A Model for Predicting User Intention to Use Voice Recognition Technologies at the Workplace in Saudi Arabia

A Model for Predicting User Intention to Use Voice Recognition Technologies at the Workplace in Saudi Arabia

Khalid Majrashi (Department of Information Technology, Institute of Public Administration, Saudi Arabia)
Copyright: © 2022 |Pages: 18
DOI: 10.4018/IJTHI.2022010107
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Abstract

The use of voice recognition technologies (VRTs) has been expanding, and these are currently used at workplaces. This study tested a model for predicting users’ intention to use VRTs at workplaces. The model extended the technology acceptance model (TAM) and considered four additional factors—perceived privacy, perceived security, perceived trust, and social norms—and four variables—age, education level, gender, and nationality. We validated the model based on responses from 300 employees working in Saudi Arabia. The results indicated a medium level of acceptance and a valid TAM in its original form. Further, perceived privacy and perceived security are significant predictors of perceived trust and perceived trust is an important predictor of attitudes and intention to use VRTs. The social norms variable was a significant predictor of intention to use and accept VRTs. The results also showed that age and education level significantly affect users’ attitudes toward VRT adoption.
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Introduction

Voice recognition technologies (VRTs) are a computer software or hardware device with the ability to decode the human voice. Several terms have been used in the literature to refer to a software or a hardware that uses voice as the primary interface to interact with them. These terms include voice user interfaces, conversational agents, intelligent or virtual personal assistants, interactive voice systems, and conversational interfaces (Corbett & Weber, 2016; Gardner-Bonneau & Blanchard, 2007; Kepuska & Bohouta, 2018; McTear, Callejas, & Griol, 2016; Myers, Furqan, Nebolsky, Caro, & Zhu, 2018; Porcheron, Fischer, Reeves, & Sharples, 2018).

Voice recognition applications have grown significantly and are increasingly used among individuals as part of their daily interaction with technologies (Getsmarter, 2019; Myers et al., 2018). This growth is because of the substantial investment by major companies, such as Amazon, Google, Microsoft, and Apple, in speech recognition technologies (Myers et al., 2018), and the production of advanced voice-activated technologies that support many languages around the world. An example of the products of these companies is the “smart speakers” or the autonomous screenless voice gadgets, such as Alexa by Amazon, Google Home, Cortana by Microsoft, and HomePod by Apple (PWC, 2018).

Many voice-based applications have also been made available to users in many devices, such as desktops/laptops, tablets, smart TVs, smartphones, and smartwatches (PWC, 2018). One category of these applications is the intelligent voice assistants, such as Apple Siri, Google Assistant, and Samsung Bixby Voice (Pew Research Center, 2017; PWC, 2018). Voice interaction has also become a feature in many modern web and mobile applications. For instance, the speech-to-text feature has been embedded into several word processors (e.g., Google Docs and Apple Pages) and translation applications (e.g., Google Translate).

Many VRTs have been adopted in private and government organizations (Simon, 2007). These are used in areas such as human resources, sales, marketing, customer relationship management, education and healthcare (Forbes Technology Council, 2018; Getsmarter, 2019; Simon, 2007). Voice-activated technology can be used in several ways in the workplace—for example, by integrating with business applications and for improving efficiency of search engines, increasing productivity, increasing effectiveness of meetings, enabling easy access to big data, increasing the effectiveness of financial services, and making IT operations smarter and faster (Forbes Technology Council, 2018). Many studies have found that voice recognition systems are efficient, effective, and increase productivity when tested in organizations (Simon, 2007). However, despite all the benefits of VRTs, it has been found that there is still some resistance to accepting them in organizations (Costanzo, 2003).

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