Digital Employee Engagement - Theoretical Perspective

Employee engagement has been defined as the harnessing of organization members’ selves to their work roles; in engagement, people employ and express themselves physically, cognitively, and emotionally during role performances (Kahn, 1990). Employee engagement is associated with outcomes at individual, team, and organization levels (Simon et al, 2021). Employees who are engaged are more satisfied in their jobs and increased performance (Simon et al, 2021).

With the COVID-19 the existing anchor theories of employee engagement have now been under more scrutiny than before, with the adoption of technology (Brian & David, 2021).

The prevalent theories in employee engagement include Social Exchange Theory (SET), Job-demands resources (JD-R), Self-determination Theory (SDT), Job-characteristic model (JCM), Conservation of resources theory (COR), Social Identity Theory (SIT) et al.

Job-characteristic model (JCM) deals with the kind of skills and nature of work assigned to an employee and how it may impact whether an employee is motivated and satisfied at work, or negatively manifest in his absence from work. (J. R. Hackman and G. R. Oldham, 1980)

Social Identity Theory (SIT) strong identification with the organization will drive employee behaviour. (Tajfel, 1979)

Frederick Herzberg’s two factor theory, employees have needs related to motivators that lead to satisfaction and hygiene that reduces dissatisfaction. Both these needs to be met by organization. (Frederick Herzberg, 1959)

CARE Model – Context, Altruistic, Resonance and Enable. Which identifies 15 variables, together, drives employee engagement. (Swaminathan Mani and Mridula Mishra, 2021)

• Employees are positively engaged when they have a learning environment in their organization (Liat Eldor and Itzhak Harpaz, 2016). 
• Knowledge sharing and creativity at the workplace also impact engagement positively. (Liat Eldor and Itzhak Harpaz, 2016). 
• Sustainability is a trend today that organizations are committed to and Artificial Intelligence plays a key role in such initiatives at an individual employee level – emotional well-being – algorithms that can assist employees in improving their productivity and helping them complete tasks with less effort. (Jayden et al, 2018)
• Engaged workforce demonstrates employer loyalty, raised creativity and higher productivity. (Ronald & Georg, 2020)
• Employee resilience can be built by creating a learning organization, hence driving employee engagement. (Parul Malik & Pooja Garg, 2017)
• Resource-Based Theory (RBT) and Predictive HR Analytics (PHRA) has a positive impact in building capability on talent management outcomes. (R. Navodya Gurusinghe, Bhadra J. H. Arachchige & Dushar Dayarathna, 2021)
• Data-driven decision-making mindset in all HR operations builds capabilities in HR professionals, and engagement (Markus Ellmer & Astrid Reichel, 2021)
• Predictive analytics can reshape entirely the HRM decision-making process, helping them engage pro-actively with employees (Alessandro Margherita, 2022)
• Employee engagement is one of the concepts and sources related to technology enablers in HR analytics (Alessandro Margherita, 2022)
• HR analytics can have a positive influence on the level of employee job engagement and workplace motivation (Alessandro Margherita, 2022)
• Machine learning models can help in better predicting churn of employees (disengagement) (Praveen Ranjan Srivastava & Prajwal Eachempati, 2021)

 Artificial Intelligence-based technologies impact employees and organizations across human resource planning, recruitment and selection, training and development, compensation and benefits, and performance management. (Pawan Budhwar, Ashish Malik, M. T. Thedushika De Silva & Praveena Thevisuthan, 2022). Artificial Intelligence-based technologies impact employee-to-customer at work, peer-to-peer and manager-to-team(s). (Pawan Budhwar, Ashish Malik, M. T. Thedushika De Silva & Praveena Thevisuthan, 2022). AI can augment human capacity by delivering routine high-volume analytical tasks. Artificial intelligence has become increasingly common in the workplace, and today recruitment algorithms are available to hire and select applications. (Megan Fritts and Frank Cabrera, 2021). Machine intelligence is now integrated into attrition models to predict employee turnover (Andrew B. Speer, 2021). HRM teams are now using AI-bots to engage and communicate with employees (Ashish Malik , Pawan Budhwar , Charmi Patel & N. R. Srikanth, 2020). And Bots are also deployed alongside call centre agents to boost productivity and employee experience. Machine Learning can process audio and video to judge employee performance accurately and fairly than human managers. (Andy Charlwood and Nigel Guenole, 2021). Machine intelligence drives innovation in HRM by positively influencing the output an organization produces, and drives competitiveness through new service offerings and sustained performance. (Cristina Trocin et al, 2021). Data-driven machine intelligence enables intuition-based HR decision-making and leads to competitive advantage (Jaemin Kim et al, 2021).  

Kahn (1990) refers to employee engagement as a combination of physical, cognitive, and emotional expression in performing their roles. May (2004) extended it to include the application of behaviors. Wellins & Concelman (2005) referred to employee engagement as a mix of commitment, loyalty, productivity, and ownership. Saks (2006) highlighted the aspect of knowledge application to perform. Cha (2007) included recognition within the company and a sense of work value. Soane (2012) mentions work-role focus. Xu (2013) includes the identity of the employee within the organization, their responsibility, and their mental state. Xiao & Duan (2014) speak of employee loyalty and commitment. Liu (2016) of personal harmony. Hewitt Consulting (2001) looks at the tenure of employees within the company. Harter (2002) satisfaction and enthusiasm at work. Zeng & Han (2005) pleasant, proud, encouraging experiences at work.