簡易檢索 / 詳目顯示

研究生: 林權萱
Lin, Chuan-Hsuan
論文名稱: 以建築師事務所觀點探究建築性能模擬軟體在設計實務中之採用因素
Study on the Adoption Factors of Building Performance Simulation Software in Architectural Design Practice
指導教授: 蔡耀賢
Tsay, Yaw-Shyan
學位類別: 碩士
Master
系所名稱: 規劃與設計學院 - 建築學系
Department of Architecture
論文出版年: 2018
畢業學年度: 106
語文別: 中文
論文頁數: 126
中文關鍵詞: 設計工具採用意願半結構式訪談問卷調查結構方程模型
外文關鍵詞: Design Tool, Mixed-research Method, Semi-structured Interview, Questionnaire Survey, SEM
相關次數: 點閱:110下載:11
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 在台灣現今的建築工作流程中,面對建築早期設計階段這個對其永續發展特性做出決定的最關鍵時刻,多數建築師仍使用經驗法則。建築模擬大多是在早期設計階段完成之後,才交付由專家進行,對設計決策的影響力相當有限。若要改善建築性能模擬軟體在實務界之應用層面,發揮模擬軟體之真正效用,應該先針對造成軟體工具無法被有效運用的原因為何做更深入的討論。
    本研究目的為了解台灣建築師事務所決策者對於採用模擬軟體的考量因素,採用包含質性訪談和抽樣問卷的綜合調查方法。首先對國內16位建築師或相關決策者進行深度訪談,探討建築師事務所對於採用模擬軟體的可能考量因素,並提出假設理論。再對在事務所業務中具有重大決策能力者進行抽樣問卷調查,並進行結構方程模型(SEM)分析,以驗證假設理論。
    結果顯示,決策者認為模擬軟體是否有用、模擬軟體與目前工作流程的相容性以及模擬能夠幫助提升競爭力的程度為影響事務所採用模擬軟體意圖的最關鍵因素;其中模擬軟體與工作流程相容性較差可能為目前軟體無法有效導入設計過程的主因,而模擬軟體對於設計競圖時的幫助則是促進事務所採用模擬軟體的重要誘因。另外,當決策者對於模擬軟體有較高的結果品質認同和技術背景資訊認知時,這些軟體也更容易被認為有用,進而提升採用意願。在軟體操作技術層面則發現,目前模擬軟體多被認為使用不易,若能改善軟體的技術外部支援和事務所內部的人力資源品質,將顯著影響事務所對於軟體的易用性認知。而決策者的背景屬性如不同的公司規模、業務類型對於各項因素的認知程度也被發現有顯著差異存在。

    Even though there are various simulation tools have been released on the market in decades, and the value of simulation for green building design has been widely emphasized in literature, most architects in Taiwan stay using the rule of thumb in their architectural design workflow. The study is to explore the critical factors affecting architecture firms’ adoption of software as a basis for improving and realizing the application of building performance simulation (BPS) software on green building design in architectural practice.
    Using a mixed-research method involving semi-structured interview and questionnaire survey to managing architects, the study examined the concerned factors of architects' firms on the adoption of simulation software in practical design process. Empirical data collected from qualitative and quantitative studies revealed that architectural firms’ intention to adopt simulation software are affected mainly by their perception of the software’s usefulness, relevance to works and the compatibility of the software with the current workflow or other design software. The software would be perceived as useful when it was considered to help improve competitiveness, have high output quality and result demonstrability, and when the firms have the required background technological knowledge. In terms of software operation, though the current simulation software is often considered to be difficult to use, the result was validated that the extent to which the simulation tools have external technical supports and there are skilled employees in firms to use software can significantly affect the software’s perceived ease-of-use by firms. Furthermore, the significant differences of the factor perception like cost and competition by different scales and business types of architectural firms are also found. These findings suggest the important consideration from architectural firms’ perspective for developing practical strategies of simulation software to better support architectural firms’ adoption on green building design.

    第一章 緒論 1 1-1 研究背景與動機 1 1-2 研究目的 5 1-3 研究範圍與流程 6 1-3-1 研究範圍 6 1-3-2 研究流程 7 第二章 文獻回顧與相關理論 9 2-1 建築實務設計階段之定義 9 2-1-1 美國建築學會的定義 9 2-1-2 紐西蘭建築師學會的定義 10 2-2 建築性能模擬的需求與現況 11 2-2-1 建築性能模擬的需求 11 2-2-2 建築性能模擬軟體的使用 12 2-3 使用者對於軟體考量 14 2-4 建築性能模擬軟體於設計流程之應用 15 第三章 研究方法 17 3-1 綜合調查方法 17 3-1-1 訪談調查 17 3-1-2 理論假設 18 3-1-3 問卷調查 23 3-2 分析方法 27 3-2-1 訪談分析方法 27 3-2-2 問卷分析方法 27 第四章 訪談調查結果 31 4-1 訪談對象資訊 31 4-2 業界綠建築相關方面的發展現況 33 4-3 建築性能模擬軟體使用現況及阻礙 36 4-4 提升採用模擬軟體意願之建議做法 38 4-5 採用模擬軟體意願之潛在因素 40 4-5-1 技術與工作相關性(Job Relevance) 40 4-5-2 相容性(Compatibility) 42 4-5-3 技術有用性認知(Perceived Usefulness) 44 4-5-4 技術背景資訊認知(Technological Knowledge) 46 4-5-6 技術易用性認知(Perceived Ease of Use) 47 4-5-7 技術結果可證性(Result Demonstrability) 49 4-5-8 技術結果品質(Output quality) 51 4-5-9 成本(Cost) 52 4-5-10 人力資源品質(User Readiness) 54 4-5-11 外部支援(External Support) 55 4-5-12 競爭力(Competition) 57 4-5-13 小結 58 4-6 受訪者背景屬性與考量因素之關係 58 4-6-1 公司規模 58 4-6-2 業務類型 61 4-6-3 年齡 64 4-7 訪談結果小結 65 第五章 問卷調查結果 67 5-1 問卷蒐集結果 67 5-2 描述性統計 68 5-2-1 受訪者背景屬性 68 5-2-2技術採用因素認知量表 72 5-3 項目分析與修正 80 5-4 測量模式分析結果 86 5-5 結構模式分析結果 90 5-6 背景屬性與潛在因素分析結果 94 5-6-1 公司規模 94 5-6-2 業務類型 97 5-6-3 決策者年齡 100 5-7 背景屬性與軟體使用現況 102 5-8 問卷結果小結 106 第六章 結果與討論 109 6-1 主要研究理論模型假設 110 6-2 決策者背景屬性之影響 113 6-3 本研究之理論限制 114 第七章 結論與建議 115 7-1 研究結論 115 7-2 後續研究建議 117 參考文獻 119 附錄 123

    (一) 中文文獻
    1. 內政部建築研究所(2015),綠建築評估手冊-基本型
    2. 陳上元、邱秀婷(2014),綠色的建築資訊模型,內政部建築研究所智慧化居住空間專屬網站
    3. 鄭泰昇(2014),BIM導入台灣綠建築設計案例實作研究,內政部建築研究所
    4. 吳明隆(2005),SPSS統計應用學習實務:問卷分析與應用統計
    5. 陳寬裕、王正華(2010),結構方程模型分析實務:AMOS的運用
    6. 陳順宇(2005),多變量分析,台北:華泰文化

    (二) 英文文獻
    1. Pachauri R.K., Reisinger A. (2007). Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva, Switzerland: IPCC.
    2. Jernigan F.E. (2007). Big BIM, little bim: The practical approach to building information modelling: Integrated practice done the right way! 1st ed. Salisbury, MD: 4Site Press.
    3. Hand J., Crawley D., Donn M., Lawrie L. (2008). Improving non-geometric data available to simulation programs. Building and Environment 43(4), 674-685.
    4. Lam K.P., Wong N.H., Henry F. (1999). A Study of the Use of Performance-based Simulation Tools for Building Design and Evaluation in Singapore. International Building Performance Simulation Association, Kyoto, Japan.
    5. Attia S., Beltran L., Herde A.D., Hensen J. (2009). "Architect friendly": A Comparison of Ten Different Building Performance Simulation Tools. International Building Performance Simulation Association.
    6. Ellis M.W., Mathews E.H. (2001). A new simplified thermal design tool for architects. Building and Environment 36 (9), 1009-1021.
    7. Yezioro A. (2009). A knowledge based CAAD system for passive solar architecture. Renewable Energy 34 (3), 769-779.
    8. Chlela F., Husaunndee A., Inard C., Riederer P. (2009). A new methodology for the design of low energy buildings. Energy and Buildings 41(9), 982-990.
    9. Petersen S., Svendsen S. (2010). Method and simulation program informed decisions in the early stages of building design. Energy and Buildings 42(7), 1113-1119.
    10. F. Garde, A. Lenoir, M. David. (2010). Building design and energy performance of buildings in the French island of La Reunion. Feedback and updating of the PERENE project. REHVA world congress Clima 2010, Antalya, Turkey.
    11. AIA. (1995). AIA Document D200:Project Checklist.
    12. All Australian Architecture. Stages of the Design and Building Process. Available https://www.aaarchitect.com.au/design-and-building-process.html.
    13. IEA-SHC. (2011). IEA SHC Annual Report 2011.
    14. Salman A., Justin B. (2009). BIM for Sustainability Analyses. Construction Education and Research 5, 276-292.
    15. Verdonck E., Lieve W., Verbeeck G., Froyen H. (2011). Design Support Tools in Practice. The Architects’ Perspective. In Designing Together: Proceedings of the 14th International Conference on Computer Aided Architectural Design, 769-784.
    16. Kensek K.M., Noble D. (2015). Building Information Modeling: BIM in Current and Future Practice. John Wiley & Sons.
    17. Alexander et al. (2016). Developing A Workflow for Daylight Simulation Daylight Requirements Simulation in Early Design Stages to Address the Green Star Ratings within Local Regulations
    18. Davis F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13(3), 319-340.
    19. 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), 985.
    20. Venkatesh V., Davis F.D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science 46(2), 186-204.
    21. Venkatesh V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems Research 11(4), 342-365
    22. Rogers E.M. (1983). The Diffusion of Innovation, 3th ed. New York.
    23. Rogers E.M. (1995). The Diffusion of Innovation, 4th ed. New York.
    24. Rogers E.M. (2003). The Diffusion of Innovation, 5th ed. New York
    25. Wu J.H., Wang S.C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management 42, 719-729.
    26. Chen L., Gillenson M.L., Sherrell D.L. (2002). Enticing Online Consumers: A Technology Acceptance Perspective. Information and Management 39(8), 705-719.
    27. Plouffe C.R., Hulland J.S., Vandenbosch M. (2001). Research Report: Richness Versus Parsimony in Modeling Technology Adoption Decisions-Understanding Merchant Adoption of A Smart Card-Based Payment System. Information Systems Research 12(2), 208-222.
    28. Premkumar G., Roberts M. (1999). Adoption of new information technologies in rural small businesses. Management Science 27, 467-484.
    29. Premkumar G., Ramamurthy K., Nilakanta S. (1994). Implementation of electronic data interchange: an innovation diffusion perspective. Management Information Systems 11(2), 157-186.
    30. Tornatzky L.G., Klein K.J. (1982). Innovation Characteristics and Innovation Adoption Implementation: A Meta Analysis of Findings. IEEE Transactions on Engineering Management 29(11), 28-45.
    31. Moore, G. C., I. Benbasat. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research 2, 192-222.
    32. Davis F. D., Bagozzi R. P., Warshaw P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Jornal of Applied Social Psychology 22(14), 1111-1132.
    33. Delone WH. Firm Size and Characteristics of Computer Use. MIS Quarterly 5(4), 65-77.
    34. Gatignon H., Robertson T.S. (1989) Technology Diffusion: An Empirical Test of Competitive Effects. Journal of Marketing 53(1), 35-49.
    35. Kwon TH, Zmud RW. (1987) Unifying the Fragmented Models of Information System’ Implementation. Critical Issues in Information System Research, 227-251.
    36. Bagozzi R.P., Yi Y. (1988). On the Evaluation of Structural Equation Models. Academic of Marketing Science 16, 76-94.
    37. Fornell C., Larcker D.F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research 18, 39-50.
    38. Chin W.W. (1998). The Partial Least Squares Approach for Structural Equation Modeling. In G. A. Marcoulides (Ed.), Modern methods for business research, 295-336.
    39. Browne M.W., Cudeck R. (1992). Alternative Ways of Assessing Model Fit. Sociological Methods & Research 21(2), 230-258.
    40. Carmines E.G., McIver J.P. (1981). Analyzing Models with Unobserved Variables. In Bohrnstedt G.W., Borgatta E.F. (Eds.). Social Measurement: Current Issues, 65-115. Beverly Hills: Sage.
    41. Wheaton B. (1987). Assessment of Fit in Over-identified Models with Latent Variables. Sociological Methods and Research 16, 118-154.
    42. Bentler P.M. (1990). Comparative Fit Indexes in Structural Models. Psychological Bulletin 107(2), 238-246.
    43. Hu L.T., Bentler P.M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling 6 (1), 1-55.
    44. Hooper D., Coughlan J., Mullen M.R. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods 6(1), 53-60.
    45. Francisco J. Farias Stipo. (2015). A Standard Design Process for Sustainable Design. The 5th International Conference on Sustainable Energy Information Technology.
    46. Jouri Kanters, Miljana Horvat, Marie-Claude Dubois. (2014). Tools and methods used by architects for solar design. Energy and Buildings 68, 721-731.
    47. Lin C.Y., Ho Y.H. (2008). An Empirical Study on Logistics Service Providers’ Intention to Adopt Green Innovations. Technology Management and Innovation 3.
    48. Hung D.N., Long D.N., Chih Y.Y., Long L.H. (2017). Influence of Participants’ Characteristics on Sustainable Building Practices in Emerging Economies: Empirical Case Study. Construction Engineering and Management 143(8).
    49. Jeanine W.T., Robert J.T., N. Lamar Reinsch Jr. (2004). Business Communication 41(1), 5-26.

    下載圖示 校內:2020-01-01公開
    校外:2020-01-01公開
    QR CODE