基于改进Kano模型与容差驱动群决策的QFD方法及文旅场景应用

doi:10.16381/j.cnki.issn1003-207x.2024.2306

摘要/Abstract

摘要：

数字文旅作为数字经济和实体经济融合的新形态，为文旅产业发展带来全新机遇。如何结合游客需求有针对性地引入数字技术赋能文旅企业转型升级，成为行业面临的新课题。为有效识别游客需求并确定技术特性的开发方向，本文基于改进的Kano模型和容差驱动的群决策提出了一种质量功能展开（QFD）方法。在顾客需求识别与权重确定阶段，首先，利用LDA主题模型和Word2vec模型从在线评论中识别顾客需求并建立人工字典；其次，基于情感分析和TF-IDF算法思想改进Kano模型并定量确定顾客需求的类别；再次，根据企业所处的发展阶段结合前景理论动态调整顾客需求的初始权重。在技术特性排序阶段，对经典群决策方法中一致性权重分配范式进行改进，用基于专家接受度的容差范围标定机制，代替传统固定一致性阈值，聚合顾客需求-技术特性关系矩阵，最终得到技术特性的共识性排序。利用某国家级博物馆的数字文旅服务设计案例验证了本方法的可行性和有效性。

关键词: 质量功能展开, Kano模型, 前景理论, 群决策, 数字文旅服务设计

Abstract:

As a new integration of the digital and real economies， digital cultural tourism presents fresh opportunities for the tourism industry. A key challenge is how to effectively leverage digital technologies to empower the transformation and upgrading of tourism enterprises based on evolving visitor needs. Quality Function Deployment （QFD） has proven to be an effective tool for translating Customer Requirements （CRs） into improvements in Technical Characteristics （TCs）. However， methodological limitations and modeling gaps remain across QFD’s core stages. In the requirement identification stage， traditional static approaches based on surveys and interviews fail to address the dynamic nature of visitor preferences； although recent applications of text mining and NLP improve automation， the mapping of extracted needs to structured design inputs remains inadequate. In the requirement weighting stage， QFD often applies the Kano model and sentiment analysis to categorize and assign weights to CRs. While this enhances objectivity， there is still no unified standard for weight modeling or classification boundary control. In the technical prioritization stage， although group decision-making and flexible modeling methods have been introduced to handle expert divergences， most studies rely on fixed consistency thresholds， lacking mechanisms and empirical validation for real-world collaborative settings.

To address these challenges， a QFD approach is proposed based on an improved Kano model and tolerance-driven group decision-making. In the requirement identification and weighting phases， customer needs are first extracted from online reviews using LDA and Word2Vec models， with a manually constructed dictionary. Then， the Kano model is enhanced by incorporating sentiment analysis and TF-IDF principles to categorize needs and assign quantitative weights. Initial weights are dynamically adjusted using prospect theory based on the enterprise’s development stage. In the technical prioritization phase， the classical group decision paradigm is refined by introducing a tolerance-based expert acceptance mechanism to replace fixed consistency thresholds. This yields expert weights and aggregated relation matrices， ultimately producing a consensus-based ranking of technical characteristics. A case study involving a national museum's digital tourism service design validates the feasibility and effectiveness of the proposed method.

This study makes the following key contributions：（1） a quantitative improvement to the Kano model that eliminates dependence on traditional Kano surveys；（2） a two-stage weight adjustment mechanism grounded in prospect theory to prevent excessive initial weight distortion； and （3） a tolerance calibration mechanism driven by expert acceptance rates， replacing theoretical thresholds and promoting a data-driven， progressively inclusive group decision process—offering new tools for QFD and similar application scenarios.

Key words: quality function deploymen, Kano model, prospect theory, group decision making, service design of digital cultural tourism

中图分类号:

C934

张阔,李辉,刘培德. 基于改进Kano模型与容差驱动群决策的QFD方法及文旅场景应用[J]. 中国管理科学, 2026, 34(10): 164-174.

Kuo Zhang,Hui Li,Peide Liu. An Improved Kano- and Tolerance-Driven Group Decision Making-Based QFD Method and its Application in Cultural and Tourism Scenarios[J]. Chinese Journal of Management Science, 2026, 34(10): 164-174.

图/表 10

图1

表1

有效指示向量与Kano质量特性间的对应关系"

$C R i ⃗$	Kano类别	$C R i ⃗$	Kano类别	$C R i ⃗$	Kano类别
$(∅, 1,1) T$	I	$(- 1, - 1, ∅) T$	I	$(∅, ∅, - 1) T$	M
$(∅, 0,0) T$	I	$(- 1, - 1, - 1) T$	I	$(∅, 0, - 1) T$	M
$(∅, - 1, - 1) T$	I	$(1, ∅, - 1) T$	O	$(0, ∅, - 1) T$	M
$(1, ∅, 1) T$	I	$(1,1, - 1) T$	O	$(0,0, - 1) T$	M
$(1,1, ∅) T$	I	$(1,0, - 1) T$	O	$(∅, - 1, - 1) T$	M
$(1,1, 1) T$	I	$(1, - 1, - 1) T$	O	$(1, ∅, ∅) T$	A
$(0, ∅, 0) T$	I	$(- 1, ∅, 1) T$	R	$(1, ∅, 0) T$	A
$(0,0, ∅) T$	I	$(- 1,1, 1) T$	R	$(1,1, 0) T$	A
$(0,0, 0) T$	I	$(- 1,0, 1) T$	R	$(1,0, ∅) T$	A
$(- 1, ∅, - 1) T$	I	$(- 1, - 1,1) T$	R	$(1,0, 0) T$	A

表1

图2

表2

顾客需求即人工词典词频"

CRs	顾客需求	人工词典词频
$C R 1$	儿童服务	725
$C R 2$	价格费用	706
$C R 3$	服务设施	594
$C R 4$	解说教育内容	783
$C R 5$	票务预约流程	360
$C R 6$	展览内容	850
$C R 7$	参观组织流程	925

表2

图3

表3

各顾客需求的指示向量与Kano类型"

参数	$C R 2$	$C R 3$	$C R 4$	$C R 5$	$C R 6$	$C R 7$
$θ X X +$	0.1527	0.1188	0.0724	$∅$	0.1003	0.1317
$θ X X 0$	0.0561	0.0399	0.0326	$∅$	0.0398	0.0386
$θ X X -$	0.0383	0.0276	0.0325	$∅$	0.0408	0.2455
$θ Y Y +$	0.0941	0.0770	0.0450	$∅$	0.0736	0.0864
$θ Y Y 0$	0.2289	0.1578	0.3043	$∅$	0.1874	0.1794
$θ Y Y -$	0.1381	0.1891	0.1302	$∅$	0.1240	0.1566
$θ Z Z +$	0.0427	0.0256	$∅$	0.1064	$∅$	0.0444
$θ Z Z 0$	0.1072	0.1265	$∅$	0.1105	$∅$	0.0957
$θ Z Z -$	0.1465	0.4964	$∅$	0.2241	$∅$	0.4261
$C R i ⃗$	$(1,0, - 1) T$	$(1,0, - 1) T$	$(1,0, ∅) T$	$(∅, ∅, - 1) T$	$(1,0, ∅) T$	$(1,0, - 1) T$
Kano类型	O	O	A	M	A	O

表3

图4

图5

表4

基于前景理论的顾客需求权重调整对比"

	调整后的顾客需求权重
	$λ 1'$	$λ 2'$	$λ 3'$	$λ 4'$	$λ 5'$	$λ 6'$	$λ 7'$
初始值	0.1467	0.1428	0.1202	0.1584	0.0728	0.1720	0.1871
Wu等^[21]	0.1824	0.1683	0.0985	0.2218	0.0000	0.2627	0.0663
Liu等^[2]	0.1467	0.1428	0.1202	0.1775	0.0728	0.2094	0.1306
本文值	0.1635	0.1254	0.1056	0.1766	0.0728	0.1917	0.1644

表4

图6

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