The configurational effects of task-technology fit, technology-induced engagement and motivation on learning performance during Covid-19 pandemic: An fsQCA approach

Abstract

At the onset of 2020, Covid-19 pandemic began and disrupted teaching and learning activities with substantial implications for resources and operations. Against this backdrop, the configural causal effects of task-technology fit, technology-induced engagement and motivation, gender, and residential location on learning performance are examined. The proposed association was tested with a dyad sample of faculty members and students (n = 16) using fuzzy sets (fsQCA) analysis. Results show that (i) task-technology fit, and technology-induced motivation emerge as necessary conditions for high learning performance; (ii) task-technology fit, technology-induced engagement and motivation are sufficient conditions for high learning performance among female students, (iii) task-technology fit, technology-induced engagement and motivation are sufficient conditions for high learning performance among students living in urban areas and (iv) task-technology fit is a sufficient condition for high learning performance among female students living in rural areas irrespective of technology-induced engagement and motivation. Implications for theory and policy prescriptions are offered for practitioners.

A configural model of analytics capabilities, ambidexterity, co‐opetition, and firm performance in the supply chain context

“…This technique asserts its dominance over PLS‐SEM in four unique ways. One , fsQCA can operate and generate quality results with small samples (Elçi & Abubakar, 2021). Two , fsQCA is grounded in complexity theory concerning examining complex and non‐linear relationships (Fiss, 2011; Kaya et al, 2020).…”

“…An outcome's occurrence or emergence can be dictated by multiple sufficient conditions. The study variables were rescaled and transformed into full membership, cross‐over, and full non‐membership using the following anchors: six, four, and two for seven‐point scales and four, three, and two for five‐point scales based on guidelines from past studies (Elçi & Abubakar, 2021; Fiss, 2011; Shamout, 2020c). Full membership = 1, cross‐over point = 0.5, and full non‐membership = 0 in fuzzy sets.…”

The impact of COVID-19 induced emergency remote instruction on students’ academic performance at an HBCU

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A New Destination on the Palm? The Moderating Effect of Travel Anxiety on Digital Tourism Behavior in Extended UTAUT2 and TTF Models

“…Many leisure and learning interactions were digitalized during the pandemic; thus, consumers have embraced a variety of digital forms ( Wang et al, 2021 ). The TTF model is an important model for studies of the effect of online experiences, such as technology-facilitated student learning activities ( Lçi and Abubakar, 2021 ). However, the characteristics of the two main factors in the TTF model, technologies and tasks, have changed during the past 2 years of the pandemic ( Wu et al, 2021 ).…”