Research on factors influencing an individual’s behavior of energy management: a field study in China

Journal of Management Analytics

Volumn 4, Issue 3, 2017, Pages 203-239

  Fan, Yanfeng ^a  

^a ARIZONA STATE UNIVERSITY   (United States)

Author keywords

diffusion of innovations; energy consumption; energy management; energy saving attitude; theory of planned behavior

Indexed keywords

EID: 85044944615     PISSN: 23270012     EISSN: 23270039     Source Type: Journal    
DOI: 10.1080/23270012.2017.1310000     Document Type: Article

References (62)

1. Abrahamse, W., & Steg, L., (2009). How do socio-demographic and psychological factors relate to households’ direct and indirect energy use and savings. Journal of Economic Psychology, 30(5), 711–720. doi: 10.1016/j.joep.2009.05.006
2. Adams, D. A., Nelson, R. R., & Todd, P. A., (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227–247. doi: 10.2307/249577
3. Ajzen, I., (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. doi: 10.1016/0749-5978(91)90020-T
4. Aydinalp, M., Ugursal, V. I., & Fung, A. S., (2002). Modeling of the appliance, lighting, and space cooling energy consumption in the residential sector using neural networks. Applied Energy, 71(2), 87–11. doi: 10.1016/S0306-2619(01)00049-6
5. Aydinalp, M., Ugursal, V. I., & Fung, A. S., (2004). Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks. Applied Energy, 79(2), 159–178. doi: 10.1016/j.apenergy.2003.12.006
6. Bentler, P. M., & Chou, C., (1987). Practical issues in structural modeling. Sociological Methods and Research, 16(1), 78–117. doi: 10.1177/0049124187016001004
7. Bi, Z., & Cochran, D., (2014). Big data analytics with applications. Journal of Management Analytics, 1(4), 249–265. doi: 10.1080/23270012.2014.992985
8. Black, J. S., Stern, P. C., & Elworth, J. T., (1985). Personal and contextual influences on household energy adaptations. Journal of Applied Psychology, 70, 3–21.
9. Branger, J., & Pang, Z., (2015). From automated home to sustainable, healthy and manufacturing home: A new story enabled by the internet-of-things and industry 4.0. Journal of Management Analytics, 2(4), 314–332. doi: 10.1080/23270012.2015.1115379
10. Carlsson-Kanyama, A., Linden, A., & Eriksson, B., (2005). Residential energy behaviour: Does generation matter? International Journal of Consumer Studies, 29(3), 239–253. doi: 10.1111/j.1470-6431.2005.00409.x
11. Chan, R. Y. K., (2001). Determinants of Chinese consumers' green purchase behavior. Psychology and Marketing, 18(4), 389–413. doi: 10.1002/mar.1013
12. Chen, L., (2009). Online consumer behavior: An empirical study based on theory of planned behavior (Doctoral dissertation). ETD collection for University of Nebraska–Lincoln, AAI3355613.
13. Chong, D., & Shi, H., (2015). Big data analytics: A literature review. Journal of Management Analytics, 2(3), 175–201. doi: 10.1080/23270012.2015.1082449
14. Chung, S. S., & Poon, C. S., (2001). A comparison of waste-reduction practices and new environmental paradigm of rural and urban Chinese citizens. Journal of Environmental Management, 62(1), 3–19. doi: 10.1006/jema.2000.0408
15. Curtis, F. A., Simpson-Housley, P., & Youck, C., (1984). Household energy conservation and locus of control: A research note. International Journal of Energy Research, 8(1), 89–93. doi: 10.1002/er.4440080109
16. Davis, F. D., (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. doi: 10.2307/249008
17. Davis, F. D., (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475–487. doi: 10.1006/imms.1993.1022
18. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R., (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. doi: 10.1287/mnsc.35.8.982
19. Egmond, C., Jonkers, R., & Kok, G., (2005). A strategy to encourage housing associations to invest in energy conservation. Energy Policy, 33, 2374–2384.
20. Fishbein, M. E., & Ajzen, I., (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. In I. Ajzen (Ed.), Prediction of behavior (pp. 335–385). Reading, MA: Addison-Wesley.
21. Gatersleben, B., Steg, L., & Vlek, C., (2002). Measurement and determinants of environmentally significant consumer behaviour. Environment and Behavior, 34, 335–362.
22. Hungerford, H., Litherland, R., Peyton, R., Ramsey, J., Tomera, A., & Volk, T., (1988). Investigating and evaluation environmental issues and actions. Champaign, IL: Stipes Publishing L.L.C.
23. Ianuale, N., Schiavon, D., & Capobianco, E., (2015). Smart cities and urban networks: Are smart networks what we need? Journal of Management Analytics, 2(4), 285–294. doi: 10.1080/23270012.2015.1023856
24. Jiayue, A., & Liu, Z., (2014). What impact entrepreneurial intention? Cultural, environmental, and educational factors. Journal of Management Analytics, 1(3), 224–239. doi: 10.1080/23270012.2014.994232
25. Knabe, A., (2012). Applying Ajzen’s theory of planned behavior to a study of online course adoption in public relations education (Doctoral dissertation). Retrieved from e-Publications Marquette: http://epublications.marquette.edu/cgi/viewcontent.cgi?article=1186&context=dissertations_mu
26. Knight, R., Lutzenhiser, L., & Lutzenhiser, S., (2004). Selling residential energy efficiency retrofits: Why do they buy? Proceedings of the 2004 ACEEE Summer Study, American Council for an Energy Efficient Economy, Washington, DC.
27. Linden, A., Carlsson-Kanyama, A., & Eriksson, B., (2006). Efficient and inefficient aspects of residential energy behaviour: What are the policy instruments for change? Energy Policy, 34(14), 1918–1927. doi: 10.1016/j.enpol.2005.01.015
28. Loren, L., & Lutzenhiser, S., (2006). Evaluation of the California home performance program in the PG&E service area, 2004–05. Portland, OR: Lutzenhiser Associates (forthcoming for PG&E and the California Public Utilities Commission).
29. Lutzenhiser, L., (2002). Greening the economy from the bottom-up? Lessons in consumption from the energy case. In N. W., Biggart (Ed.), Readings in economic sociology (pp. 345–356). Oxford: Blackwell.
30. Ma, M., Oikonomou, A., & Zheng, H., (2009). Second life as a learning and teaching environment for digital games education. Retrieved from http://eprints.hud.ac.uk/id/eprint/23206
31. Mathieson, K., (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191. Retrieved from http://www.jstor.org/stable/23010882
32. Midden, C. J. H., & Ritsema, B. S. M. Z., (1983). The meaning of normative processes for energy conservation. Journal of Economic Psychology, 4, 37–55. doi: 10.1016/0167-4870(83)90044-2
33. Moore, G. C., & Benbasat, I., (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. doi: 10.1287/isre.2.3.192
34. Nor, K. M., (2005). An empirical study of internet banking acceptance in Malaysia: An extended decomposed theory of planned behavior (Doctoral dissertation). Southern Illinois University Carbondale, Carbondale, IL.
35. Oikonomou, V., Becchis, F., Steg, L., & Russolillo, D., (2009). Energy saving and energy efficiency concepts for policy making. Energy Policy, 37(11), 4787–4796. doi: 10.1016/j.enpol.2009.06.035
36. Pavlou, P. A., & Fygenson, M., (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. Management Information Systems Quarterly, 30(1), 115–143. Retrieved from http://www.jstor.org/stable/25148720
37. Poortinga, W., & Pidgeon, N. F., (2003). Public perceptions of risk, science and governance: Main findings of a British survey of five risk cases. Norwich: UEA/MORI.
38. Poortinga, W., Steg, L., & Vlek, C., (2004). Values, environmental concern and environmental behavior: A study into household energy use. Environment and Behavior, 36, 70–93.
39. van Raaij, W. F., & Verhallen, T. M. M., (1983). A behavioral model of residential energy use. Journal of Economic Psychology, 3(1), 39–63.
40. Reiss, P., & White, M., (2006). Evaluating welfare with nonlinear prices (NBER Working Paper No. 12370). doi: 10.3386/w12370
41. Rogers, E. M., (2003). Diffusion of innovations. New York, NY: Free Press.
42. Ryan, G., & Wu, F., (2011). What influences trader choice of electronic versus intermediated execution? International Review of Finance, 11(4), 445–476.
43. Samuelson, C. D., & Biek, M., (1991). Attitudes toward energy conservation: A confirmatory factor analysis. Journal of Applied Social Psychology, 21(7), 549–568. doi: 10.1111/j.1559-1816.1991.tb00536.x
44. Sardianou, E., (2005). Household energy conservation patterns: Evidence from Greece. Retrieved from http://www.lse.ac.uk/europeanInstitute/research/hellenicObservatory/pdf/2nd_Symposium/Eleni_Sardianou_paper.pdf
45. Scott, S., Dorrington, R., Svetlov, V., Beeser, A. E., Distler, M., & Cooper, T. G., (2000). Functional domain mapping and subcellular distribution of dal82p in saccharomyces cerevisiae. The Journal of Biological Chemistry, 275(10), 7189–7204. doi: 10.1074/jbc.275.10.7198
46. Segars, A. H., & Grover, V., (1993). Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS Quarterly, 17(4), 517–525.
47. Sherer, M., Maddux, J. E., Mercandante, B., Prentice-Dunn, S., Jacobs, B., & Rogers, R. W., (1982). The self-efficacy scale: Construction and validation. Psychological Reports, 51(2), 663–671. doi: 10.2466/pr0.1982.51.2.663
48. Steg, L., (2005). Car use: Lust and must. Instrumental, symbolic and affective motives for car use. Transportation Research Part A, 39, 147–162.
49. Steg, L., & Vlek, C., (2009). Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology, 29(3), 309–317. doi: 10.1016/j.jenvp.2008.10.004
50. Stern, P. C., (1992). Psychological dimensions of global environmental change. Annual Review of Psychology, 43, 269–302.
51. Stern, P. C., (1999). Information, incentives, and proenvironmental consumer behavior. Journal of Consumer Policy, 22(4), 461–478. doi: 10.1023/A:1006211709570
52. Szajna, B., (1994). software evaluation and choice: Predictive evaluation of the technology acceptance instrument. MIS Quarterly, 18(3), 319–324.
53. Szajna, B., (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85–92. Retrieved from http://www.jstor.org/stable/2633017
54. Tanner, C., & Kast, S. W., (2003). Promoting sustainable consumption: Determinants of green purchases by Swiss consumers. Psychology & Marketing, 20(10), 883–902. doi: 10.1002/mar.10101
55. Taylor, J. B., (1995). The monetary transmission mechanism: An empirical framework. San Francisco, CA: Federal Reserve Bank of San Francisco.
56. Taylor, S., & Todd, P. A., (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(4), 144–176.
57. Venkatesh, V., & Davis, F. D., (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481. doi: 10.1111/j.1540-5915.1996.tb00860.x
58. Xu, L. D., He, W., & Li, S., (2014). Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4), 2233–2243.
59. Wu, C.-Y., & Sun, Y., (2006). Review on studies of environmental attitudes and behavior and the relationship between them. Retrieved from http://en.cnki.com.cn/Article_en/CJFDTOTAL-YUCE200604011.htm
60. Zhang, Z., & Jasimuddin, S. M., (2015). A model-based analysis for mobile knowledge management in organizations. Journal of Management Analytics, 2(1), 35–52. doi: 10.1080/23270012.2015.1012232
61. Zheng, B., (1997). Aggregate poverty measures. Journal of Economic Surveys, 1(2), 123–162. doi:10.1111/1467-6419.0002811:123–162
62. Zou, H., Chen, H. M., & Dey, S., (2015). Exploring user engagement strategies and their impacts with social media mining: The case of public libraries. Journal of Management Analytics, 2(4), 295–313.