The Way of Expanding Technology Acceptance—Open Innovation Dynamics

To promote efficient use of electrical energy, technology-based solutions, along with their corresponding user acceptance assessments, have been seen to facilitate goal fulfillment concerning desired functionality and expected benefits, in an open innovation fashion. This paper simultaneously develops an electrical energy consumption monitoring system (EECMS) device that shall monitor and control the use of energy in real-time and assesses its acceptability to users according to the extended technology acceptance model (TAM) approach. This proposed EECMS device is tested in an academic institution in the Philippines, and it is found that the device can function as desired as well as render a significant favor from its users according to additional key constructs. As such, future developments of the device are encouraged to enhance key constructs identified as suitable for future adoption.

[1]  P. Berkhout,et al.  Defining the rebound effect , 2000 .

[2]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[3]  Zdenek Kotásek,et al.  I-path analysis , 1993, Proceedings ETC 93 Third European Test Conference.

[4]  Peter A. Dacin,et al.  The Company and the Product: Corporate Associations and Consumer Product Responses , 1997 .

[5]  Roberto Barchino,et al.  Analysis of competence acquisition in a flipped classroom approach , 2018, Comput. Appl. Eng. Educ..

[6]  Yang Lu,et al.  Industry 4.0: A survey on technologies, applications and open research issues , 2017, J. Ind. Inf. Integr..

[7]  Shu-Chin Wang,et al.  Precise Positioning of Marketing and Behavior Intentions of Location-Based Mobile Commerce in the Internet of Things , 2017, Symmetry.

[8]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[9]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[10]  D. K. Serghides,et al.  Analysis of structural elements and energy consumption of school building stock in Cyprus: Energy simulations and upgrade scenarios of a typical school , 2014 .

[11]  Elgar Fleisch,et al.  PowerPedia: changing energy usage with the help of a community-based smartphone application , 2011, Personal and Ubiquitous Computing.

[12]  H. Gam,et al.  Perceived ease of use and usefulness of sustainability labels on apparel products: application of the technology acceptance model , 2017, Fashion and Textiles.

[13]  E. Park,et al.  Consumer Acceptance Analysis of the Home Energy Management System , 2017 .

[14]  Rajneesh Narula,et al.  Buyer (dis)satisfaction and process innovation: the case of information technology services provision , 2018 .

[15]  M. Santamouris,et al.  Socio-economic status and residential energy consumption: A latent variable approach , 2019, Energy and Buildings.

[16]  Jay Lee,et al.  Cyber-physical Systems Architecture for Self-Aware Machines in Industry 4.0 Environment , 2015 .

[17]  Jayesh D. Patel,et al.  Predicting green product consumption using theory of planned behavior and reasoned action , 2016 .

[18]  I. Andresen,et al.  Lessons learnt from embodied GHG emission calculations in zero emission buildings (ZEBs) from the Norwegian ZEB research centre , 2018 .

[19]  A. Paladino,et al.  Using the theory of planned behaviour to predict intentions to purchase sustainable housing , 2019, Journal of Cleaner Production.

[20]  Won Jun Lee,et al.  User Acceptance of the Mobile Internet , 2002 .

[21]  Rebecca E. Grinter,et al.  Getting to green: understanding resource consumption in the home , 2008, UbiComp.

[22]  Ronny Scherer,et al.  The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education , 2019, Comput. Educ..

[23]  Jon-Chao Hong,et al.  The effect of consumer innovativeness on perceived value and continuance intention to use smartwatch , 2017, Comput. Hum. Behav..

[24]  A. Pitts,et al.  A survey on electrical appliance use and energy consumption in Vietnamese households: Case study of Tuy Hoa city , 2019, Energy and Buildings.

[25]  Hyojoo Son,et al.  Toward an understanding of construction professionals' acceptance of mobile computing devices in South Korea: An extension of the technology acceptance model , 2012 .

[26]  R. V. Carrillo-Serrano,et al.  Development of a Real Time Energy Monitoring Platform User-Friendly for Buildings☆ , 2013 .

[27]  Shakil Sulaiman.  INTEGRATION OF LEGACY APPLIANCES INTO THE SMART HOME , 2015 .

[28]  Li Da Xu,et al.  Industry 4.0: state of the art and future trends , 2018, Int. J. Prod. Res..

[29]  Yogesh Kumar Dwivedi,et al.  Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology , 2015, Gov. Inf. Q..

[30]  Rudolf R. Sinkovics,et al.  The Use of Partial Least Squares Path Modeling in International Marketing , 2009 .

[31]  Habibollah Asghari,et al.  Determinants of behavioral intention to use e-textbooks: A study in Iran's agricultural sector , 2019, Comput. Electron. Agric..

[32]  Xinrong Li,et al.  Wireless Sensor Network System Design Using Raspberry Pi and Arduino for Environmental Monitoring Applications , 2014, FNC/MobiSPC.

[33]  Jacky Chin,et al.  A Behavioral Model of Managerial Perspectives Regarding Technology Acceptance in Building Energy Management Systems , 2016 .

[34]  Lei-da Chen,et al.  Enticing online consumers: an extended technology acceptance perspective , 2002, Inf. Manag..

[35]  Haoyu Wen,et al.  How Is Motivation Generated in Collaborative Consumption: Mediation Effect in Extrinsic and Intrinsic Motivation , 2019, Sustainability.

[36]  G. Faluvegi,et al.  Quantified, Localized Health Benefits of Accelerated Carbon Dioxide Emissions Reductions , 2018, Nature Climate Change.

[37]  Shu-Chu Chen,et al.  A Study of EFL College Students’ Acceptance of Mobile Learning , 2015 .

[38]  Marjan Hericko,et al.  A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types , 2011, Comput. Hum. Behav..

[39]  Dennis F. Galletta,et al.  The Role of Human Computer Interaction in Management Information Systems Curricula: A Call to Action , 2004, Commun. Assoc. Inf. Syst..

[40]  Vladislav Kantchev Shunturov,et al.  Dormitory residents reduce electricity consumption when exposed to real‐time visual feedback and incentives , 2007 .

[41]  Charlie C. L. Wang,et al.  Current and future trends in topology optimization for additive manufacturing , 2018 .

[42]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[43]  William R. King,et al.  A meta-analysis of the technology acceptance model , 2006, Inf. Manag..

[44]  Wan Salihin Wong Abdullah,et al.  The Effects of Perceived Usefulness and Perceived Ease of Use on Continuance Intention to Use E-Government , 2016 .

[45]  Kriengkrai Assawamartbunlue,et al.  Specific energy consumption of cement in Thailand , 2019, Energy Procedia.

[46]  S. Sharma.  The relationship between energy and economic growth: Empirical evidence from 66 countries , 2010 .

[47]  Tenzin Doleck,et al.  Modeling Students' Perceptions of Simulation-Based Learning Using the Technology Acceptance Model , 2018, Clinical Simulation in Nursing.

[48]  Biliang Luo,et al.  Rural household energy consumption characteristics and determinants in China , 2019, Energy.

[49]  Ying Fan,et al.  Exploring reasons behind careful-use, energy-saving behaviours in residential sector based on the theory of planned behaviour: Evidence from Changchun, China , 2019, Journal of Cleaner Production.

[50]  Zhigang Huang,et al.  Individual new energy consumption and economic growth in China , 2019 .

[51]  Rich Ling,et al.  Measured energy savings from a more informative energy bill , 1995 .

[52]  Leif Gustavsson,et al.  Life cycle primary energy use and carbon emission of an eight-storey wood-framed apartment building , 2010 .

[53]  Yogi Sugiawan,et al.  Are carbon dioxide emission reductions compatible with sustainable well-being? , 2019, Applied Energy.

[54]  J. Wang,et al.  Energy consumption in elementary and high schools in Taiwan , 2019, Journal of Cleaner Production.

[55]  R. N. Elliott,et al.  American Council for an Energy-Efficient Economy , 2002 .

[56]  António Amaral,et al.  Network and information security challenges within Industry 4.0 paradigm , 2017 .

[57]  June Lu,et al.  Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology , 2005, J. Strateg. Inf. Syst..

[58]  Kieran Mathieson,et al.  Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior , 1991, Inf. Syst. Res..

[59]  Celbert Mirasol Himang,et al.  Modeling the Success of Windows Domain Network (WDN) Using the DeLone and McLean Information System (IS) Success Model: A University Case , 2019, Int. J. Sociotechnology Knowl. Dev..

[60]  Hosny I. Hamdy,et al.  Determinants of continuance intention factor in Kuwait communication market: Case study of Zain-Kuwait , 2015, Comput. Hum. Behav..

[61]  Jennifer Paff Ogle,et al.  Socially Responsible Labeling , 2012 .

[62]  J. Yun,et al.  Micro- and Macro-Dynamics of Open Innovation with a Quadruple-Helix Model , 2019, Sustainability.

[63]  Clare D'souza,et al.  An empirical study on the influence of environmental labels on consumers , 2006 .

[64]  Oliver Musshoff,et al.  Understanding the adoption of smartphone apps in dairy herd management. , 2019, Journal of dairy science.

[65]  R. Bagozzi,et al.  On the evaluation of structural equation models , 1988 .

[66]  Ritu Agarwal,et al.  A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology , 1998, Inf. Syst. Res..

[67]  Hema Date,et al.  Understanding determinants of cloud computing adoption using an integrated TAM-TOE model , 2015, J. Enterp. Inf. Manag..

[68]  C. Ruiz-Mafé,et al.  The role of consumer innovativeness and perceived risk in online banking usage , 2009 .

[69]  Eddie W. L. Cheng,et al.  SEM being more effective than multiple regression in parimonious model testing for management development research , 2001 .

[70]  Milin Lu,et al.  Measurement of energy rebound effect in households: Evidence from residential electricity consumption in Beijing, China , 2016 .

[71]  S. Noh,et al.  Characterization of CO2 emissions during construction of reservoir embankment elevation in South Korea , 2013, The International Journal of Life Cycle Assessment.

[72]  Giuseppe Anastasi,et al.  An intelligent system for electrical energy management in buildings , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[73]  Dominique Guinard,et al.  Increasing energy awareness through web-enabled power outlets , 2010, MUM.

[74]  K. Witkowski.  Internet of Things, Big Data, Industry 4.0 – Innovative Solutions in Logistics and Supply Chains Management ☆ , 2017 .

[75]  Charles D. Barrett.  Understanding Attitudes and Predicting Social Behavior , 1980 .

[76]  João Figueiredo,et al.  A SCADA system for energy management in intelligent buildings , 2012 .

[77]  Humayun Zafar,et al.  A Multi-Country Assessment of Mobile Payment Adoption , 2008 .

[78]  Celbert M. Himang,et al.  Library Periodical Indexing Software Evaluation using Unified Theory of Acceptance and Use of Technology , 2011 .

[79]  Michael Nye,et al.  Making energy visible: A qualitative field study of how householders interact with feedback from smart energy monitors , 2010 .

[80]  Miimu Airaksinen,et al.  Effect of energy measures on the values of energy efficiency indicators in Finnish daycare and school buildings , 2017 .

[81]  Alok Mishra,et al.  Theory of Reasoned Action application for Green Information Technology acceptance , 2014, Comput. Hum. Behav..

[82]  Mohd Zulkifli Muhammad,et al.  An Exploration of Social Networking Sites (SNS) Adoption inMalaysia Using Technology Acceptance Model (TAM), Theory ofPlanned Behavior (TPB) And Intrinsic Motivation , 2011 .

[83]  G. Maione,et al.  Extended Theory of Planned Behavior (ETPB): Investigating Customers’ Perception of Restaurants’ Sustainability by Testing a Structural Equation Model , 2018, Sustainability.

[84]  Wei-Wen Wu,et al.  Developing an explorative model for SaaS adoption , 2011, Expert Syst. Appl..

[85]  Nadeem Javaid,et al.  Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes , 2017 .

[86]  Trivess Moore,et al.  Modelling the through-life costs and benefits of detached zero (net) energy housing in Melbourne, Australia , 2014 .

[87]  Rebecca Ford,et al.  The effects of feedback on energy conservation: A meta-analysis. , 2015, Psychological bulletin.

[88]  Farzana Parveen,et al.  Technology Complexity , Personal Innovativeness And Intention To Use Wireless Internet Using Mobile Devices In Malaysia , 2008 .

[89]  A AbbasHasan,et al.  Determinants of continuance intention factor in Kuwait communication market , 2015 .

[90]  Boris Otto,et al.  Design Principles for Industrie 4.0 Scenarios , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[91]  Amy J. C. Trappey,et al.  A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0 , 2017, Adv. Eng. Informatics.

[92]  Yogesh Kumar Dwivedi,et al.  Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust , 2018, Technology in Society.

[93]  Tang Jeung-tai E.,et al.  Perceived Innovativeness, Perceived Convenience and TAM: Effects on Mobile Knowledge Management , 2009, 2009 Third International Conference on Multimedia and Ubiquitous Engineering.

[94]  Sunyoung Lee,et al.  Real-time Energy Monitoring and Controlling System based on ZigBee Sensor Networks , 2011, ANT/MobiWIS.

[95]  Lucio Soibelman,et al.  Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring , 2010 .

[96]  Wu He,et al.  Internet of Things in Industries: A Survey , 2014, IEEE Transactions on Industrial Informatics.