Social drivers of technology adoption and use in the workplace productivity context

Matson, Zannah; Donmez, Birsen; Savan, Beth; Photiadis, David; Farahani, Elham; Dafoe, Joanna

doi:10.1177/1071181312561446

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…in the way people form these perceptions (of ease of use, of utility, of social influences) (Taherdoost, 2018). Indeed, there is an emerging literature that test the TAM and TAM2 model in various countries and show cultural values do indeed influence how people form the perceptions that are pertinent to the TAM (Dutot et al, 2019;Matson et al, 2012;Muk & Chung, 2015). Given the use of emotional AI is global, and its acceptance will be subjected to cultural and socio-political circumstances particular to each country, there is a need to complement the TAM model with other theories that take into account cultural values.…”

Section: Literature Review: the Mindsponge-based Extended Technology ...mentioning

confidence: 99%

The Mindsponge-based extensions of the Technology Acceptance Model: Behavioral determinants of the attitude toward of non-conscious emotional data harvesting

Ho¹,

Nguyen²,

Vuong³

2022

Preprint

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With the rise of emotional AI, citizens are increasingly subjected to the practice of non-conscious emotional data harvesting, i.e., the opaque ways in which their emotions are analyzed, categorized, and responded to by algorithm. The facts that nowadays, AI technologies not only feel but also feed our emotions, often operate silently in the background of our physical and virtual infrastructure without our knowledge, and are quietly moving across cultural and national borders necessitate a more culturally sensitive way of quantitively studying the act of technological acceptance. Thus, this study provides the first attempt to extend the Technological Acceptance Model (TAM) (Davis, 1989) model with insights from the Mindsponge model of information filtering (Vuong and Napier, 2015) and the Bayesian statistical approach. Analyzing a multinational dataset of 1,015 young adults (age 18-27) with this new framework, this study aims to understand behavioral factors determining the attitude toward non-conscious data harvesting by the government and the private sector. First, we find that the data fitting results of mindsponge-based TAM models are distributed more Bayesian weights compared to the purely TAM models in both the public and private cases. The results suggest fertile ground for future studies that further explore the intersection of psychology, culture, and technology. Second, the analyses also indicate that autonomy and self-efficacy seem to solve the so-called privacy-personalization paradox, where people state a strong preference for privacy but are willing to give up their personal data for personalized benefits. Concretely, we find positive correlates of attitude toward non-conscious data harvesting by either governmental or private sectors are the variables that measure the familiarity with the AI technologies, perceived utility of AI technologies, emotional control when engaging with social media discussion, and using social media for public messaging. Finally, this study found an indicator for the lack of trust toward the government’s engagement with non-conscious dataveillance, concurring with the literature. These results carry important implications for governing and ethical living in the age of emotional AI.

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Section: Literature Review: the Mindsponge-based Extended Technology ...mentioning

confidence: 99%

The Mindsponge-based extensions of the Technology Acceptance Model: Behavioral determinants of the attitude toward of non-conscious emotional data harvesting

Ho¹,

Nguyen²,

Vuong³

2022

Preprint

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“…The third research gap that is addressed by this study is related to the missing ecosystem perspective in smart building research. It is suggested by [63] that there are multiple stakeholders that are involved in the smart building industry: the building owners, the building operators, and the building designers that drive building operation and interaction decisions. Evidently, it is argued that the building and real estate industry and business have complex social and technical process [64], in which a wide range of stakeholders have to build and communicate high-quality design spaces [65].…”

Section: Research Gaps Addressed In This Studymentioning

confidence: 99%

Platform-Based Business Models: Insights from an Emerging AI-Enabled Smart Building Ecosystem

Ahokangas

Turunen

et al. 2019

Electronics

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Artificial intelligence (AI) is emerging to become a highly potential enabling technology for smart buildings. However, the development of AI applications quite often follows a traditional, closed, and product-oriented approach. This study aims to introduce the platform model and ecosystem thinking to the development of AI-enabled smart buildings. The study identifies the needs for a user-oriented digital service ecosystem and business model in the smart building sector in Finland, which aimed to facilitate the launch of scalable businesses and an experiential and dynamic business ecosystem. A multi-method, interpretive case study was applied in the focal ecosystem, with the leading real estate and facility management operators in Northern Europe as part of a Finnish national innovation project. Our results propose an extended comprehensive framework of the 5C ecosystemic model (Connection, Content, Computation, Context, and Commerce) and the possible paths of ecosystem players in the domain of smart building and smart built environment, both theoretically and empirically. The platform-oriented business models are missing, yet desired, by the ecosystem actors. The value chain and ecosystem platforms imply the quest for new (platform) models. Finally, our research discusses the need for new value-chain- and ecosystem-oriented AI development and big data platforms in the future.

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“…Operation protocol is based on policy, company standards, and recommended manufacturer maintenance (Colmenar-Santos, de Lober, Borge-Diez, & Castro-Gil, 2013;Elmualim, Valle, & Kwawu, 2012). Stakeholder objectives are the building owner's, operator's, and designers' goals driving building operation interaction decisions (Matson et al, 2012). However, these approaches all create a gap between user tasks and the building input factors -conditions, operation parameters, and user preferences.…”

Section: Industrial and Manufacturing Systems Engineering Iowa Statementioning

confidence: 99%

“…The most common approach to building interaction design is the consideration of stakeholder objectives in which the building owners, operator, and designers' goals are the driving force behind control decisions (Matson et al, 2012). Objectives are defined through descriptors (such as energy efficiency, minimum financial profit, aesthetic qualities, and notoriety) rather than in proprietary terms (such as a specific manufacturer's HVAC model number).…”

Section: Stakeholder Objectivesmentioning

confidence: 99%

A User-Centered Approach to User-Building Interactions

Kalvelage

Dorneich

2014

Proceedings of the Human Factors and Ergonomics Society Annual

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Providing a task-representative user interface for building operations and control puts the user in charge of their health, safety, and well-being and improves task performance. Enabling users control of building conditions and operations often results in poor overall building performance, such as increased energy usage due to lighting a non-occupied room. As a result, building designers limit the control users have in the spaces they occupy by implementing lighting schedules, for example. However, research has indicated an improved user experience results when users regulate their environment. This conflict produces significant challenges in building interaction design regarding the allocation of control in user-building interactions. An approach is being developed to align user-building interaction design with the building's purpose-to support users' tasks. Rather than multiple operation specific interfaces, providing users a unified taskrepresentative interface links building operations, users, and tasks. The result is a more informative interaction between the user and building operations and a more effective, efficient, and enhanced task experience.

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Social drivers of technology adoption and use in the workplace productivity context

Cited by 4 publications

References 7 publications

The Mindsponge-based extensions of the Technology Acceptance Model: Behavioral determinants of the attitude toward of non-conscious emotional data harvesting

The Mindsponge-based extensions of the Technology Acceptance Model: Behavioral determinants of the attitude toward of non-conscious emotional data harvesting

Platform-Based Business Models: Insights from an Emerging AI-Enabled Smart Building Ecosystem

A User-Centered Approach to User-Building Interactions

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