Technological advancements have revolutionized business activities, including management, marketing, and the overall business structure. Artificial intelligence (AI) has played a vital role in automating and digitizing business communication. This study focused on the impact of AI on business management activities. The research aimed to examine the impact of AI on business management structure, employee motivation, and product or service quality. The study’s findings presented substantial results that helped the research answer the overarching research questions. Furthermore, the researcher reviewed existing literature on the topic to develop the research hypothesis and theoretical framework. Therefore, this study presents knowledge that can be utilized in academia and the corporate world. This chapter summarizes the study’s findings and discusses the results of the research questions.
The chapter is divided into five sections, each giving in-depth analysis and discussion content as identified during the research. The first section discusses the summary of this study’s findings. The findings are drawn from the study’s collected and analyzed quantitative and qualitative data. The section highlights the key findings that helped the research achieve the study objectives. The second section discusses the key findings that supported the researcher’s answer to the study questions. In the section, the researcher discusses the impact of AI integration on business management structure, employee motivation, and product output quality. Furthermore, the section highlights the critical advantages of AI integration among business activities and during unprecedented natural pandemics like Covid-19. The third section provides recommendations that corporations and Small and Medium Enterprises (SMEs) can adopt to enhance the effectiveness of AI integration. The recommendations give way for further research on the topic so that scholars can expand on the existing study. The fourth section suggests directions for further research by highlighting this study’s limitations and how they can be avoided in future research. Finally, the conclusion summarizes the pertinent issues discussed in this chapter.
Summary Of Findings
This study involved collecting qualitative and quantitative data about AI among businesses in China’s Hong Kong. The collected data was analyzed and interpreted in chapter four of this paper. After the data analysis, the researcher noticed fascinating findings of AI that enabled the achievement of the study’s objectives. The results included AI integration in improved revenues, business performance, costs, working environment, and emergency response.
Improved Revenues Among Companies
Revenues are crucial since they help companies determine their performance and attract investors. The researcher analyzed Alibaba, Baidu, and JD.com financial data over ten years, ranging from 2011 to 2021. The researcher found a significant improvement in the companies’ annual revenues. Furthermore, the online survey and focused groups’ results indicated that AI integration was profitable among the companies. The profitability was owed to the fact that AI helps improve service and product quality. Moreover, integrating AI in marketing activities helps companies retain existing customers and attract new customers over the various digital platforms (Kingsnorth, 2019). Therefore, AI integration contributes to profitability among companies by improving product or services quality and improving online marketing activities.
AI Impact on Companies’ Performance
Business performance is a multi-faceted concept involving financial, social, and technical capabilities (Scarpellini, 2021). Some of the companies engaged in the research were in the manufacturing and processing sectors. The manufacturing companies utilized AI in their various manufacturing processes like mixing chemicals, lifting heavy loads and operating the entire manufacturing process. Meanwhile, the processing companies integrated AI in determining the quality of product output. Both companies utilized AI in their financial analysis and marketing activities. It was fascinating to note that the human resource department is the most active department that uses AI during the recruitment process, altering the business structure and salary management. The participants agreed that AI integration has significantly improved the companies’ performance.
Cost of AI Integration
AI helps companies improve profitability and companies’ performance, but it is costly. Cost analysis is a crucial activity since it helps determine the return on the benefit on companies’ assets (Grover et al., 2018). The analysis enables the management to make the right financial decisions by avoiding inconsistent investment activities (Rosenbusch et al.,2019). The researcher found that AI integration was costly among the companies. Although the respondents did not quote the actual costs of the integration process, the high prices were owed to the initial installation and maintenance. However, the respondents affirmed that AI integration was a worthy risk since it helped increase the companies’ revenues over time. AI integration is a costly process, but with a good return on investment.
AI and Working Environment
An effective working environment involves smooth business activities that support the employees without discrimination. Furthermore, the setting requires unity among the employees. AI integration brings the employees together since it eases the manufacturing processes, among other business activities. Moreover, AI has reduced complex tasks by eliminating human intervention. With AI integration, many employees find their job interesting and enjoyable. According to the respondents, AI integration has enhanced unity between the management and employees since activities like salary management are automated, and the employees are no longer needed to submit manual reports as was before AI integration. The employees enjoy working in an AI-enabled environment since most of their automated activities.
AI Use Among Companies
Businesses involve many activities ranging from recruitment to marketing activities. Effective company management consists of dividing the activities among different departments (Stone, Cox, and Gavin, 2020). The companies can categorize their activities among human resource activities, manufacturing movements, supply chain, logistics, and finance departments, among other departments (Fernando and Wulansari, 2020 ). Each department has unique and executed activities by experts in relevant fields (Colli et al., 2018). For instance, the human resource department is involved in recruiting employees and payroll processing. Meanwhile, the marketing department is involved in product promotion activities. The participating companies utilize AI in various activities like financial management, marketing, and processing. Coherently, AI integration is significant in multiple business activities.
AI Use and Emergency Response
Natural disasters precipitated by environmental degradation, among other causes, are inevitable. For instance, the onset of Covid-19 was a significant blow to many business activities (Fu, Purvis-Roberts, and Williams, 2020). The pandemic was meted out by strict regulations, including total lockdown and shutdown of significant business activities. The researcher noticed fascinating results: the companies’ revenues increased rapidly between 2019 and 2021. There was a steep increase in revenues between 2019 and 2020. During the years, there was an onset of the novel virus. The respondents concluded that AI integration during the pandemic. AI helped the companies enhance their marketing activities through e-commerce and the utilization of social media platforms. Furthermore, the companies utilized AI in automating activities like quality control and product packaging. Although many employees lost their jobs, the business operations remained normal and maintained steep profitability. Although it was not clear how the companies could respond to other emergencies, AI was believed to enhance innovative ideas that would help companies survive natural emergencies.
Impact of AI Integration Among Businesses on Business Management Structure
AI integration is crucial to the business management structure since it leads to the creation of new job designations. AI integration is developed in various business activities ranging from finance management to manufacturing processes (Rabah, 2018). Furthermore, AI plays a crucial role in automating multiple business activities like marketing departments. With the onset of Covid-19, the AI integration impact was evident (Nayal et al., 2020). Many companies reduced their employee population to meet the strict government regulations to combat the pandemic. Consequently, many employees were laid off, and technology took over their designations (Shafi, Liu, and Ren, 2020). Meanwhile, the introduction of new technological use, AI, led to new job positions. According to the study results, AI’s impact on business management had two significant effects: the formation of new job positions and temporary and permanent employee discharge.
New Job Positions
AI integration requires human intervention for effective functionality. Although machines take over humans’ complex jobs, they need humans for control and maintenance (Lu et al., 2020). The researcher asked the respondents how AI impacts the companies’ management. Creating new jobs was a critical issue that came up during the interviews. Business activities like financial management, quality control, manufacturing, and processing are complex and beyond human intervention. Consequently, AI has taken over such complex processes. AI is integrated into quality control, replacing the traditional chemical engineers (Huang and Rust, 2018). Furthermore, AI is integrated into financial management, replacing traditional accountants (Zhang et al.,2020). Meanwhile, the marketing activities that entirely depend on digitization have been taken over by AI. Although there is a massive replacement, new job positions are being created.
AI systems are advanced, requiring complex technical knowledge and skills taught in technical colleges. Data analysts, system engineers, and quantum scientists, among other specialized technicians, are being employed in companies (Aiello et al., 2021). Furthermore, the systems need full-time monitoring to avoid unprecedented events and accidents (Zhang et al.,2020). Consequently, system monitors and the response team get new job descriptions. Meanwhile, the marketing departments utilize AI through chatbots that automate replies to customers. Not to need traditional customer services, the marketing department recruits digital marketing specialists who can manage and handle the automated marketing adverts (Okuda and Shoda, 2018). Creating new positions alters the typical management structure and creates unique job descriptions. Furthermore, the replaced employees are placed in different departments from their original works. For instance, it would be appropriate for the exceptional digital marketer to fall under the Information Technology department rather than marketing (Okuda and Shoda, 2018). AI integration has altered business management structure by creating new job positions and changes in job descriptions.
Creating new job positions leads to employees being discharged in different sectors. Employees without technical skills are discharged since their roles are either taken over by AI or by persons with required technical skills (Nam et al., 2021). During the Covid-19 pandemic, many employees were discharged since the companies could not accommodate them with the dwindling company revenues (Nhamo, Dhube, and Chikodzi, 2020). Furthermore, the integration of e-commerce activities led to the discharge of traditional marketers and manual workers in the supply chain (Leung, Lee, and Choy, 2020). Meanwhile, the processing departments are the most affected since companies no longer need traditional chemical engineers but those with technical skills to control the automated machines in the quality control stage (Nhamo, Dhube, and Chikodzi, 2020). Consequently, AI integration involved the formation of new job descriptions leading to employee discharge.
Impact of AI Integration Among Businesses on the Employees’ Motivation and Activities
Employee motivation is a multi-dimensional concept involving commitment, creativity, and teamwork. AI integration significantly improves business activities and eases business processes. The employees are left on the verge of controlling the automated systems rather than taking up the tasks (Madakam, Holmukhe, and Jaiswal, 2019). Consequently, there is increased employee motivation in the company. The companies’ management takes up many roles like role division and designation of new departments (Rana and Sharma, 2019). According to the study results, AI impacts employee motivation, including improved proactiveness, increased innovation, and enhanced creativity.
Automated business activities enable problem identification and problem-solving beforehand. Proactiveness in the workplace involves self-initiated efforts to bring about a change by solving problems before they occur (Otto et al., 2019). The study results indicated that many employees were working in AI integrated environments experienced lesser issues than those working in a hundred percent manual departments. AI enables employees to identify common problems in processing and other stages during the production process (Paschen, Wilson, and Ferreira, 2020). The majority of the research respondents stated that AI has helped them overcome problems experienced at personal levels and organizational levels. For instance, some of the employees working in the financial departments spent sleepless nights consolidating the companies’ financial statements. The employees experienced psychological problems, including insomnia and depression, due to pressure at work to achieve specific set deadlines. However, with the advent of AI, financial consolidation was automated, and the employees could only generate physical reports. AI integration plays a crucial role in helping the employees identify a problem and solve it at earlier stages (Paschen, Wilson, and Ferreira, 2020).
Increased Employee Motivation
Motivation involves enthusiasm, high energy levels, and commitment to accomplish assigned tasks. Commitment towards action is relative to the employees’ attitude towards their job and working environment (Paschen, Wilson, and Ferreira, 2020). Furthermore, the output quality can affect employee motivation since it allows the employees to appreciate their efforts. AI improves employee motivation in various ways, including facilitation of effective working processes, enabling a scheduled workplan, and improving the product or service output (Yu et al., 2021). Automated process makes working easy since the employees can accomplish a complicated task by pressing a button (Michaelis et al., 2020). Furthermore, AI allows problems identification that is solved on time through the system’s diagnostic tests (Canhoto and Clear, 2020).
Meanwhile, automated management systems allow the human resource department and supervisors to generate a scheduled workplan that can be electronically shared with the relevant employees (Michaelis et al., 2020). Consequently, the employees can plan their workplan on time and accomplish the assigned without any undue influence that may demotivate them (Michaelis et al., 2020). The management and supervisors are also motivated since they can identify any conflicting area within the organization and hold the necessary parties accountable (Canhoto and Clear, 2020). The overall advantage of AI as a driver of employee motivation is a quality product or service output (Thrall et al., 2018). The improved output significantly contributes to employee motivation through appraisal. Effective business processes, timely workplan, and quality product or service output motivate employees in an organization.
Enhanced Employee Creativity and Innovativeness
AI techniques can be used to create new ideas by producing novel combinations of familiar ideas, exploration of conceptual spaces, and generation of previously though difficult ideas. Creativity should be distinguished from innovation since creativity is conceiving something new, but innovation is the act of putting something to practice (Sawyer, 2019). Creativity and innovation go hand-in-hand since creativity enhances innovation (Patricio et al., 2020). AI integration allows the employees to conceive new ideas that help improve the aesthetic and physical touch (Hoyer et al., 2020). Furthermore, the combination of different AI-enhanced processing ideas leads to solving problems that promote creativity in the company (Rem, Astrom, and Eriksson, 2020).
Meanwhile, automated systems allow the employees to identify pitfalls within the organizational structure and business activities. The employees respond to the identified problems by coming up with innovative ideas. Automation and digitization enhance the conception of new business ideas and innovative solutions to existing business problems.
Impact of AI integration Among Businesses on Product Output or Service Quality
Product output or service quality is essential in attracting a broad consumer base. Therefore, enhanced product or service quality contributes to business success and helps establish an excellent brand reputation in the market (Ozkan et al., 2019). For instance, companies like Samsung Electronics and Apple Inc. have a broad and loyal consumer base due to their product output quality (Almeida et al., 2020). Meanwhile, a company’s quality services involve timely and sufficient assistance if a client needs more information on a given product or service (Li et al., 2021). AI integration is crucial in improving service and product output quality since it allows defection detection, quality and accurate processes, and timely response on the company’s supply chain.
AI systems are complex and combine different programming languages involving learning and getting solutions. The systems are enhanced through automated and complicated natural and machine languages that can collect information from the environment and past processes (Al Aani et al., 2019). After that, the engines analyze the collected data to make accurate decisions (Li et al., 2021). Furthermore, AI-based predictors can learn patterns of defect-proneness from the initial products and utilize the information to detect potential proneness in the new products (Al Aani et al., 2019). Consequently, the system help solves any possible product defect improving its quality.
Quality products are a result of effective production processes and supply chains. Companies utilize AI in many activities, including processing and manufacturing, as discussed earlier in this chapter. The process involves the integration of complex machinery and software during the production stage (Leiner et al., 2021). Furthermore, AI is integrated into the quality control and measurement of hazardous chemicals for human beings (Asha et al., 2022). Automated quality and measurement controls result in the production of quality and authentic products (Leiner et al., 2021). The use of robots in the manufacturing processes has negated defects of human errors (Krugh and Mears, 2018). The AI systems can take data from past projects and utilize the data in the production of quality output with precision (Asha et al., 2022). AI integration is significant in consolidating policies, procedures, plans, and execution of complex production processes.
Timely Response to Customers’ Feedback
Quality service and products involve effective production, processing, and delivery to the customer. Therefore, a company’s supply chain is crucial to developing service quality. While complex systems are involved in manufacturing, processing, and packaging products, practical communication between the company and the client is essential (Freixanet, Rialp, and Churakova, 2020). Effective communication systems integrate complex communication programs enhanced through AI. Some companies utilize e-commerce for marketing activities, while others have specific communication channels for marketing purposes (Gyenge et al., 2020). The e-commerce websites have integrated systems that allow the customers to order, select, and track their ordered products (Ahmed, Srejiith, and Abdullah, 2021).
Furthermore, the systems have inbuilt systems that allow the customers to give feedback on the product and make a return if unsatisfied (Ersoz and Merdin, 2022). AI systems collaborate the various communication channels, including messaging, payment, and geolocation systems that allow smooth online transactions (Ahmed, Srejiith, and Abdullah, 2021). Alibaba, one of the study’s research samples, has an integrated communication system that allows effective online transactions. Therefore, quality products or services involve effective production processes and efficient communication channels between the company and clients.
Integration of AI in Management Activities During the Emergence of Unprecedented Pandemics
Business management can be complex during the onset of unprecedented natural pandemics like Covid-19. The pandemics present difficulties in job assignments and communicate with the clients who seek information from the companies’ premises (Kaushik and Guleria, 2020). The pertinent issue during a natural pandemic is business communication (Ikram et al., 2020). The communication channels involve getting in touch with different company stakeholders, including the employees and clients. The studied companies presented fascinating financial results during the Cobvid-19 period. The rapid profitability and increased annual sales were attributed to AI integration within the communication systems. AI enhances survival during unprecedented pandemics by ensuring effective communication systems, forecasting, and effective operations management.
Effective Communication Systems
Effective communication is crucial during unprecedented pandemics since the management must communicate with different stakeholders to ensure that everything is smoothly running. For instance, during the beginning of Covid-19, many companies were locked down, and everything was done virtually (Hacker et al., 2020). Companies like Alibaba, Baidu, and JD. Com developed communication systems that allowed the management to communicate with the different stakeholders. Furthermore, the companies had established virtual customer care desks that allowed the clients to lodge complaints and get real-time assistance (Adam, Weinsel, and Benlian, 2021). The communication system enabled virtual meetings and job allocation within the organization. AI integration played a crucial role in moderating, managing, and enhancing communication.
AI systems have integrated complex programming languages that allow companies to analyze data and predict future trends. Alibaba has installed a forecasting system that analyses the company’s performance and predicts future performance during unprecedented pandemics (Chen and Biswas, 2021). The systems consolidate the company’s financial performance, consumer base, and supply chain and gives a probable impact on the company in the event of unprecedented pandemics. Furthermore, AI integrations allow companies to make earlier preparations in emergencies. For instance, some forecasting systems advice companies to regulate the amount of production based on global financial performance (Osadchy et al.,2018). Furthermore, the companies utilize the profits and losses data to control their recruitment processes and acquire the necessary tools to prepare for emergencies (Paul and Chowdhury, 2020). Companies with integrated AI systems remain afloat since they use forecasting systems to prepare for unprecedented events like Covid-19.
Effective Operations Management
Business operations management involves assigning tasks, giving appraisals, and motivating the employees. Natural pandemics may encumber the operations management activities since the supervisors do not interact with their employees physically (Chaker et al.,2021). Furthermore, virtual management is saddled with monitoring problems due to the absence of physical touch between the supervisors and employees (Ceccio, Meroni, and Plebani, 2021). Technological innovations lead to advanced operations management systems that allow virtual supervision (Kumar, Mookerjee, and Shubham, 2018). The systems have integrated communication system and advanced features that helps virtual but real-time job supervision (Chaker et al.,2021). Furthermore, the systems have incorporated software that allows employee appraisal and report generation (Ceccio, Meroni, and Plebani, 2021). Operations management systems are significant during the onset of unprecedented natural pandemics.
Advantages of AI Integration Among the Major Firms’ Departments
The study involved respondents working in the various company departments and heads of the departments. The finance department is the company’s backbone since it takes part in resource acquisition and allocation among its stakeholders (Butzbach, 2022). Some of the activities done in the department include consolidation of the company’s financial data, paying for the services offered to the company, and acquiring resources, among others. AI systems are significant to the department since they enhance accurate financial reporting and share the same information with different stakeholders. The systems allow the department to allocate salaries as communicated by the human resource departments (Flores, Xu, and Lu, 2020). Furthermore, the AI systems will enable the finance department to predict the company’s future performance and make accurate financial expenditures in preparation for future developments (Butzbach, 2022). Therefore, the finance department benefits from the AI system through accurate financial reporting and economic preparation in the wake of inadvertent pandemics.
Human Resource Department
The human resource department is the largest of all departments, and it involves recruitment, appraisal, and employee motivation activities. The department’s action determines the company’s success since it is involved in recruiting effective and competent employees. The department utilizes AI systems in the recruitment processes, employee appraisal, and motivation activities (Jia et al., 2018). The systems have consolidated information about potential employees and analyzed the information to select competitive employees. Furthermore, the departments benefit from the scenario by analyzing market trends and industrial issues. The collected information can predict the company’s dynamics and regulate human resource activities like employee recruitment (Black and van Esch, 2020). The AI systems are beneficial to the human resource departments since they enable easy recruitment and future preparations.
The marketing department benefits from AI integration through digital marketing and consumer data analysis. The department utilizes AI-enabled systems that enable efficient marketing activities and effective consumer sourcing (Campbell et al., 2020). Through the systems, the marketers send product or service information to the potential consumer and track the performance of the marketing activity by analyzing adverts (Ali, 2021). Many social media platforms have automated messaging features that enable the company effectively interact with consumers. Digitization and automation have revolutionized marketing activities, and improved the quality of adverts and information shared with potential consumers.
Information Technology (IT) Department
IT department is the most technically revolutionized department among the firms. The department is responsible for monitoring IT systems and giving technical support to a firm. Given the technicality of the activities done in the department, AI systems are crucial to help solve complicated processes (Singh et al., 2020). AI-enabled systems help companies detect security flaws, technical hitches, and employee misuse of the company systems. Through AI, the department’s tasks are eased and fastened. For instance, an AI-enabled system self-detects viruses and any system malfunction. After that, the system can solve the problem or cue the necessary authority for an immediate response (Singh et al., 2020). Furthermore, unlike traditional computers, AI-enabled computers have self-paced and installed tutorials that guide the users. Consequently, the IT officers are relieved from familiarizing the employees with the computer systems and only attend to crucial issues. Like many other company departments, the IT department efficiently executes its tasks through AI integration.
The researcher identified various problems associated with using AI in business activities during the study. Many employees who took part in the research embraced AI use and were confident in the system outcomes. However, most of the interviewed employees expressed their fear of losing their jobs. For instance, one of the interviewed managers said that she was reluctant to embrace AI use during the introduction at their company since he was certain that her job would be overtaken. Furthermore, the respondents alluded to the AI system’s use of apathy to the complexity and technicality of the same. Meanwhile, when asked about the ethical concerns about AI usage, most of the respondents did not know about the system use’ policy adopted at the organizational level. Therefore, companies can adopt several recommendations to improve AI use among employees.
Development of AI System Use Policy
The AI system used among companies involved mobile applications and desktop-based software at company levels. For instance, Alibaba allows consumers to download a mobile application software through which they can place an order and track their ordered products. However, many users, including the employees, were unaware of the company’s system use policy. Consequently, ethical concerns about data sharing and manipulation are probable. The companies can develop comprehensive system use manuals that the employees and clients can read before using the system. The companies can integrate an interactive ‘system use policy’ test to ensure that the users understand the implications of misusing the AI system. System Use Policy is essential since it ensures that any system user makes efficient decisions.
Integration of User-Tutorials
Increasing AI uses apathy among employees and clients can be alluded to the technicality of the systems. Although companies like Baidu and JD.com had integrated user tutorials, some companies did not include the tutorials to enable the users to understand the system use. Integration of user-friendly tutorials would help the companies increase the number of AI system users and positive embracement. The tutorials could be integrated with the AI system before use and immediately after system installation. Furthermore, the companies can engage their clients through online training on using their AI systems. Frequently training accompanied by certification among the employees could encourage them to embrace the systems. User tutorials, employee training, and motivation by certification are great ways of encouraging AI system use.
Offering Technical Training to the Employees
Employees interact with the AI system daily but can be affected in the event of system entropy. Many respondents had developed a negative attitude towards the AI systems since their jobs were at risk. With increasing AI use among companies, employees without technical skills are dismissed and replaced with those with necessary skills. Consequently, the companies’ ethical culture is at risk of deterioration. The companies can accommodate employees without technical skills by subjecting them to training and offering academic support like educational loans for technical degrees. Employees are crucial for a company’s ethical culture and can be retained by being provided training by the company.
Suggestions for Future Research
This study involved selected companies in China and individuals working in various Chinese companies. Although the research sheds light on AI use among Chinese companies, it lacks a global perspective. Therefore, future research should involve companies outside China and from developing countries. Furthermore, the study was limited to financial and time constraints. Thus, the collected data may have lacked accuracy giving biased information. Future researchers on the overarching thematic area should allow sufficient time and have the financial muscle to enhance data collection accuracy. Therefore, future research should provide a global perspective on the topic, conduct the research over sufficient time, and acquire enough funds to carry out the research.
AI use among companies is beneficial since it ensures efficient processes in different firms’ departments. This chapter summarized the study’s findings and offered an in-depth content analysis of the results. According to the chapter, AI has impacted business management activities like encouraging employee motivation, restructuring companies’ management, and improving product output quality. Therefore, AI integration is beneficial to companies and should be embraced. However, AI use apathy is an area of concern among companies. Organizations can adopt different mechanisms like user-tutorial integration and employee technical training. Since the research was limited to time and finances, future research should have enough resources to enhance data quality.
Adam, M., Wessel, M. and Benlian, A., 2021. AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), pp.427-445. Web.
Aiello, C.D., Awschalom, D.D., Bernien, H., Brower, T., Brown, K.R., Brun, T.A., Caram, J.R., Chitambar, E., Di Felice, R., Edmonds, K.M. and Fox, M.F., 2021. Achieving a quantum smart workforce. Quantum Science and Technology, 6(3), p.030501. Web.
Al Aani, S., Bonny, T., Hasan, S.W. and Hilal, N., 2019. Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination? Desalination, 458, pp.84-96. Web.
Ali, B.J., 2021. Assessing (The impact) of advertisement on customer decision making: Evidence from an educational institution. Ali, BJ (2021). Assessing (The impact) of advertisement on customer decision making: Evidence from an educational institution. Afak for Science Journal, 6(01), pp.267-280. Web.
Almeida, M., Sousa, E., Rodrigues, C., Candeias, M.B. and Au-Yong-Oliveira, M., 2021. Samsung vs. Apple: How Different Communication Strategies Affect Consumers in Portugal. Administrative Sciences, 11(1), p.19. Web.
Asha, P., Natrayan, L., Geetha, B.T., Beulah, J.R., Sumathy, R., Varalakshmi, G. and Neelakandan, S., 2022. IoT enabled environmental toxicology for air pollution monitoring using AI techniques. Environmental research, 205, p.112574. Web.
Black, J.S. and van Esch, P., 2020. AI-enabled recruiting: What is it and how should a manager use it?. Business Horizons, 63(2), pp.215-226. Web.
Butzbach, O., 2022. The elusive nature of shareholders’ claims over the corporation, or the strange non-death of shareholder primacy. In The corporation: Rethinking the iconic form of business organization. Emerald Publishing Limited. Web.
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.
Canhoto, A.I. and Clear, F., 2020. Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential. Business Horizons, 63(2), pp.183-193. Web.
Ciccio, C.D., Meroni, G. and Plebani, P., 2020. Business process monitoring on blockchains: Potentials and challenges. In Enterprise, Business-Process and Information Systems Modeling (pp. 36-51). Springer, Cham.
Chaker, N.N., Nowlin, E.L., Walker, D. and Anaza, N.A., 2021. Alone on an island: A mixed-methods investigation of salesperson social isolation in general and in times of a pandemic. Industrial Marketing Management, 96, pp.268-286. Web.
Colli, M., Madsen, O., Berger, U., Møller, C., Wæhrens, B.V. and Bockholt, M., 2018. Contextualizing the outcome of a maturity assessment for Industry 4.0. Ifac-papersonline, 51(11), pp.1347-1352. Web.
Chen, Y. and Biswas, M.I., 2021. Turning Crisis into Opportunities: How a Firm Can Enrich Its Business Operations Using Artificial Intelligence and Big Data during COVID-19. Sustainability, 13(22), p.12656. Web.
Ersöz, F. and Merdin, D., 2022. Data Mining Approach in Repair and Service Systems of Electronic Products Under Warranty. In Big Data and Blockchain for Service Operations Management (pp. 209-238). Springer, Cham. Web.
Fernando, Y. and Wulansari, P., 2020. Perceived understanding of supply chain integration, communication and teamwork competency in the global manufacturing companies. European Journal of Management and Business Economics. Web.
Freixanet, J., Rialp, A. and Churakova, I., 2020. How do innovation, internationalization, and organizational learning interact and co-evolve in small firms? a complex systems approach. Journal of Small Business Management, 58(5), pp.1030-1063. Web.
Fu, F., Purvis-Roberts, K.L. and Williams, B., 2020. Impact of the COVID-19 pandemic lockdown on air pollution in 20 major cities around the world. Atmosphere, 11(11), p.1189. Web.
Grover, V., Chiang, R.H., Liang, T.P. and Zhang, D., 2018. Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), pp.388-423. Web.
Gyenge, B., Máté, Z., Vida, I., Bilan, Y. and Vasa, L., 2021. A new strategic marketing management model for the specificities of E-commerce in the supply chain. Journal of Theoretical and Applied Electronic Commerce Research, 16(4), pp.1136-1149. Web.
Hacker, J., vom Brocke, J., Handali, J., Otto, M. and Schneider, J., 2020. Virtually in this together–how web-conferencing systems enabled a new virtual togetherness during the COVID-19 crisis. European Journal of Information Systems, 29(5), pp.563-584. Web.
Hoyer, W.D., Kroschke, M., Schmitt, B., Kraume, K. and Shankar, V., 2020. Transforming the customer experience through new technologies. Journal of Interactive Marketing, 51, pp.57-71. Web.
Huang, M.H. and Rust, RT, 2018. Artificial intelligence in service. Journal of Service Research, 21(2), pp.155-172. Web.
Ikram, M., Zhang, Q., Sroufe, R. and Ferasso, M., 2020. The social dimensions of corporate sustainability: an integrative framework including COVID-19 insights. Sustainability, 12(20), p.8747. Web.
Kaushik, M. and Guleria, N., 2020. The impact of pandemic COVID-19 in workplace. European Journal of Business and Management, 12(15), pp.1-10.
Kingsnorth, S., 2019. Digital marketing strategy: an integrated approach to online marketing. Kogan Page Publishers.
Krugh, M. and Mears, L., 2018. A complementary cyber-human systems framework for industry 4.0 cyber-physical systems. Manufacturing letters, 15, pp.89-92. Web.
Kumar, S., Mookerjee, V. and Shubham, A., 2018. Research in operations management and information systems interface. Production and Operations Management, 27(11), pp.1893-1905. Web.
Leung, K.H., Lee, C.K. and Choy, K.L., 2020. An integrated online pick-to-sort order batching approach for managing frequent arrivals of B2B e-commerce orders under both fixed and variable time-window batching. Advanced Engineering Informatics, 45, p.101125. Web.
Leiner, T., Bennink, E., Mol, C.P., Kuijf, H.J. and Veldhuis, W.B., 2021. Bringing AI to the clinic: blueprint for a vendor-neutral AI deployment infrastructure. Insights into Imaging, 12(1), pp.1-11. Web.
Li, C.Z., Chen, Z., Xue, F., Kong, X.T., Xiao, B., Lai, X. and Zhao, Y., 2021. A blockchain-and IoT-based smart product-service system for the sustainability of prefabricated housing construction. Journal of Cleaner Production, 286, p.125391. Web.
Lu, V.N., Wirtz, J., Kunz, W.H., Paluch, S., Gruber, T., Martins, A. and Patterson, P.G., 2020. Service robots, customers and service employees: what can we learn from the academic literature and where are the gaps?. Journal of Service Theory and Practice. 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.
Michaelis, J.E., Siebert-Evenstone, A., Shaffer, D.W. and Mutlu, B., 2020. Collaborative or simply uncaged? understanding human-cobot interactions in automation. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-12). Web.
Nam, K., Dutt, C.S., Chathoth, P., Daghfous, A. and Khan, M.S., 2021. The adoption of artificial intelligence and robotics in the hotel industry: prospects and challenges. Electronic Markets, 31(3), pp.553-574. Web.
Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, B.E., Kazancoglu, Y. and Narwane, V., 2021. Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. The International Journal of Logistics Management. Web.
Nhamo, G., Dube, K. and Chikodzi, D., 2020. Impact of COVID-19 on the Global Network of Airports. In Counting the Cost of COVID-19 on the Global Tourism Industry (pp. 109-133). Springer, Cham. Web.
Okuda, T. and Shoda, S., 2018. AI-based chatbot service for financial industry. Fujitsu Scientific and Technical Journal, 54(2), pp.4-8. Web.
Osadchy, E.A., Akhmetshin, E.M., Amirova, E.F., Bochkareva, T.N., Gazizyanova, Y. and Yumashev, AV, 2018. Financial statements of a company as an information base for decision-making in a transforming economy. Web.
Jia, Q., Guo, Y., Li, R., Li, Y. and Chen, Y., 2018. A conceptual artificial intelligence application framework in human resource management. In Proceedings of the international conference on electronic business (pp. 106-114). Web.
Ahmed, F.Y., Sreejith, R. and Abdullah, M.I., 2021. Enhancement of E-Commerce Database System During the COVID-19 Pandemic. In 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) (pp. 174-179). IEEE. Web.
Flores, E., Xu, X. and Lu, Y., 2020. Human Capital 4.0: a workforce competence typology for Industry 4.0. Journal of Manufacturing Technology Management.
Otto, M.C., Hoefsmit, N., Van Ruysseveldt, J. and van Dam, K., 2019. Exploring proactive behaviors of employees in the prevention of burnout. International journal of environmental research and public health, 16(20), p.3849. Web.
Özkan, P., Süer, S., Keser, İ.K. and Kocakoç, İ.D., 2019. The effect of service quality and customer satisfaction on customer loyalty: The mediation of perceived value of services, corporate image, and corporate reputation. International Journal of Bank Marketing. Web.
Paul, S.K. and Chowdhury, P., 2020. Strategies for managing the impacts of disruptions during COVID-19: an example of toilet paper. Global Journal of Flexible Systems Management, 21(3), pp.283-293. Web.
Paschen, J., Wilson, M. and Ferreira, J.J., 2020. Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel. Business Horizons, 63(3), pp.403-414. Web.
Patricio, R., Moreira, A., Zurlo, F. and Melazzini, M., 2020. Co‐creation of new solutions through gamification: A collaborative innovation practice. Creativity and Innovation Management, 29(1), pp.146-160. Web.
Rabah, K., 2018. Convergence of AI, IoT, big data and blockchain: a review. The lake institute Journal, 1(1), pp.1-18. Web.
Rana, G. and Sharma, R., 2019. Emerging human resource management practices in Industry 4.0. Strategic HR Review. Web.
Reim, W., Åström, J. and Eriksson, O., 2020. Implementation of artificial intelligence (AI): a roadmap for business model innovation. AI, 1(2), pp.180-191. Web.
Rosenbusch, N., Gusenbauer, M., Hatak, I., Fink, M. and Meyer, K.E., 2019. Innovation offshoring, institutional context and innovation performance: A meta‐analysis. Journal of management studies, 56(1), pp.203-233. Web.
Scarpellini, S., 2021. Social indicators for businesses’ circular economy: Multi-faceted analysis of employment as an indicator for sustainability reporting. European Journal of Social Impact and Circular Economy, 2(1), pp.17-44. Web.
Shafi, M., Liu, J. and Ren, W., 2020. Impact of COVID-19 pandemic on micro, small, and medium-sized Enterprises operating in Pakistan. Research in Globalization, 2, p.100018. Web.
Singh, A.V., Ansari, M.H.D., Rosenkranz, D., Maharjan, R.S., Kriegel, F.L., Gandhi, K., Kanase, A., Singh, R., Laux, P. and Luch, A., 2020. Artificial intelligence and machine learning in computational nanotoxicology: unlocking and empowering nanomedicine. Advanced Healthcare Materials, 9(17), p.1901862. Web.
Stone, R.J., Cox, A. and Gavin, M., 2020. Human resource management. John Wiley & Sons.
Thrall, J.H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K. and Brink, J., 2018. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology, 15(3), pp.504-508. Web.
Yu, K.H., Zhang, Y., Li, D., Montenegro-Marin, C.E. and Kumar, P.M., 2021. Environmental planning based on reduce, reuse, recycle and recover using artificial intelligence. Environmental Impact Assessment Review, 86, p.106492. Web.
Zhang, Y., Xiong, F., Xie, Y., Fan, X. and Gu, H., 2020. The impact of artificial intelligence and blockchain on the accounting profession. Ieee Access, 8, pp.110461-110477. Web.