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旅游导刊 ›› 2022, Vol. 6 ›› Issue (3): 62-85.DOI: 10.12054/lydk.bisu.189
收稿日期:
2021-08-10
修回日期:
2022-03-10
出版日期:
2022-06-30
发布日期:
2022-06-23
作者简介:
罗文斌(1982— ),男,湖南衡阳人,博士,湖南师范大学旅游学院副教授,研究方向:土地利用与旅游发展、乡村振兴与农户可持续生计、旅游审美与教育。E-mail: sdlwb@vip.126.com基金资助:
LUO Wenbin(), DING Dexiao, PEI Jiaoyang
Received:
2021-08-10
Revised:
2022-03-10
Online:
2022-06-30
Published:
2022-06-23
摘要:
旅游审美是旅游活动的根本属性,游客审美行为影响研究对深入理解旅游活动审美本质,厘清游客审美行为驱动机制,创建“知美—悟美—尚美”的旅游新风尚,探究旅游高质量发展途径具有重要意义。本研究基于计划行为理论和认知理论,加入旅游审美认知变量,构建了游客审美行为意向影响构念模型,并采用结构方程模型展开了实证研究。研究结果表明:(1)游客审美认知、审美行为态度对旅游审美行为意向具有正向驱动作用,其中审美认知影响作用最大,是关键因素,行为态度次之,是重要因素;(2)游客主观规范、行为控制认知对审美行为态度具有正向主导作用,并通过主导行为态度间接影响审美行为意向。
中图分类号:
罗文斌, 丁德孝, 裴娇阳. 游客旅游审美行为意向的影响因素及机理研究——基于计划行为理论拓展模型[J]. 旅游导刊, 2022, 6(3): 62-85.
LUO Wenbin, DING Dexiao, PEI Jiaoyang. Influencing Factors and Mechanism of Tourists’ Intended Aesthetic Behavior: An Extended Model Based on the TPB[J]. Tourism and Hospitality Prospects, 2022, 6(3): 62-85.
潜在 变量 Latent variable | 观测变量 Observed variable | M1标准化 因子载荷 M1 Standardized factor loading | 克隆巴赫 系数 Cronbach’s α | 组合信度 CR | 平均方差萃取值 AVE | 累计方差 贡献率 Cumulative variance contribution rate |
---|---|---|---|---|---|---|
主观 规范 SN | SN1亲人和朋友支持我在旅游中慢慢品味文化和风景 | 0.716 | 0.682 | 0.683 | 0.421 | 11.995% |
SN2政府鼓励游客在景区对风景慢慢品味 | 0.555 | |||||
SN3同行朋友支持我在旅游中欣赏美景,慢慢品味文化和风景 | 0.665 | |||||
行为 控制 认知 PBC | PBC1有足够的可支配收入开展旅游审美活动 | 0.470 | 0.641 | 0.650 | 0.395 | 9.888% |
PBC2有手机、相机等设备支持我开展旅游审美活动 | 0.560 | |||||
PBC3有以往旅游审美体验的积累,能轻松获得美感 | 0.807 | |||||
审美 行为 态度 ABA | ABA1旅游审美有利于满足我的旅游需要 | 0.819 | 0.708 | 0.692 | 0.533 | 9.974% |
ABA2我支持在旅游中开展旅游审美活动 | 0.629 | |||||
审美 认知 AC | AC1我能感知自然、人文或社会景观的美 | 0.633 | 0.773 | 0.777 | 0.412 | 15.331% |
AC2我能理解景观中的内容和含义 | 0.660 | |||||
AC3看到旅游目的地的景观能使我想起一些具体的人、物或者诗词、画作等事物或沉浸在想象的情境中 | 0.659 | |||||
AC4我能从审美角度把握自然、人文及社会景观的特征 | 0.698 | |||||
AC5在欣赏旅游景观时,我能选择出最佳的拍摄角度 | 0.551 | |||||
审美 行为 意向 ABI | ABI1到了旅游目的地后,我会有主动去感知美丽的自然景观和人文景观的冲动 | 0.688 | 0.749 | 0.723 | 0.403 | 13.894% |
ABI2面对优美的风景,我愿意驻足慢慢欣赏 | 0.737 | |||||
ABI3旅游过程中欣赏沿途美丽的风景,我脑海中总会不断联想,我会用手机将其拍下来 | 0.504 | |||||
ABI4 细细欣赏美丽的风景或人物、事物,我内心会由衷感到快乐和幸福 | 0.584 |
表1 观测变量信息
Tab.1 Information table of observed variables
潜在 变量 Latent variable | 观测变量 Observed variable | M1标准化 因子载荷 M1 Standardized factor loading | 克隆巴赫 系数 Cronbach’s α | 组合信度 CR | 平均方差萃取值 AVE | 累计方差 贡献率 Cumulative variance contribution rate |
---|---|---|---|---|---|---|
主观 规范 SN | SN1亲人和朋友支持我在旅游中慢慢品味文化和风景 | 0.716 | 0.682 | 0.683 | 0.421 | 11.995% |
SN2政府鼓励游客在景区对风景慢慢品味 | 0.555 | |||||
SN3同行朋友支持我在旅游中欣赏美景,慢慢品味文化和风景 | 0.665 | |||||
行为 控制 认知 PBC | PBC1有足够的可支配收入开展旅游审美活动 | 0.470 | 0.641 | 0.650 | 0.395 | 9.888% |
PBC2有手机、相机等设备支持我开展旅游审美活动 | 0.560 | |||||
PBC3有以往旅游审美体验的积累,能轻松获得美感 | 0.807 | |||||
审美 行为 态度 ABA | ABA1旅游审美有利于满足我的旅游需要 | 0.819 | 0.708 | 0.692 | 0.533 | 9.974% |
ABA2我支持在旅游中开展旅游审美活动 | 0.629 | |||||
审美 认知 AC | AC1我能感知自然、人文或社会景观的美 | 0.633 | 0.773 | 0.777 | 0.412 | 15.331% |
AC2我能理解景观中的内容和含义 | 0.660 | |||||
AC3看到旅游目的地的景观能使我想起一些具体的人、物或者诗词、画作等事物或沉浸在想象的情境中 | 0.659 | |||||
AC4我能从审美角度把握自然、人文及社会景观的特征 | 0.698 | |||||
AC5在欣赏旅游景观时,我能选择出最佳的拍摄角度 | 0.551 | |||||
审美 行为 意向 ABI | ABI1到了旅游目的地后,我会有主动去感知美丽的自然景观和人文景观的冲动 | 0.688 | 0.749 | 0.723 | 0.403 | 13.894% |
ABI2面对优美的风景,我愿意驻足慢慢欣赏 | 0.737 | |||||
ABI3旅游过程中欣赏沿途美丽的风景,我脑海中总会不断联想,我会用手机将其拍下来 | 0.504 | |||||
ABI4 细细欣赏美丽的风景或人物、事物,我内心会由衷感到快乐和幸福 | 0.584 |
潜在变量 Latent variables | AC | PBC | SN | ABA | ABI |
---|---|---|---|---|---|
AC | 0.642 | ||||
PBC | 0.618 | 0.629 | |||
SN | 0.515 | 0.659 | 0.649 | ||
ABA | 0.376 | 0.597 | 0.634 | 0.730 | |
ABI | 0.576 | 0.534 | 0.569 | 0.569 | 0.635 |
表2 区分效度与潜在变量相关系数
Tab.2 Discriminant validity and the correlations of latent variables
潜在变量 Latent variables | AC | PBC | SN | ABA | ABI |
---|---|---|---|---|---|
AC | 0.642 | ||||
PBC | 0.618 | 0.629 | |||
SN | 0.515 | 0.659 | 0.649 | ||
ABA | 0.376 | 0.597 | 0.634 | 0.730 | |
ABI | 0.576 | 0.534 | 0.569 | 0.569 | 0.635 |
统计检验 指标类型 Statistical inspection index type | 具体指标 Specific index | 评价标准 Evaluation standard | 初始模型 M1 Initial model | 是否符合 标准 Whether it meets the standard | 修正模型 M2 Revised model | 是否符合 标准 Whether it meets the standard |
---|---|---|---|---|---|---|
绝对适配度 Absolute adaptation | CMIN/DF | <5 适合,<3 良好 | 3.376 | 是 | 1.637 | 是 |
RMSEA | ≤0.05拟合良好,≤0.08拟合合理 | 0.084 | 否 | 0.044 | 是 | |
GFI | ≥0.9 为优,≥0.8尚可接受 | 0.883 | 是 | 0.945 | 是 | |
RMR | ≤0.5 | 0.138 | 是 | 0.038 | 是 | |
增值适配度 Value-added adaptation | NFI | ≥0.9为优,≥0.8尚可接受 | 0.782 | 否 | 0.899 | 是 |
IFI | ≥0.9为优,≥0.8尚可接受 | 0.836 | 是 | 0.958 | 是 | |
TLI | ≥0.9为优,≥0.8尚可接受 | 0.798 | 否 | 0.946 | 是 | |
CFI | ≥0.9为优,≥0.8尚可接受 | 0.834 | 是 | 0.957 | 是 | |
精简适配度 Streamlined adaptation | PGFI | >0.5 | 0.646 | 是 | 0.661 | 是 |
PNFI | >0.5 | 0.644 | 是 | 0.707 | 是 |
表3 模型整体适配度指标
Tab.3 Overall fitness index of SEM
统计检验 指标类型 Statistical inspection index type | 具体指标 Specific index | 评价标准 Evaluation standard | 初始模型 M1 Initial model | 是否符合 标准 Whether it meets the standard | 修正模型 M2 Revised model | 是否符合 标准 Whether it meets the standard |
---|---|---|---|---|---|---|
绝对适配度 Absolute adaptation | CMIN/DF | <5 适合,<3 良好 | 3.376 | 是 | 1.637 | 是 |
RMSEA | ≤0.05拟合良好,≤0.08拟合合理 | 0.084 | 否 | 0.044 | 是 | |
GFI | ≥0.9 为优,≥0.8尚可接受 | 0.883 | 是 | 0.945 | 是 | |
RMR | ≤0.5 | 0.138 | 是 | 0.038 | 是 | |
增值适配度 Value-added adaptation | NFI | ≥0.9为优,≥0.8尚可接受 | 0.782 | 否 | 0.899 | 是 |
IFI | ≥0.9为优,≥0.8尚可接受 | 0.836 | 是 | 0.958 | 是 | |
TLI | ≥0.9为优,≥0.8尚可接受 | 0.798 | 否 | 0.946 | 是 | |
CFI | ≥0.9为优,≥0.8尚可接受 | 0.834 | 是 | 0.957 | 是 | |
精简适配度 Streamlined adaptation | PGFI | >0.5 | 0.646 | 是 | 0.661 | 是 |
PNFI | >0.5 | 0.644 | 是 | 0.707 | 是 |
路径 Hypothesis path | 非标准化路径系数 Unstandardized path coefficient | 标准化路径系数 Standardized path coefficient | 组合信度CR | 标准误 S.E | p值 p value | 假设检验 Support |
---|---|---|---|---|---|---|
SN→ABA | 0.517 | 0.444 | 3.643 | 0.142 | *** | 支持 |
SN→ABI | 0.188 | 0.166 | 1.402 | 0.134 | 0.161 | 拒绝 |
PBC→ABA | 0.370 | 0.362 | 2.811 | 0.132 | ** | 支持 |
PBC→ABI | -0.033 | -0.033 | -0.264 | 0.126 | 0.792 | 拒绝 |
ABA→ABI | 0.325 | 0.335 | 3.170 | 0.102 | ** | 支持 |
AC→ABA | -0.110 | -0.088 | -0.869 | 0.126 | 0.385 | 拒绝 |
AC→ABI | 0.484 | 0.399 | 3.855 | 0.126 | *** | 支持 |
表4 结构方程模型路径系数与假设检验
Tab.4 The path coefficient and hypothesis test of SEM
路径 Hypothesis path | 非标准化路径系数 Unstandardized path coefficient | 标准化路径系数 Standardized path coefficient | 组合信度CR | 标准误 S.E | p值 p value | 假设检验 Support |
---|---|---|---|---|---|---|
SN→ABA | 0.517 | 0.444 | 3.643 | 0.142 | *** | 支持 |
SN→ABI | 0.188 | 0.166 | 1.402 | 0.134 | 0.161 | 拒绝 |
PBC→ABA | 0.370 | 0.362 | 2.811 | 0.132 | ** | 支持 |
PBC→ABI | -0.033 | -0.033 | -0.264 | 0.126 | 0.792 | 拒绝 |
ABA→ABI | 0.325 | 0.335 | 3.170 | 0.102 | ** | 支持 |
AC→ABA | -0.110 | -0.088 | -0.869 | 0.126 | 0.385 | 拒绝 |
AC→ABI | 0.484 | 0.399 | 3.855 | 0.126 | *** | 支持 |
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