Decision Making Model for Choosing Voice-Operated Intelligent Speakers for Graduate Students

The recent technological advancements have shed light on some futuristic inventions and possibilities. One of these inventions and technologies has been home automation especially due to the rise of Internet of Things. One of the main areas of advancement in home automation has been Voice-operated speakers. Given the surprising success of the Amazon Echo ‐‐ a smart speaker that responds to your voice commands, plays music, and controls your smart home ‐‐ this area has become a competitive one. With Google Home entering the arena, backed by the company's ubiquitous search engine, the Echo's place on top is no longer secure. This growth will, evidently, increase the poll of sellers and would make the decision-making process an arduous one, both, for producers and the buyers of these products. This study looks into the decision-making criteria for the selection of the best voice operated speaker by consumers in order to facilitate the decision-making process involving smart, voice activated speakers. The Hierarchical Decision Model (HDM) has been used to establish a model based on perspectives, criteria, and alternatives. Furthermore, with the aim of better demonstrating the practicality of the study, five real voice operated speaker products are evaluated based on the perspectives and criteria weights obtained from the HDM model and scores.

[1]  Hee-Cheol Kim,et al.  Six Human Factors to Acceptability of Wearable Computers , 2013 .

[2]  Amir Shaygan,et al.  A fuzzy AHP-based methodology for project prioritization and selection , 2019, Soft Comput..

[3]  Amir Shaygan,et al.  Selecting Health Care Improvement Projects: A Methodology Integrating Cause-and-Effect Diagram and Analytical Hierarchy Process , 2017, Quality management in health care.

[4]  Amir Shaygan,et al.  Adoption Criteria Evaluation of Activity Tracking Wristbands for University Students , 2017, 2017 Portland International Conference on Management of Engineering and Technology (PICMET).

[5]  Rüdiger Kays,et al.  Performance Evaluation of Wireless Home Automation Networks in Indoor Scenarios , 2012, IEEE Transactions on Smart Grid.

[6]  Munkee Choi,et al.  User acceptance of wearable devices: An extended perspective of perceived value , 2016, Telematics Informatics.

[7]  Husam Barham,et al.  Achieving Competitive Advantage Through Big Data: A Literature Review , 2017, 2017 Portland International Conference on Management of Engineering and Technology (PICMET).

[8]  Bo-Chen Lin,et al.  A hybrid decision-making model for factors influencing the purchase intentions of technology products: the moderating effect of lifestyle , 2015, Behav. Inf. Technol..

[9]  Soheil Zarrin,et al.  A comprehensive assessment of cloud computing for smart grid applications: A multi-perspectives framework , 2015, 2015 Portland International Conference on Management of Engineering and Technology (PICMET).

[10]  Tharam S. Dillon,et al.  Cloud Computing: Issues and Challenges , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[11]  Fuyuan Xu,et al.  An Empirical Study on Factors Influencing Consumers' Initial Trust in Wearable Commerce , 2016, J. Comput. Inf. Syst..

[12]  Shuang-Hua Yang,et al.  A zigbee-based home automation system , 2009, IEEE Transactions on Consumer Electronics.

[13]  Chris Baber,et al.  End-User perception towards pervasive cardiac healthcare services: Benefits, acceptance, adoption, risks, security, privacy and trust , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[14]  Turgut Turan,et al.  Use of Hierarchal Decision Modeling (HDM) for selection of graduate school for master of science degree program in engineering , 2009, PICMET '09 - 2009 Portland International Conference on Management of Engineering & Technology.

[15]  Muhammad Asadullah,et al.  An overview of home automation systems , 2016, 2016 2nd International Conference on Robotics and Artificial Intelligence (ICRAI).

[16]  S. Sundar,et al.  Personalization versus Customization: The Importance of Agency, Privacy, and Power Usage , 2010 .

[17]  Victor Dibia,et al.  An Affective, Normative and Functional Approach to Designing User Experiences for Wearables , 2015 .

[18]  Michel Vacher,et al.  Context-aware decision making under uncertainty for voice-based control of smart home , 2017, Expert Syst. Appl..

[19]  Nameer Al-Mulla,et al.  Technology assessment: case of the wearable computing for fitness , 2015, Int. J. Medical Eng. Informatics.

[20]  Matthew S. Eastin,et al.  Wearable fitness technology: A structural investigation into acceptance and perceived fitness outcomes , 2016, Comput. Hum. Behav..

[21]  Seongcheol Kim,et al.  Consumer valuation of the wearables: The case of smartwatches , 2016, Comput. Hum. Behav..

[22]  T. Daim,et al.  Landscape Analysis: Fracking Technology , 2018 .

[23]  Brent B. Clark,et al.  The Technology Effect: How Perceptions of Technology Drive Excessive Optimism , 2016 .

[24]  Amir Shaygan Landscape Analysis: What Are the Forefronts of Change in the US Hospitals? , 2018 .