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Developing and Testing a Smartphone Dependency Scale Assessing Addiction Risk

Author

Listed:
  • Donald Amoroso

    (Auburn University, Montgomery, USA)

  • Ricardo Lim

    (Nagoya University of Commerce and Business, Japan)

  • Francisco L. Roman

    (Asian Institute of Management, Philippines)

Abstract

A 2019 Pew study of emerging economies revealed citizen concerns over smartphone use as risky behavior and their ill effects, such as addiction and overdependency, among many factors. In response, the authors developed a smartphone dependency scale (SDS) of factors that contribute to smartphone addiction, particularly for emerging economies like the Philippines. The instrument was developed from previously validated constructs. They propose that social influence, convenience, affective (anxiety), physiological (maladjustment), and cognitive (mindfulness) factors separately drive smartphone dependency. To test the SDS, the authors surveyed 901 Philippine participants. The scales showed excellent internal consistency and reliability and adequate concurrent and criterion-related validities. A confirmatory factor analysis showed that SDS factors had good data fit. This instrument is a first step in (1) exploring why individuals become dependent (addicted) on mobile devices and (2) stimulating further research concerning smartphone dependency for emerging market settings.

Suggested Citation

  • Donald Amoroso & Ricardo Lim & Francisco L. Roman, 2021. "Developing and Testing a Smartphone Dependency Scale Assessing Addiction Risk," International Journal of Risk and Contingency Management (IJRCM), IGI Global, vol. 10(4), pages 14-38, October.
    • Handle: RePEc:igg:jrcm00:v:10:y:2021:i:4:p:14-38
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    Cited by:

    1. Nattakit Yuduang & Ardvin Kester S. Ong & Yogi Tri Prasetyo & Thanatorn Chuenyindee & Poonyawat Kusonwattana & Waranya Limpasart & Thaninrat Sittiwatethanasiri & Ma. Janice J. Gumasing & Josephine D. , 2022. "Factors Influencing the Perceived Effectiveness of COVID-19 Risk Assessment Mobile Application “MorChana” in Thailand: UTAUT2 Approach," IJERPH, MDPI, vol. 19(9), pages 1-19, May.
    2. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Nattakit Yuduang & Reny Nadlifatin & Satria Fadil Persada & Kirstien Paola E. Robas & Thanatorn Chuenyindee & Thapanat Buaphiban, 2022. "Utilization of Random Forest Classifier and Artificial Neural Network for Predicting Factors Influencing the Perceived Usability of COVID-19 Contact Tracing “MorChana” in Thailand," IJERPH, MDPI, vol. 19(13), pages 1-28, June.

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