Elsevier

Computers in Human Behavior

Volume 55, Part B, February 2016, Pages 1117-1124
Computers in Human Behavior

Full length article
Examination of the factors that influence the technological adoption intentions of tomorrow’s new media producers: A longitudinal exploration

https://doi.org/10.1016/j.chb.2014.09.040Get rights and content

Highlights

  • Technological adoption intentions of college students were examined longitudinally.

  • Results indicated social influence plays an increasingly strong role of the adoption process.

  • Utilitarian factors accounted for the bulk of the explained variance in adoption intentions.

  • Hypothesized model accounted for 42% and 58% (respectively) of variance in adoption intentions.

Abstract

In a world awash with digital media, employers in mass communication professions are increasingly searching for and hiring employees with both traditional and new media production skills. As such, post-secondary institutions have, en masse, begun to incorporate instruction on multimedia production into their curricula. Despite this widespread integration of new media into coursework, administrators, instructors, and students are still searching for best practices as they relate to efficient and effective delivery of instruction. In light of such needs, this study used the technological acceptance model and structural equation modeling to explore, on a longitudinal basis, the psychological factors that influence mass communication students’ adoption of new media production technologies. Our results demonstrated that subjective, normative influences play an increasingly powerful role in student adoption decisions over time. Furthermore, the data indicated that usefulness perceptions were the strongest predictor of student decisions to adopt new media production technologies.

Introduction

Employers in professional mass communication fields have increasingly identified a need for employees with media production skills in the areas of audio, video, and photography. In an effort to address this need, many post-secondary mass communication schools have made financially burdensome changes to their curricula to better ensure that graduates have competencies in the production and distribution of “new media” content (e.g., Abram, 2009, Atkinson, 2008; Claussen, 2012; Larsen & Len-Rios, 2006; Lewis, 2010, Lowrey et al., 2005, Marron, 2013, Stewart, 2007). Despite these rapid changes to student coursework, those charged with curricular design have yet to identify “best practices” as they relate to effective instruction of new media skills (Claussen, 2012, Marron, 2013). According to Marron (2013), mass communication educators are currently coping with both the ongoing emergence of revolutionary digital technologies and a professional world that remains in a seemingly perpetual state of change.

Further complicating matters is a relative lack of empirical research on the social and psychological factors that influence mass communication students’ adoption of emergent production technologies. Although technological adoption has been studied widely in occupational contexts (e.g., Schepers and Wetzels, 2007, Wu and Lederer, 2009), researchers have seldom sought to explore user acceptance factors within post-secondary environments. And, with a few exceptions (e.g., Venkatesh and Davis, 2000, Venkatesh and Morris, 2000), research on technological adoption has been cross-sectional in nature. Given the foregoing, the purpose of this study was to examine post-secondary students’ adoption of new media production technologies from a longitudinal perspective. Gaining a better understanding of the social and psychological dynamics that underlie technological adoption on the part of post-secondary students will aid post-secondary educators currently in search of finding the most effective means of delivering educational content to tomorrow’s professional media producers.

Section snippets

The technological acceptance model

The technological acceptance model (TAM) is one of the most “parsimonious and robust” (Yang, 2007, p. 34) theoretical frameworks used to understand technological adoption. According to Venkatesh and Davis (2000), empirical studies employing TAM have consistently explained upwards of 40% of variance in technology usage intentions. TAM is predicated upon the idea that user acceptance plays a crucial role in determining the overall success of organizational technological initiatives (Davis, 1989).

Method

This study was conducted using students taking coursework at a large university in the western United States. All participants were enrolled in a three course, 12-credit hour sequence that is a core introductory requirement for all journalism, advertising, public relations, and communications majors in the university’s School of Journalism and Communication. The class serves as a prerequisite for subsequent in-major media production classes. Approximately 400 students pass through the sequence

The measurement model(s)

The current data were analyzed using structural equation modeling (SEM; Amos v.18). Before hypotheses testing, series of confirmatory factor analyses (CFA) were performed. First, data from Time 1 and Time 2 were examined separately. In each case, the fit statistics indicated that the data adequately fit the model. In the case of Time 1, the model fit statistics were χ 2  = 154.018, DF  = 94, χ 2/DF  = 1.638, p  < .01; CFI = .978; RMSEA = .0451 (90% CI  = .036, .065); SRMR = .047. In the case of Time 2, the

Discussion

Using the TAM framework, we analyzed survey data, collected at both the beginning and the end of the term, from students currently enrolled in an introductory media production course at a large university in the United States. We believe that the findings resultant of this study have a number of implications both for practice and theory.

First, this study found that, over time, social influence in the form of subjective norms played an increasingly strong role in the decision to adopt the

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