Understanding the S-Curve of Ambidextrous Behavior in Learning Emerging Digital Technologies

Abstract

Emerging digital technologies such as Internet of Things, artificial intelligence and machine learning, data analytics, and cloud technologies have been evolving rapidly and impacting the labor market. As new technologies proliferate, the availability of talent in these skills becomes a massive challenge for firms involved in digital transformation. As a result, firms go for an ambidextrous strategy via continuous in-house talent development for upcoming needs while using existing talent for ongoing projects. The success of a firm's ambidextrous strategy depends on the ambidextrous behavior of employees in terms of learning aspirations and actual learning behavior. This study is designed to analyze the employee's learning aspiration and reskilling resistance using the S-Curve model. Influencing factors are identified, and their influence on employee's ambidextrous behavior is analyzed. A case study of a multinational corporation is used to identify influencing factors involving focus group discussions, surveys, interviews, and ERP data. Subsequently, a nonlinear relationship is identified between employees' reskilling resistance and work experience in an S-Curve form. Further, it explains the moderation effect of the influencing factors on the exploration and exploitation behavior along the S-Curve.

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