence, 2(8), pp.428-436. Web.
Riyanto, S., Endri, E. and Herlisha, N., 2021. Effect of work motivation and job satisfaction on employee performance: Mediating role of employee engagement. Problems and Perspectives in Management, 19(3), pp.162-174. Web.
Savio, R., 2021. Latest Project Management Trends and Challenges with COVID-19. Eximia, 2(1), pp.8-11. Web.
Sharma, P., Leung, T.Y., Kingshott, R.P., Davcik, N.S. and Cardinali, S., 2020. Managing uncertainty during a global pandemic: An international business perspective. Journal of business research, 116, pp.188-192. Web.
Sipior, J.C., 2020. Considerations for development and use of AI in response to COVID-19. International Journal of Information Management, 55, p.102170. Web.
Stevens, R.E., Loudon, D.L., Wrenn, B. and Warren, W.E., 2021. Marketing planning guide. CRC Press.
Tang, X., Li, X., Ding, Y., Song, M. and Bu, Y., 2020. The pace of artificial intelligence innovations: speed, talent, and trial-and-error. Journal of Informetrics, 14(4). Web.
Tao, F., Qi, Q., Liu, A. and Kusiak, A., 2018. Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, pp.157-169. Web.
Tadapaneni, N.R., 2020. Artificial Intelligence in Finance and Investments. International journal of innovative research in science, engineering and technology.
Tarigan, Z.J.H. and Siagian, H., 2021. The effects of strategic planning, purchasing strategy and strategic partnership on operational performance (Doctoral dissertation, Petra Christian University). Web.
Tri, N.M., Hoang, P.D. and Dung, N.T., 2021. Impact of the industrial revolution 4.0 on higher education in Vietnam: challenges and opportunities. Linguistics and Culture Review, 5(S3), pp.1-15. Web.
Tripoli, M. and Schmidhuber, J., 2018. Emerging Opportunities for the Application of Blockchain in the Agri-food Industry. FAO and ICTSD: Rome and Geneva. Licence: CC BY-NC-SA, 3. Web.
Tschang, F.T. and Almirall, E., 2021. Artificial intelligence as augmenting automation: implications for employment. Academy of Management Perspectives, 35(4), pp.642-659. Web.
Ünal, E., Urbinati, A. and Chiaroni, D., 2019. Managerial practices for designing circular economy business models: the case of an Italian SME in the office supply industry. Journal of Manufacturing Technology Management. Web.
Valeri, M., 2021. Post-modernism and organizational design. In Organizational Studies (pp. 85-98). Springer, Cham. Web.
Velikorossov, V.V., Maksimov, M.I., Orekhov, S.A., Huseynov, S.E.O., Filin, S.A. and Tserenchimed, S., 2020. Artificial Intelligence as an Innovative Tool of the Support System for Making Management Decisions. DEStech Transactions on Social Science, Education and Human Science.
Wettstein, F., Giuliani, E., Santangelo, G.D. and Stahl, G.K., 2019. International business and human rights: A research agenda. Journal of World Business, 54(1), pp.54-65. Web.
Yawalkar, M.V.V., 2019. a Study of Artificial Intelligence and its role in Human Resource Management. International Journal of Research and Analytical Reviews (IJRAR), 6, pp.20-24.
Yarlagadda, R.T., 2018. Internet of things & Artificial Intelligence in modern society. International Journal of Creative Research Thoughts (IJCRT), ISSN, pp.2320-2882. Web.
Yogesh, S., Sharaha, N. and Roopan, S., 2019. Digital marketing and its analysis. International Journal of Innovative Research in Computer and Communication Engineering, 5(7), p.201957007.
Zhou, S., Qiao, Z., Du, Q., Wang, G.A., Fan, W. and Yan, X., 2018. Measuring customer agility from online reviews using big data text analytics. Journal of Management Information Systems, 35(2), pp.510-539. Web.