Learn about multiple regression in SPSS with data on technology adoption and job satisfaction among knowledge workers / Melina Doargajudhur, Geshwaree Huzooree.

Format:

Book

Author/Creator:

Doargajudhur, Melina, author.

Huzooree, Geshwaree, 1985- author.

Language:

English

Subjects (All):

Knowledge workers--Effect of technological innovations on--Research--Methodology.

Knowledge workers.

Regression analysis--Data processing.

Regression analysis.

Physical Description:

1 online resource : illustrations

Place of Publication:

London : SAGE Publications Ltd, 2026.

Summary:

While the broader literature highlights both the opportunities and challenges of digital work, there remains a need for empirical data that allows students and researchers to examine how different aspects of technology-enabled work operate together. In particular, technology adoption is often studied in isolation, even though employees simultaneously experience varying levels of autonomy, motivation, and technology-related pressure (Doargajudhur et al., 2025).This dataset was created to examine the relationships between technology adoption, job autonomy, technology pressure, work motivation, and key work outcomes such as job satisfaction, organizational commitment, and job performance. Data were collected from 200 knowledge workers across multiple industries using a structured questionnaire with seven-point Likert scale responses.The dataset demonstrates how multiple regression analysis in SPSS can be used to understand how different aspects of technology use and work conditions contribute to employees’ satisfaction and performance. The analysis shows that work motivation and job autonomy are strong predictors of both job satisfaction and performance, while technology pressure is negatively associated with these outcomes. Technology adoption alone (IT consumerization) does not significantly increase satisfaction or performance once other factors are considered.Through this dataset, students learn how to compute composite variables, run multiple regression in SPSS, and interpret statistical outputs in a meaningful applied context. Notably, the dataset encourages students to move beyond technical execution by linking statistical results to substantive questions about fairness, well-being, and inclusion in technology-enabled work environments. In doing so, by examining how motivation, autonomy, and technology pressure interact with technology adoption, students can reflect on why some digital work arrangements benefit employees while others create strain or dissatisfaction.The dataset file is accompanied by a teaching guide, a student guide and how-to guide.

Notes:

Description based on XML content.

ISBN:

9781036249274

OCLC:

1594889263

Publisher Number:

T302120