Category: Finance, Business, Management, Economics and Accounting
ORIGINAL
Fostering employee engagement and knowledge sharing through artificial intelligence
Fomentar el compromiso de los empleados y el intercambio de conocimientos mediante la inteligencia artificial
S. Anisha Estherita1 *, S. Vasantha1 *
1School of Management Studies, Vels Institute of Science, Technology and Advanced Studies, (VISTAS), Pallavaram, Chennai, Tamil Nadu, India.
Cite as: Anisha Estherita S, Vasantha S. Fostering employee engagement and knowledge sharing through artificial intelligence. Salud, Ciencia y Tecnología - Serie de Conferencias. 2024; 3:897. https://doi.org/10.56294/sctconf2024897
Submitted: 05-02-2024 Revised: 24-04-2024 Accepted: 11-06-2024 Published: 12-06-2024
Editor: Dr. William Castillo-González
ABSTRACT
Artificial Intelligence is the field that growing at a rapid pace which involves the development of intelligent machines that perform tasks with the aid of human intelligence. The implementation of Artificial Intelligence has led to significant advancements in various business fields. It has the potential to transform the businesses and improve the process in many ways. Knowledge is the vital asset of any person, while its shared, it becomes an asset for many. Sharing of knowledge involves the exchange of information and expertise among the individuals in an organization. Knowledge sharing can help organizations to identify and the address problems effectively and swiftly. Engaging employee in an organization becomes a vital aspect for organizational productivity and organizational success as well. Once when an employee becomes emotionally attached to their organization, they feel responsible about their work and will work with involvement. Artificial Intelligence has the potential to promote employee engagement and knowledge sharing. Through personalized learning and development opportunities, it fosters employee engagement, whereas through real-time communication and collaboration technologies it facilitates knowledge sharing within the organization. This review article aims at discovering how Artificial Intelligence facilitates sharing of knowledge and engaging employees in the organization by undertaking a secondary method of data collection. This review article’s primary goal is to add to the body of knowledge already available on the subject. The study found that adoption of Artificial Intelligence creates work environments that maximize knowledge sharing and enhances employee engagement.
Keywords: Artificial Intelligence; Employee Engagement; Knowledge Sharing; Technology.
RESUMEN
La Inteligencia Artificial es el campo que crece a un ritmo vertiginoso que implica el desarrollo de máquinas inteligentes que realizan tareas con la ayuda de la inteligencia humana. La implantación de la Inteligencia Artificial ha dado lugar a avances significativos en diversos ámbitos empresariales. Tiene el potencial de transformar los negocios y mejorar el proceso de muchas maneras. El conocimiento es el activo vital de cualquier persona y, cuando se comparte, se convierte en un activo para muchos. Compartir conocimientos implica el intercambio de información y experiencia entre los individuos de una organización. El intercambio de conocimientos puede ayudar a las organizaciones a identificar y abordar los problemas con eficacia y rapidez. Comprometer a los empleados de una organización es un aspecto vital para la productividad y el éxito de la misma. Una vez que un empleado se vincula emocionalmente a su organización, se siente responsable de su trabajo y trabajará con implicación. La Inteligencia Artificial tiene el potencial de promover el compromiso de los empleados y el intercambio de conocimientos. Mediante el aprendizaje personalizado y las oportunidades de desarrollo, fomenta el compromiso de los empleados, mientras que a través de las tecnologías de comunicación y colaboración en tiempo real facilita el intercambio de conocimientos dentro de la organización. Este artículo de revisión pretende descubrir cómo la Inteligencia Artificial facilita el intercambio de conocimientos y el compromiso de los empleados en la organización mediante un método secundario de recopilación de datos. El objetivo principal de este artículo de revisión es enriquecer el conjunto de conocimientos ya disponibles sobre el tema. El estudio concluye que la adopción de la Inteligencia Artificial crea entornos de trabajo que maximizan el intercambio de conocimientos y mejoran el compromiso de los empleados.
Palabras clave: Inteligencia Artificial; Compromiso de los Empleados; Intercambio de Conocimientos; Tecnología.
INTRODUCTION
AI has become a game-changer for businesses in a variety of industries in recent years. It has opened up new possibilities due to its capacity to analyze large datasets, identify patterns, and make predictions. Artificial Intelligence uses technologies and tools to enhance the various aspects of business such as its processes, decision-making and interactions with customers. Artificial Intelligence Technologies have gained greater importance due to its ability to change business processes, promote innovation and boost efficiency in an organization. Artificial Intelligence are transforming the companies processes and procedures and also create new opportunities for growth within an organization.(1,2,3,4,5) Beyond the realm of technology, artificial intelligence is having a profound impact. AI-driven automation is a tool used by organizations to automate repetitive tasks, freeing up staff members to concentrate on jobs of greater value. According to,(6) Artificial Intelligence technologies drive revolutionary change and have an impact on basic aspects of how firms operate. The ability of AI to analyze data gives organizations the power to make wise decisions. It can sort through enormous volumes of data to produce insightful analysis that informs strategic decisions. Artificial intelligence (AI) enables data-driven decision-making at a never-before-seen scale, whether it be for forecasting market trends, spotting operational inefficiencies, or allocating resources optimally. Through artificial intelligence, roles, procedures, and even business models may be made intelligent. In addition to the challenges associated with technological adoption, organizations using artificial intelligence must also consider the need for structural and cultural change.(7,8,9,10,11,12,13) The fundamental notion of creative work habits is changed by the application of artificial intelligence in the workplace.(4,15,16) Employees increasingly collaborate with Artificial Intelligence systems to manage complex problem-solving, conduct data-driven experiments, and generate novel ideas.(17,18,19)
The process of transferring information from one person to another so they can utilize it or apply it further is known as knowledge sharing.(20) Every organization needs knowledge since it is a key resource that may provide the company a competitive edge. Since it is one of the most valuable resources, its production, distribution, and use are becoming more and more crucial. Knowledge-sharing organizations exhibit greater degrees of creativity, agility, and readiness for any new challenge.(9) In the workplace, socialization and internalization play a significant role in encouraging knowledge exchange among employees.(21,22) To maximize the benefits of information sharing, organizations should think about making an investment in it.(52) In order to distinguish oneself apart from competitors, an organization should cultivate a culture of knowledge sharing.(23,24,25,26,27) By learning from the more seasoned and senior staff members in the company, information exchange also benefits the employees.(3) Knowledge sharing is made easier by technology, which is largely responsible for chatbots, digital platforms, and collaborative tools. Information is stored and made easily accessible by intranets, document management systems, and knowledge bases. Good knowledge sharing involves more than just information exchange, it also includes creating an environment where learning and teamwork are valued, which improves performance and ensures an organization’s long-term success.
Not only are engaged workers devoted to their jobs, but they also take great pride in giving their all. Numerous benefits apply to both the employee and the company when they are engaged. Improvements in motivation, commitment, and work satisfaction are all correlated with higher levels of engagement. Employees that are engaged also often perform better, are more productive, and act in more creative ways.(28,29,30) Engagement also benefits organizational objectives including staff retention, profitability, and customer pleasure.(21) Employee engagement has been found to be influenced by several things. Positive organizational cultures that are defined by support, trust, and open communication are conducive to increased levels of engagement. These cultures play a critical role in the workplace.(31,32) Employee engagement is also influenced by leadership styles; leaders that are encouraging and motivating will encourage higher levels of engagement from their staff.(33,34,35,36) Enhanced job design that incorporates autonomy, feedback, and skill utilization possibilities has been associated with increased levels of engagement. Employees who are engaged at work are more innovative, productive, and satisfied with their jobs. Businesses that put a high priority on and make investments in employee engagement typically outperform their rivals and foster a positive work environment.
This research article’s main goal is to examine the complex and dynamic relationship that exists between information sharing within organizations, employee engagement, and artificial intelligence (AI) and its capacity for change. By carefully analyzing this complex relationship, the study aims to both identify the associations that currently exist between knowledge sharing and employee engagement as well as investigate how artificial intelligence (AI), an effective instrument, can improve and facilitate these processes in the context of the contemporary workplace. By doing this, this study hopes to shed light on how AI-driven solutions can completely change how businesses promote employee engagement and facilitate the easy exchange of knowledge within their workforce. Conceptual framework figure 1 shown in below.
Conceptual framework
Figure 1. Conceptual framework
Literature review
Employee Engagement
Employee engagement holds great importance in the fields of organizational psychology and management. It speaks to workers’ emotional investment and active participation in their jobs. Engagement among workers is “the incorporation of organization members’ souls to their work duties, and people employ and express themselves physically, cognitively, and emotionally during job performances”.(27) Dimensions such as work features, leadership, organizational culture, and social support, have an impact on employee engagement. Job autonomy, meaningful work, supervisor support, and chances for skill growth are important antecedents. Numerous favorable effects, including as higher productivity, work happiness, organizational dedication, and enhanced overall performance, have been associated with high levels of employee engagement. On the other hand, dissatisfied workers are more prone to burn out, have greater turnover, and perform worse on the job. Commitment, enthusiasm, and a readiness to go above and beyond what is expected of duty are traits of engaged workers.(5) Research conducted(22) has repeatedly demonstrated that greater levels of employee engagement result in a number of advantageous outcomes, such as enhanced customer satisfaction, less staff turnover, and increased productivity. According to,(17) leaders have a significant impact on how engaged their teams are. Improved communication, transformational leadership, and supportive leadership practices have all been linked to increased employee engagement. Organizational culture plays a key impact in encouraging engagement, according to (38,39,40). An inclusive and happy work environment may greatly increase employee engagement. Higher levels of engagement may be fostered through job design aspects such as autonomy, variety, and skill development, as stated.(20) A thorough analysis of the research on employee engagement is provided by Shuck and Wollard, who also emphasize the significance of HRD in promoting engagement. It is believed that HRD practices facilitate employee growth, communication, and participation in decision-making, hence aiding in the establishment of an engaging culture. Macey and Schneider highlight the significance of creating a precise conceptual framework for engagement research, arguing that engagement is a complex construct that may be interpreted as a cognitive, behavioral, or emotional state. The review(1) offer a conceptual framework that connects organizational culture, leadership, and HR procedures to employee engagement and performance.(41,42,43,44,45) They contend that controlling and assessing engagement should take a more strategic approach as it is a major factor in generating competitive advantage.
Knowledge Sharing
Knowledge is often considered one of an organization’s most valuable resources. Effective information sharing ensures the greatest possible use of this asset.(34) The systematic and voluntary exchange of information inside an organization is referred to as knowledge sharing.(7) This exchange can take place informally via conversations and mentorship, as well as officially through training programmes and papers.(46) An organization’s success depends on its ability to share information as it encourages innovation, adaptability, and problem-solving abilities.(34,47,48) Introduced the SECI concept, emphasizing the necessity for information exchange to foster innovation. The SECI framework places a strong emphasis on how knowledge is created through internalization, combination, externalization, and socialization. The exchange of knowledge depends on information and communication technology. In their study(46) highlighted how digital platforms may transform group learning and information exchange. Organizations that successfully communicate information are more likely to be innovative, flexible, and problem-solving oriented, according to study.(9) Numerous studies have revealed a positive correlation between information sharing and organizational effectiveness. Due to technology tools like social media platforms and information management systems, sharing data has been easier, faster, and more accessible.(49,50,51,52) Culture may also facilitate the sharing of knowledge by creating an environment that values and encourages communication.(35) Lastly, leadership is essential in promoting knowledge sharing because it sets an example and supports information-sharing behaviors.(45) Knowledge exchange is essential to the creation and maintenance of a dynamic, successful company. It makes it possible for businesses to take advantage of the information they have collected to gain a competitive edge and promotes a culture of learning inside the company.(53,54,55)
Artificial Intelligence
Artificial Intelligence (AI) has transformed industries and workplaces, affecting how companies grow and function. Artificial intelligence systems will be able to process massive amounts of data, forecast the future, and automate tasks at a scale never seen before.(10,11) Artificial Intelligence technology has made it possible for organizations to make data-driven decisions and leverage unparalleled processing capacity. Artificial intelligence improves human abilities by providing information, generating novel ideas, and supporting decision-making in addition to automating repetitive tasks.(15) Artificial intelligence (AI) powered devices foster creativity and expedite the generation of ideas.(2) Game-changing improvements might result from AI’s ability to sort through vast amounts of data and provide new insights.(33) Artificial intelligence has an influence that extends beyond job automation. AI-driven technologies encourage creativity and speed up the idea creation process.(2) AI’s capacity to filter through massive information and offer fresh insights has the potential to spark game-changing inventions.(33) The impact of Artificial Intelligence goes beyond simple work automation. Beyond the growth of technology, Artificial Intelligence (AI) has the potential to reorganize organizational structures and processes due to its capacity to manage massive amounts of data, predict outcomes, and automate activities.(8) Artificial intelligence has the potential to optimize several business processes by identifying bottlenecks and offering recommendations for improvements. Artificial intelligence has the potential to impact organizational structures and decision-making processes, as stated.(39) The shift to data-driven decision-making may need alterations to leadership principles and cultural norms.
Employee Engagement and Artificial Intelligence
Regularly performed AI-driven pulse surveys offer real-time information on employee engagement, allowing businesses to act promptly.(31) AI may give managers knowledge on how to increase team member engagement and retention.(22) AI technologies are being used to measure, assess, and enhance employee engagement, resulting in data-driven and better-informed initiatives. By examining feedback, performance reviews, and communication data, artificial intelligence’s natural language processing (NLP) and sentiment analysis may determine employee sentiment and engagement levels.(32) The application of AI to offer individualized learning and development opportunities can greatly increase employee engagement.(19) Floridi(18) emphasizes how AI-driven chatbots may improve assistance and communication by responding to employee questions, offering advice on HR-related questions, and providing information on corporate regulations. AI and employee engagement have a complicated and multidimensional interaction. Job autonomy, perceived organizational support, trust in AI systems, fairness, openness, and explainability are some of the variables that mediate or regulate this relationship. Harter,(22) in their research, found that early intervention and focused retention efforts may be made possible by machine learning models that anticipate which employees are engaged or not engaged to their work. By offering tools and assistance for stress management and upholding work-life balance, AI chatbots also improve employee well-being.(19) AI technologies have the power to completely change how businesses perceive, quantify, and improve employee engagement. AI can be included into HR procedures to increase worker engagement. Study made(16) offer frameworks or models that describe how AI can be utilized to improve various HR processes, including hiring, performance management, remuneration, and career development. AI has the potential to improve staff development and learning. According to some studies, AI-based solutions can offer individualized coaching sessions, training plans, and feedback systems to help staff members advance their knowledge and abilities.(42) Technologies for employee engagement shown in figure 2.
Figure 2. Technologies for employee engagement
Source: https://www.hrexchangenetwork.com/
AI predicts retention, automates help, and provides individualized insights to help businesses create a more satisfied and engaged workforce. Artificial intelligence (AI) has a number of benefits for enhancing employee engagement, according(13), including the ability to identify and address employee complaints in real-time, maintain open lines of communication with staff, and increase productivity and satisfaction. Employers can also use AI to personalize their approach to employee engagement so that it better fits the needs and preferences of each employee. There have also concerns been raised regarding the potential negative effects of AI on employee engagement. Inadequate AI implementation can also lead to a breakdown in management-employee communication and information overload.(6) Open channels of communication with staff, increased productivity and satisfaction, and real-time identification and resolution of employee complaints are just a few benefits that artificial intelligence (AI) offers for enhancing employee engagement. Employers can also use AI to personalize their approach to employee engagement so that it better fits the needs and preferences of each employee.(12)
Knowledge Sharing and Artificial Intelligence
Artificial Intelligence technologies are being used more and more to improve the effectiveness and efficiency of knowledge sharing procedures. According(37) personalization is a crucial component of information sharing systems driven by AI. To offer material that is specifically suited to each user, Artificial Intelligence systems examine their preferences, behaviors, and interests. By guaranteeing that workers receive material that is pertinent to their individual responsibilities and interests, personalization improves knowledge transfer. Artificial Intelligence powered semantic search produces more precise and context-aware search results. Artificial Intelligence systems are able to extract pertinent knowledge assets from a variety of sources and comprehend the purpose underlying a search query. Natural language processing (NLP) enabled chatbots may respond to queries, deliver information, and direct users to relevant resources.(19) Knowledge can be found and shared more easily because of Artificial Intelligence based content management technologies that automatically organize and classify information. By enhancing content accessibility and organization, this feature makes knowledge sharing and discovery easier (37). Benefits of AI for knowledge sharing in shown in figure 3.
Figure 3. Benefits of AI for knowledge sharing
Artificial Intelligence is capable of extracting and summarizing information from unstructured data sources like emails, social media, and papers. This feature makes it easier to gather and distribute standard procedures and insights. Additionally, it can assist in turning implicit information into explicit knowledge that is easier to share within the company.(32) Knowledge exchange procedures may be streamlined by automation powered by Artificial Intelligence. Knowledge management routine chores may be automated with robotic process automation (RPA), freeing up staff members to concentrate on higher-value work. This increases productivity and guarantees that knowledge-sharing programmes are carried out consistently.(47) Recommender systems powered by Artificial Intelligence Systems are important for sharing information. They enhance the discoverability of knowledge assets by analyzing user behavior and preferences to propose relevant information.(23) By customizing content to each user’s requirements and preferences, AI personalization algorithms improve knowledge sharing platforms by increasing user engagement and knowledge absorption.(29) By offering intelligent tools and platforms that improve the efficacy, precision, and efficiency of information transfer procedures, artificial intelligence can promote knowledge sharing.(50) Language, location, and time limits are just a few of the obstacles that artificial intelligence can assist in removing in the way of efficient information sharing.(45) There are advantages and disadvantages of using Artificial Intelligence in knowledge exchange. Some research indicates that Artificial Intelligence can increase the amount and quality of knowledge that is shared,(45) but other studies point out possible drawbacks such job displacement, a loss of autonomy, and privacy issues.(51)
Objective
To do an in-depth literature survey assessing how artificial intelligence enhances employee engagement and knowledge sharing.
METHOD
This study is descriptive in nature. The researcher undertakes a secondary data collection method which analyses and synthesizes the data collected with relation to the variables involved in the study, namely knowledge sharing, employee engagement and artificial intelligence. This further adds to the existing knowledge in this field.(53)
DISCUSSION
The modern workplace is marked by rapidity and increased digitization, making employee engagement and knowledge sharing vital to the success of an organization. Companies are always looking for new and creative ways to maintain employee engagement and encourage knowledge sharing among staff members. Artificial Intelligence (AI) has become an effective tool that can be essential to accomplishing these objectives.(54) This article discovered that, Artificial Intelligence Technologies provide managers with the required valuable insights and data-driven knowledge for the managers to make decisions in regard to their team members. Artificial intelligence (AI) has the ability to examine employee input from a variety of sources, including surveys, performance reviews, and communication channels, in order to spot trends, issues, and opportunities for development. Techniques for natural language processing (NLP) can be used to extract themes and sentiment from feedback. Managers can use this information to address particular problems and make data-driven decisions. This finding was in coincidence with the findings of the study made.(22) Employee engagement and AI in shown in figure 4.
Figure 4. Employee engagement and AI
The study further explored that using Artificial Intelligence will be used to determine employee sentiment and engagement levels, so that if any deviation is there, the employee can be looked after and their engagement levels can be enhanced. Managers can be informed of possible problems or changes in employee engagement levels by using sentiment analysis of communications made by employees on digital platforms.(28) This makes it possible to make prompt interventions and modifications to promote a peaceful and happy workplace. This finding was in relation to the findings of the study made.(32) The study delving deeper into artificial intelligence and knowledge sharing, it was discovered that Artificial intelligence-based content management technologies help knowledge to be found and shared more easily as these technologies automatically organize and classify information. This finding was consistent with the finding of the study made.(37) Eventually, Artificial Intelligence - driven content management systems that automatically organize and classify information have sped up the process of learning and sharing knowledge. Artificial intelligence (AI)-driven content management systems automatically organize and classify information by combining machine learning, natural language processing (NLP), semantic analysis, and user behavior tracking. This improves organizational content’s general accessibility and usability in addition to making knowledge discovery more efficient.
AI has an impact on knowledge sharing and employee engagement in organizations that is both positive and harmful. The use of AI in performance management and decision-making processes can result in job displacement, a loss of autonomy, a lack of transparency and explainability, and other negative outcomes that negatively affect employee engagement. However, AI-based tools can also offer individualized feedback, coaching, and training programmes that improve employee skills and knowledge and lead to higher levels of engagement.(30) Organizations should utilize AI responsibly, transparently, and with respect for the privacy and autonomy of their employees in order to reduce these threats.
Organizations can use artificial intelligence-powered virtual assistants and decision support systems to provide workers greater autonomy and control over their job in order to increase employee engagement through artificial intelligence (AI). With the use of coaching programmes and learning platforms driven by artificial intelligence, they may also provide more chances for growth and learning. Organizations can adopt Artificial Intelligence powered communication tools, which can summaries lengthy documents or emails into shorter formats and automatically translate messages into numerous languages based on the recipient’s preferences, to help staff communicate more effectively. Organizations can deploy powered by artificial intelligence collaboration systems that prioritize assignments according to priority and urgency and recommend possible collaborators based on their experience and knowledge to foster successful employee cooperation.
Hence, this study on the whole found that Artificial Intelligence (AI) has great potential to help organizations succeed in the quickly changing digital landscape by encouraging knowledge sharing and employee engagement. Artificial intelligence (AI) technologies present novel approaches to customizing engagement tactics and streamlining the effective exchange of knowledge in the workplace. AI-enhanced onboarding, chatbots for support, and personalized feedback all improve employee engagement by offering individualized experiences and ongoing assistance. Whereas, implementation of Artificial Intelligence requires establishment of clear Artificial Intelligence ethics and governance guidelines should be established in organizations, employees should be included in discussions regarding the application of Artificial Intelligence, and Artificial Intelligence systems should be routinely audited and evaluated for accountability, fairness, and transparency. The use of Artificial Intelligence for knowledge sharing and employee engagement can be made more ethical and responsible by following ethical Artificial Intelligence practices.(55)
CONCLUSION
In conclusion, job conditions may change if Artificial Intelligence is included into procedures for sharing knowledge and employee engagement. Artificial Intelligence technologies enable smooth knowledge exchange while offering businesses more individualized, effective, and data-driven ways to comprehend, inspire, and assist their employees. This study discovered that Artificial Intelligence improves the strategies to engage employees in a better way and also facilitates comparatively quick and easy knowledge sharing. But at the same time, to build a peaceful and welcoming workplace where Artificial Intelligence enhances rather than compromises the employees’ wellbeing, it is crucial to utilize the Artificial Intelligence in an ethical and proper way.
Artificial intelligence (AI) affects knowledge sharing and employee engagement in organizations in both positive and negative ways. AI-based tools can boost employee skills and knowledge through personalized feedback, coaching, and training programmes and these benefits can lead to higher levels of engagement. However, when Artificial Intelligence is used in performance management and decision-making processes, job displacement, autonomy loss, lack of transparency, and explainability can all have a negative effect on employee engagement. Organizations should utilize Artificial Intelligence Technologies responsibly, transparently, and with respect for the privacy and autonomy of their employees in order to reduce these threats.
REFERENCES
1. Albrecht SL, Bakker AB, Gruman JA, Macey WH, Saks AM. Employee engagement, human resource management practices and competitive advantage: An integrated approach. Journal of organizational effectiveness: People and performance. 2015 Mar 9; 2(1): 7-35.
2. Amabile TM, Conti R, Coon H, Lazenby J, Herron M. Assessing the work environment for creativity. Academy of management journal. 1996 Oct 1; 39(5): 1154-1184.
3. Amayah A. Determinants of knowledge sharing in a public-sector organization. Journal of Knowledge Management. 2013; 17(3): 454–471.
4. Anderson LM, Bateman TS. Individual environmental initiative: Championing natural environmental issues in US business organizations. Academy of Management journal. 2000 Aug 1; 43(4): 548-570.
5. Bakhshi A, Kumar K, Rani E. Organizational justice perceptions as predictor of job satisfaction and organization commitment. International journal of Business and Management. 2009 Sep; 4(9): 145-54.
6. Bakker S, Laaouar N. An exploratory case study on the opportunities of how Artificial Intelligence can contribute to reduce the communication gap and streamline the distribution of resources. 2022.
7. Bali RK, Wickramasinghe N, Lehaney B. Knowledge management primer. Routledge; 2009 Sep 10.
8. Bharadwaj A, El Sawy OA, Pavlou PA, Venkatraman NV. Digital business strategy: toward a next generation of insights. MIS quarterly. 2013 Jun 1: 471-482.
9. Bock GW, Zmud RW, Kim YG, Lee JN. Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS quarterly. 2005 Mar 29(1): 87-111. https://doi.org/10.2307/25148669
10. Brynjolfsson E, McAfee A. The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company; 2014 Jan 20.
11. Campion A, Gasco-Hernandez M, Jankin Mikhaylov S, Esteve M. Overcoming the challenges of collaboratively adopting artificial intelligence in the public sector. Social Science Computer Review. 2022 Apr; 40(2): 462-477.
12. Chen X, Xie H, Zou D, Hwang GJ. Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence. 2020 Jan 1; 1: 100002. https://doi.org/10.1016/j.caeai.2020.100002
13. Chen L, Luo F, Zhu X, Huang X, Liu Y. Inclusive leadership promotes challenge-oriented organizational citizenship behavior through the mediation of work engagement and moderation of organizational innovative atmosphere. Frontiers in Psychology, 2020; 11: 560594.
14. Davenport TH, Westerman G. Why so many high-profile digital transformations fail. Harvard Business Review. 2018 Mar 9; 9(4): 15. https://hbr.org/2018/03/why-so-many-high-profile-digital-transformations-fail.
15. Davenport TH, Harris JG, Jones GL, Lemon KN, Norton D, McCallister MB. The dark side of customer analytics. Harvard business review. 2007; 85(5): 37.
16. Dutta D, Mishra SK, Tyagi D. Augmented employee voice and employee engagement using artificial intelligence-enabled chatbots: a field study. The International Journal of Human Resource Management. 2023 Jul 4; 34(12): 2451-2480.
17. Eisenbeiss SA, Van Knippenberg D, Boerner S. Transformational leadership and team innovation: integrating team climate principles. Journal of applied psychology. 2008 Nov; 93(6): 1438–1446.
18. Floridi L. Faultless responsibility: On the nature and allocation of moral responsibility for distributed moral actions. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2016 Dec 28; 374(2083): 20160112. https://doi.org/10.1098/rsta.2016.0360
19. Gao J, Huang X, Zhang L. Comparative analysis between international research hotspots and national-level policy keywords on artificial intelligence in China from 2009 to 2018. Sustainability, 2019; 11(23): 6574.
20. Hackman JR, Oldham GR. Motivation through the design of work: Test of a theory. Organizational behavior and human performance. 1976 Aug 1; 16(2): 250-279.
21. Hakanen JJ, Schaufeli WB, Ahola K. The Job Demands-Resources model: A three-year cross-lagged study of burnout, depression, commitment, and work engagement. Work & stress. 2008 Jul 1; 22(3): 224-241.
22. Harter JK, Schmidt FL, Hayes TL. Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: a meta-analysis. Journal of applied psychology. 2002 Apr; 87(2): 268-279.
23. He H, Li Y, Harris L, Liu S. Social Media and Employee Voice: The Current Landscape and Future Research Agenda. Human Resource Management Review. 2019; 29(1): 9-24.
24. Henderson JC, Venkatraman H. Strategic alignment: Leveraging information technology for transforming organizations. IBM systems journal. 1999; 38(2.3): 472-484.
25. Ipe M. Knowledge sharing in organizations: A conceptual framework. Human resource development review. 2003 Dec; 2(4): 337-359.
26. Jeon J, Lee S, Choe H. Beyond ChatGPT: A conceptual framework and systematic review of speech-recognition chatbots for language learning. Computers & Education. 2023: 104898.
27. Kahn WA. Psychological conditions of personal engagement and disengagement at work. Academy of management journal. 1990 Dec 1; 33(4): 692-724.
28. Kangasniemi M, Kankanhalli M. Digital transformation: A review and research agenda. Journal of Information Technology. 2019; 34(4): 539-551.
29. Kaur A, Bansal D. Predictive Analysis for Employee Engagement using Machine Learning and Artificial Intelligence. 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2020.
30. Li H, Zhang H, Qi X, Yang R, Huang G. Improved techniques for training adaptive deep networks. In Proceedings of the IEEE/CVF international conference on computer vision. 2019: 1891-1900.
31. Macey WH, Schneider B. The meaning of employee engagement. Industrial and organizational Psychology. 2008 Mar; 1(1): 3-30.
32. Manning CD, Raghavan P, Schütze H. Introduction to information retrieval. Cambridge university press; 2008 Jul 7.
33. Miron-Spektor E, Ingram A, Keller J, Smith WK, Lewis MW. Micro foundations of organizational paradox: The problem is how we think about the problem. Academy of management journal. 2018 Feb; 61(1): 26-45.
34. Nonaka I, Takeuchi H. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press. 1995.
35. Ortiz‐de‐Mandojana N, Bansal P. The long‐term benefits of organizational resilience through sustainable business practices. Strategic management journal. 2016 Aug; 37(8): 1615-1631.
36. Quinn RE. Diagnosing and changing organizational culture: Based on the competing values framework. Jossey-Bass. 2011.
37. Ricci F, Rokach L, Shapira B. Recommender systems: introduction and challenges. Recommender systems handbook. 2015: 1-34.
38. Schaufeli WB, Salanova M, González-Romá V, Bakker AB. The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness studies. 2002 Mar; 3: 71-92.
39. Shi G, Ma Z, Feng J, Zhu F, Bai X, Gui B. The impact of knowledge transfer performance on the artificial intelligence industry innovation network: An empirical study of Chinese firms. PloS one. 2020; 15(5): e0232658.
40. Smith HA, McKeen JD. Instilling a knowledge-sharing culture. Queen’s Centre for Knowledge-Based Enterprises. 2003 Mar; 20(1): 1-7.
41. Sundaresan S, Zhang Z. AI-enabled knowledge sharing and learning: redesigning roles and processes. International journal of organizational analysis. 2022 Jun 17; 30(4): 983-999.
42. Sutanto J, Kankanhalli A, Tan BC. Deriving IT-mediated task coordination portfolios for global virtual teams. IEEE Transactions on Professional Communication. 2011 May 23; 54(2): 133-151.
43. Truss C, Shantz A, Soane E, Alfes K, Delbridge R. Employee engagement, organisational performance and individual well-being: exploring the evidence, developing the theory. The international journal of human resource management. 2013; 24(14): 2657-2669.
44. Tushman ML, Nadler DA. Information processing as an integrating concept in organizational design. Academy of management review. 1978 Jul 1; 3(3): 613-624.
45. Wang X, Zhang X, Cheng Y, Tian F, Chen K, de Pablos PO. Artificial intelligence-enabled knowledge management. The Routledge Companion to Knowledge Management. 2022 May 22: 153-168.
46. Wasko MM, Faraj S. Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly. 2005 Mar; 29(1): 35-57.
47. Willcocks LP, Lacity M, Craig A. The IT function and robotic process automation. 2015.
48. Xanthopoulou D, Bakker AB, Demerouti E, Schaufeli WB. Reciprocal relationships between job resources, personal resources, and work engagement. Journal of Vocational behavior. 2009 Jun 1; 74(3): 235-244.
49. Zapanta T. The impact of AI on Business. Micro Sourcing. 2023. https://www.microsourcing.com/learn/blog/the-impact-of-ai-on-business/
50. Zhang H, Wu C, Zhang Z, Zhu Y, Lin H, Zhang Z, Smola A. Resnest: Split-attention networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 2736-2746.
51. Zhang S, Yao L, Sun A, Tay Y. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR). 2019; 52(1): 1-38.
52. Zheng T. A literature review on knowledge sharing. Open Journal of Social Sciences. 2017 Mar 13; 5(3): 51-58.
53. Prasad Babu P, Vasumathi A. Role of Artificial Intelligence in Project Efficiency Mediating with Perceived Organizational Support in the Indian IT Sector. Indian Journal of Information Sources and Services (IJISS). 2023; 13(2): 39–45.
54. Jyothi V, Tammineni S, Thiyagu TM, Sowndharya R, Arvinth N. A Data Management System for Smart Cities Leveraging Artificial Intelligence Modeling Techniques to Enhance Privacy and Security. Journal of Internet Services and Information Security (JISIS). 2024; 14(1): 37-51.
55. Solikin I, Darmawan D. Impact of Artificial Intelligence in Improving the Effectiveness of Accounting Information Systems. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JOWUA). 2023; 14(2): 82-93.
FINANCING
None.
CONFLICT OF INTEREST
None.
AUTHORSHIP CONTRIBUTION
Conceptualization: S. Anisha Estherita, S. Vasantha.
Research: S. Anisha Estherita, S. Vasantha.
Writing - original draft: S. Anisha Estherita, S. Vasantha.
Writing - revision and editing: S. Anisha Estherita, S. Vasantha.