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Top1. Introduction
In today’s hyper-connected global economy, the rapid evolution of technology is transforming organizations across continents at an unprecedented pace. Yet, while technological advancements are shaping markets and industries worldwide, the study of information systems (IS) has remained largely static (O’Connor et al., 2024). Most IS research still relies on isolated snapshots of organizations, neglecting the critical role that time plays in the adoption, adaptation, and transformation of technology over time (Sarker & Sahay, 2004). However, organizations from Tokyo to New York to Johannesburg are continuously adapting to evolving digital landscapes, requiring a more nuanced understanding of how IS develop across different temporal and cultural dimensions. Addressing this gap is essential, as temporal factors influence decision-making, organizational adaptation, and the long-term sustainability of IS initiatives. Understanding these dynamics can help both researchers and practitioners develop more effective strategies for managing technological change and ensuring the long-term success of IS implementations across diverse regions.
An often overlooked factor in IS research is the interplay between methodological approaches and time perception. Longitudinal studies have the potential to provide deep insights into the evolution of IS by capturing changes in technological adoption, user behavior, and organizational transformation over extended periods. However, the way time is conceptualized in IS studies remains inconsistent. Existing research often conflates two fundamentally different notions of time: Kairos (social, qualitative time) and Chronos (mechanical, quantitative time). This study aims to clarify and integrate these perspectives, highlighting how shifts in time perception influence research design, data interpretation, and methodological choices in longitudinal IS research. Recognizing the distinction between these temporal perspectives is essential to refining IS research methodologies and better capturing the complexity of technological evolution.
The importance of time perception in IS research has become even more evident in the context of global disruptions, such as the COVID-19 pandemic, which accelerated digital transformation across industries and regions (Ågerfalk et al., 2020; Chong et al., 2011; O’Connor et al., 2023; 2024). A global perspective on IS temporality provides a more comprehensive understanding of technology adoption patterns, offering valuable insights for multinational organizations and policymakers. However, as O’Connor et al. (2023) highlight, existing literature on longitudinal IS research remains fragmented and methodologically diverse. Theoretical models used in prior studies frequently fail to account for the evolving nature of global technological and organizational contexts (Saunders & Kim, 2007). Although previous research has explored various methodologies, there is no comprehensive framework that fully integrates the methodological and temporal complexities of IS evolution. This study builds upon recent contributions, particularly the systematic reviews of temporality in Information Systems Development (ISD) projects (O’Connor et al., 2023, 2024), and seeks to address the remaining gaps by explicitly linking methodological approaches to time perception in IS research.
A key challenge in IS research is the misconception between time zones and conceptual time perceptions. While geographical time zones are critical for practical coordination across global organizations, they should not be conflated with temporal perception frameworks (Kairos vs. Chronos). This study does not focus on logistical issues related to global time synchronization but rather examines the conceptualization of time in IS research—an area that has been largely overlooked. To bridge this gap, this study investigates the following questions: How do different methodological approaches influence the perception and integration of time in IS research? To what extent does the distinction between Kairos and Chronos impact the analysis of IS evolution over time? What frameworks can be developed to better integrate temporal considerations into longitudinal IS research?