交互自然性的心理结构及其影响

doi:10.3724/SP.J.1041.2023.00055

摘要/Abstract

摘要：

本文通过3个研究探究了交互自然性的心理结构及其影响。研究1通过开展字典检索, 文献回顾和专家访谈, 得到了包含有9个条目的初始量表。研究2问卷调查了353名智能网联汽车用户, 探索性因素分析发现了两因素结构 (“通达舒畅”和“随景应人”)。后续分析表明这两个因素对满意度等关键效标有显著独特的预测作用。研究3使用新样本(n = 349) 验证了双因素模型的稳定性, 还发现这两个交互自然性体验维度对推荐意愿、忠诚感等重要变量也有显著预测作用, 此外还发现, 通达舒畅更多受到基本驾驶辅助系统等功能的影响, 而随景应人更多受到交互和智能相关功能的影响。本文进一步讨论了该量表如何用于未来的人机交互研究。

关键词: 交互自然性, 智能网联汽车, 心理结构, 可用性

Abstract:

This paper investigated the structure and function of interactive naturalness through three studies. After command-line interaction and graphic interaction, natural interactions have increased and become mainstream. In measuring interactive experience, the traditional usability standard is not enough to measure the usability and naturality of product interaction. The possible reason is that natural interaction is no longer limited to the product's features but is more concerned with the human experience (the perceived naturalness) or natural experience. A direct, reliable measurement tool is urgently needed to evaluate the natural experience. Most studies used traditional usability dimensions such as visibility and ease of use to simply measure naturalness. But if naturalness simply equals usability, then many traits beyond product usability cannot be measured. Some studies directly measured naturalness with a single item or synonyms of naturalness, but the reliability of these measurements is not enough. This paper aimed to develop and verify a conceptually solid and quantitatively validated scale to measure the naturalness of interaction.

In Study 1, a comprehensive item set related to the concept of natural interaction was established by using qualitative methods including dictionary retrieval, literature review, and expert interviews. In Study 2, participants (n = 353) were recruited to evaluate the experience of different intelligent connected vehicles. In addition to the interactive naturalness scale, several key consumer behaviors and traditional usability criteria, including drive intention, satisfaction, and usability, were also collected. In Study 3, new samples (n = 349) and more criterion-related variables (two key consumption behaviors were added, namely perceived loyalty and recommendation intention) were used to further verify the validity and reliability of the developed measurement tool. We used SPSS 25.0 and Jamovi 1.2.27 to analyze the data.

According to Study 1, the naturalness of the interactive experience scale of 9 items was developed based on the qualitative research. In Study 2, the exploratory factor analysis found that the two-dimensional model (six items for joyful fluency and three items for universal awareness) best suited the data. Confirmatory factor analysis verified the stability of the two-factor model. Correlation analysis and hierarchical regression analysis suggested that these two components had good criterion-related validity and joyful fluency played a crucial role in predicting satisfaction and drive intention. In Study 3, a new sample (n = 349) was used to validate the validity of the scale further. And the validity of the scale was further verified in Study 3. The criterion-related variables used in Study 2 also obtained the same correlation and regression results in Study 3. Two factors of interactive naturalness had a significant predictive effect on the newly included variables, namely recommendation intention and perceived loyalty. In addition, joyful fluency was more related to basic vehicle functions, while universal awareness was more related to advanced intelligent interaction functions.

This study explored the structure and function of interactive naturalness. A psychometrically sound tool was obtained to measure the interactive naturalness experience of intelligent products in two dimensions: joyful fluency and universal awareness. We found that interactive naturalness is strongly linked to key experiential and post-purchase dimensions and has an additional contribution to predicting these variables that traditional usability dimensions cannot include. This finding was also supported by the difference of correlation between two factors in different vehicle functions. These results indicate that the scale developed in this study can measure the natural interaction experience of intelligent products reliably and effectively. This tool can be used in future human-computer interaction research and guidance for interface and product design.

Key words: interactive naturalness, intelligent connected vehicles, psychological structure, usability

中图分类号:

B842

曹剑琴, 张警吁, 张亮, 王晓宇. (2023). 交互自然性的心理结构及其影响. 心理学报, 55(1), 55-65.

CAO Jianqin, ZHANG Jingyu, ZHANG Liang, WANG Xiaoyu. (2023). The psychological structure and influence of interactive naturalness. Acta Psychologica Sinica, 55(1), 55-65.

图/表 7

参考文献 58

[1]	Abowd G. D., & Mynatt E. D. (2000). Charting past, present, and future research in ubiquitous computing. ACM Transactions on Computer-Human Interaction (TOCHI), 7(1), 29-58. doi: 10.1145/344949.344988 URL
[2]	Agarwal R., Sampath H. A., & Indurkhya B. (2013). A usability study on natural interaction devices with ASD children. International Conference on Universal Access in Human- Computer Interaction (pp. 447-453). Springer, Berlin, Heidelberg.
[3]	Almeida L., Lopes E., Yalçinkaya B., Martins R., Lopes A., Menezes P., & Pires G. (2019, October). Towards natural interaction in immersive reality with a cyber-glove. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp.2653-2658). IEEE.
[4]	Atiyah A., Jusoh S., & Alghanim F. (2019, April). Evaluation of the naturalness of Chatbot Applications. 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp.359-365). IEEE. DOI: 10.1109/SMC.2019.8914239. doi: 10.1109/SMC.2019.8914239
[5]	Bailey S. K., & Johnson C. I. (2020, July). A human-centered approach to designing gestures for natural user interfaces. In International Conference on Human-Computer Interaction (pp.3-18). Springer, Cham. https://doi.org/10.1007/978-3-030-49062-1_1
[6]	Bailey S. K., Johnson C. I., & Sims V. K. (2018, August). Using natural gesture interactions leads to higher usability and presence in a computer lesson. In Congress of the International Ergonomics Association (pp.663-671). Springer, Cham. https://doi.org/10.1007/978-3-319-96065-4_70.
[7]	Bassano C., Chessa M., & Solari F. (2020, February). A study on the role of feedback and interface modalities for natural interaction in virtual reality environments. In the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) (2: HUCAPP), (pp.154-161). Science and Technology Publications. DOI: 10.5220/0008963601540161 doi: 10.5220/0008963601540161
[8]	Biever W., Angell L., & Seaman S. (2020). Automated driving system collisions: Early lessons. Human Factors: The Journal of the Human Factors and Ergonomics Society, 62(2), 249-259. https://doi.org/10.1177/0018720819872034 doi: 10.1177/0018720819872034 URL
[9]	Black H. G., Vincent L. H., & Skinner S. J. (2015). Customers helping customers:Payoffs for linking customers in service settings. In Marketing Dynamism & Sustainability:Things Change, Things Stay the Same. (pp.179-179). Springer.
[10]	Brooke J. (1996). SUS: A “quick and dirty’usability. Usability Evaluation in Industry, 189(194), 4-7.
[11]	Brunswicker S., & Chesbrough H. (2018). The adoption of open innovation in large firms: Practices, measures, and risks a survey of large firms examines how firms approach open innovation strategically and manage knowledge flows at the project level. Research-Technology Management, 61(1), 35-45.
[12]	Chuah S. H.-W., Rauschnabel P. A., Krey N., Nguyen B., Ramayah T., & Lade S. (2016). Wearable technologies: The role of usefulness and visibility in smartwatch adoption. Computers in Human Behavior, 65, 276-284. https://doi.org/10.1016/j.chb.2016.07.047 doi: 10.1016/j.chb.2016.07.047 URL
[13]	Crawford A. V., Green S. B., Levy R., Lo W.-J., Scott L., Svetina D., & Thompson M. S. (2010). Evaluation of parallel analysis methods for determining the number of factors. Educational and Psychological Measurement, 70(6), 885-901. doi: 10.1177/0013164410379332 URL
[14]	de Visser E. J., Krueger F., McKnight P., Scheid S., Smith M., Chalk S., & Parasuraman R. (2012). The world is not enough: Trust in cognitive agents. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 56(1), 263-267. doi: 10.1177/1071181312561062 URL
[15]	Falcao C., Lemos A. C., & Soares M. (2015). Evaluation of natural user interface: A usability study based on the leap motion device. Procedia Manufacturing, 3, 5490-5495. doi: 10.1016/j.promfg.2015.07.697 URL
[16]	Franklin S. B., Gibson D. J., Robertson P. A., Pohlmann J. T., & Fralish J. S. (1995). Parallel analysis: A method for determining significant principal components. Journal of Vegetation Science, 6(1), 99-106. doi: 10.2307/3236261 URL
[17]	Grandhi S. A., Joue G., & Mittelberg I. (2011). Understanding naturalness and intuitiveness in gesture production:Insights for touchless gestural interfaces. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 821-824.
[18]	Grisaffe D. B. (2007). Questions about the ultimate question: Conceptual considerations in evaluating Reichheld’s net promoter score (NPS). Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 20, 36.
[19]	Hayton J. C., Allen D. G., & Scarpello V. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7(2), 191-205. doi: 10.1177/1094428104263675 URL
[20]	Helander M. G., Khalid H. M., Lim T. Y., Peng H., & Yang X. (2013). Emotional needs of car buyers and emotional intent of car designers. Theoretical Issues in Ergonomics Science, 14(5), 455-474. doi: 10.1080/1463922X.2012.656152 URL
[21]	Hsiao K.-L. (2017). What drives smartwatch adoption intention? Comparing apple and non-apple watches. Library Hi Tech, 35(1), 186-206. https://doi.org/10.1108/LHT-09-2016-0105 doi: 10.1108/LHT-09-2016-0105 URL
[22]	Jain J., Lund A., & Wixon D. (2011). The future of natural user interfaces. In CHI’11 Extended Abstracts on Human Factors in Computing Systems (pp. 211-214).
[23]	Kamide H., Mae Y., Kawabe K., Shigemi S., Hirose M., & Arai T. (2012, March). New measurement of psychological safety for humanoid. In 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 49-56). IEEE. doi: 10.1145/2157689.2157698. doi: 10.1145/2157689.2157698
[24]	Khalid H. M., Opperud A., Radha J. K., Xu Q., & Helander M. G. (2012). Elicitation and analysis of affective needs in vehicle design. Theoretical Issues in Ergonomics Science, 13(3), 318-334. doi: 10.1080/1463922X.2010.506559 URL
[25]	Kharoub H., Lataifeh M., & Ahmed N. (2019). 3D user interface design and usability for immersive VR. Applied Sciences, 9(22), 4861.
[26]	Kortum P. T., & Bangor A. (2013). Usability ratings for everyday products measured with the system usability scale. International Journal of Human-Computer Interaction, 29(2), 67-76. doi: 10.1080/10447318.2012.681221 URL
[27]	Kuang X., Zhao F., Hao H., & Liu Z. (2019). Assessing the socioeconomic impacts of intelligent connected vehicles in China: A cost-benefit analysis. Sustainability, 11(12), 3273.
[28]	Ledesma R. D., & Valero-Mora P. (2007). Determining the number of factors to retain in EFA: An easy-to-use computer program for carrying out parallel analysis. Practical Assessment, Research, and Evaluation, 12(1), 2.
[29]	Lewis J. R. (1992, October). Psychometric evaluation of the post-study system usability questionnaire: The PSSUQ. Proceedings of the Human Factors Society Annual Meeting, 36(16), 1259-1260. doi: 10.1177/154193129203601617 URL
[30]	Liljamo T., Liimatainen H., & Pöllänen M. (2018). Attitudes and concerns on automated vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 59, 24-44. https://doi.org/10.1016/j.trf.2018.08.010 doi: 10.1016/j.trf.2018.08.010 URL
[31]	Li Y.-M., & Yeh Y.-S. (2010). Increasing trust in mobile commerce through design aesthetics. Computers in Human Behavior, 26(4), 673-684. doi: 10.1016/j.chb.2010.01.004 URL
[32]	Luo Z., Chen S., Management S. O., & University J. (2018). A literature review of social support in marketing and prospects. Foreign Economics & Management, 40(1), 18-32.
[33]	McDonagh D., & Lebbon C. (2000). The emotional domain in product design. The Design Journal, 3(1), 31-43. doi: 10.2752/146069200789393562 URL
[34]	Montoro L., Useche S. A., Alonso F., Lijarcio I., Bosó- Seguí P., & Martí-Belda A. (2019). Perceived safety and attributed value as predictors of the intention to use autonomous vehicles: A national study with Spanish drivers. Safety Science, 120, 865-876. https://doi.org/10.1016/j.ssci.2019.07.041 doi: 10.1016/j.ssci.2019.07.041 URL
[35]	Moons I., & de Pelsmacker P. (2015). An extended decomposed theory of planned behaviour to predict the usage intention of the electric car: A multi-group comparison. Sustainability, 7(5), 6212-6245. doi: 10.3390/su7056212 URL
[36]	National Highway Traffic Safety Administration. (2016). Federal automated vehicles policy: Accelerating the next revolution in roadway safety. US Department of Transportation.
[37]	Nielsen J. (1994). Usability engineering. Morgan Kaufmann.
[38]	Norman D. A. (2010). Natural user interfaces are not natural. Interactions, 17(3), 6-10.
[39]	Norman D. A., & Nielsen J. (2010). Gestural interfaces: A step backward in usability. Interactions, 17(5), 46-49. doi: 10.1145/1836216.1836228 URL
[40]	Pak R., Fink N., Price M., Bass B., & Sturre L. (2012). Decision support aids with anthropomorphic characteristics influence trust and performance in younger and older adults. Ergonomics, 55(9), 1059-1072. doi: 10.1080/00140139.2012.691554 pmid: 22799560
[41]	Podsakoff P. M., MacKenzie S. B., Lee J.-Y., & Podsakoff N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879.
[42]	Pütz F., Murphy F., & Mullins M. (2019). Driving to a future without accidents? Connected automated vehicles’ impact on accident frequency and motor insurance risk. Environment Systems and Decisions, 39(4), 383-395. doi: 10.1007/s10669-019-09739-x URL
[43]	Pyrialakou V. D., Gkartzonikas C., Gatlin J. D., & Gkritza K. (2020). Perceptions of safety on a shared road: Driving, cycling, or walking near an autonomous vehicle. Journal of Safety Research, 72, 249-258. https://doi.org/10.1016/j.jsr.2019.12.017 doi: S0022-4375(19)30675-9 URL pmid: 32199570
[44]	Rajendran K., & Jayakrishnan J. (2018). Consumer perceived risk in car purchase. Journal on Management Studies, 4(2).
[45]	Roupe M., Bosch-Sijtsema P., & Johansson M. (2014). Interactive navigation interface for Virtual Reality using the human body. Computers Environment & Urban Systems, 43(Jan.), 42-50.
[46]	SAE On-Road Automated Vehicle Standards Committee. (2014). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (Surface Vehicle Recommended Practice: Superseding J3016 Jan 2014). SAE International: Warrendale, PA, USA.
[47]	Shi Y. (2018). Natural human-computer interaction. Communications of the CCF, 14(5), 8-10.
	[史元春. (2018). 自然人机交互. 中国计算机学会通讯, 14(5), 8-10.]
[48]	Urakami J., Sutthithatip S., & Moore B. A. (2020). The effect of naturalness of voice and empathic responses on enjoyment, attitudes and motivation for interacting with a voice user interface. International Conference on Human-Computer Interaction, 244-259.
[49]	Vallejo V., Tarnanas I., Yamaguchi T., Tsukagoshi T., Yasuda R., Müri R., Mosimann U. P., & Nef T. (2016). Usability assessment of natural user interfaces during serious games: Adjustments for dementia intervention. J Pain Management, 9, 333-339.
[50]	Verberne F. M., Ham J., & Midden C. J. (2012). Trust in smart systems: Sharing driving goals and giving information to increase trustworthiness and acceptability of smart systems in cars. Human Factors, 54(5), 799-810. pmid: 23156624
[51]	Villaroman N., Rowe D., & Swan B. (2011, October). Teaching natural user interaction using OpenNI and the Microsoft Kinect sensor. Paper presented at the meeting of the 2011 Conference on Information Technology Education, West Point, NY.
[52]	Wang H., Law K. S., Hackett R. D., Wang D., & Chen Z. X. (2005). Leader-member exchange as a mediator of the relationship between transformational leadership and followers’ performance and organizational citizenship behavior. Academy of Management Journal, 48(3), 420-432. doi: 10.5465/amj.2005.17407908 URL
[53]	Wernerfelt B. (1991). Brand loyalty and market equilibrium. Marketing Science, 10(3), 229-245. doi: 10.1287/mksc.10.3.229 URL
[54]	Wigdor D., & Wixon D. (2011). Brave NUI world: Designing natural user interfaces for touch and gesture. ACM SIGSOFT Software Engineering Notes, 36(6), 29-30.
[55]	Xu X. (2019). Value creation for intelligent connected vehicles:An industry value-chain perspective. In Digital Business Models (pp. 57-79). Springer.
[56]	Zhang J., & Zhang L. (2018). Cognitive mechanism and mental model of natural interaction. Communications of the CCF, 14(5), 30-35.
	[张警吁, 张亮. (2018). 自然交互的认知机理与心理模型. 中国计算机学会通讯, 14(5), 30-35.]
[57]	Zhou B., Hu H., & Dai L. (2020). Assessment of the development of time-sharing electric vehicles in Shanghai and subsidy implications: A system dynamics approach. Sustainability, 12(1), 345.
[58]	Zhou T. (2011). The effect of initial trust on user adoption of mobile payment. Information Development, 27(4), 290-300. doi: 10.1177/0266666911424075 URL

方法	条目及描述	来源	访谈提及频率
字典检索	符合预期: 这个系统的使用方式与期望一致。	Naturally：来自牛津高阶英语词典 (第四版), 现代汉语词典 (第七版) Natural：来自维基百科	50%
文献总结	人性化: 与该系统的交互像与人交互一样自然。	Abowd & Mynatt, 2000; Nielsen, 1994	50%
	愉悦感: 当我使用这个系统的时候感觉心情愉快。	Brooke, 1996; Lewis, 1992; Norman & Nielsen, 2010	25%
	情景适宜性: 该系统能根据当前情景做出适宜的反应。	Biever et al., 2020; Liljamo et al., 2018	25%
	流畅性: 用户以自己的方式进行操作而不会被打断, 感受到操作的流畅性。	Lewis, 1992; Norman & Nielsen, 2010	50%
字典& 文献总结	普适性: 任何人(包括儿童)都能自如操作这套系统。	Naturally, Natural: 来自维基百科, 牛津高阶英语词典(第四版); Brooke, 1996; Lewis, 1992; Norman & Nielsen, 2010	62.5%
	自然的: 使用车载系统的整体过程感到很顺手而不突兀。	Nature, Naturally, Natural：来自维基百科, 韦氏词典 (第三版), 牛津高阶英语词典 (第四版); Abowd & Mynatt, 2000	100%
	易学性: 很容易学会使用系统的各种功能, 无需求助他人。	Naturally: 来自牛津高阶英语词典 (第四版); Brooke, 1996; Lewis, 1992; Norman & Nielsen, 2010; Roupe et al., 2014	100%
	合理性: 该系统的使用方式符合正常的逻辑。	Natural: 来自维基百科, 牛津高阶英语词典(第四版); Abowd & Mynatt, 2000; Nielsen, 1994	87.5%

方法	条目及描述	来源	访谈提及频率
字典检索	符合预期: 这个系统的使用方式与期望一致。	Naturally：来自牛津高阶英语词典 (第四版), 现代汉语词典 (第七版) Natural：来自维基百科	50%
文献总结	人性化: 与该系统的交互像与人交互一样自然。	Abowd & Mynatt, 2000; Nielsen, 1994	50%
	愉悦感: 当我使用这个系统的时候感觉心情愉快。	Brooke, 1996; Lewis, 1992; Norman & Nielsen, 2010	25%
	情景适宜性: 该系统能根据当前情景做出适宜的反应。	Biever et al., 2020; Liljamo et al., 2018	25%
	流畅性: 用户以自己的方式进行操作而不会被打断, 感受到操作的流畅性。	Lewis, 1992; Norman & Nielsen, 2010	50%
字典& 文献总结	普适性: 任何人(包括儿童)都能自如操作这套系统。	Naturally, Natural: 来自维基百科, 牛津高阶英语词典(第四版); Brooke, 1996; Lewis, 1992; Norman & Nielsen, 2010	62.5%
	自然的: 使用车载系统的整体过程感到很顺手而不突兀。	Nature, Naturally, Natural：来自维基百科, 韦氏词典 (第三版), 牛津高阶英语词典 (第四版); Abowd & Mynatt, 2000	100%
	易学性: 很容易学会使用系统的各种功能, 无需求助他人。	Naturally: 来自牛津高阶英语词典 (第四版); Brooke, 1996; Lewis, 1992; Norman & Nielsen, 2010; Roupe et al., 2014	100%
	合理性: 该系统的使用方式符合正常的逻辑。	Natural: 来自维基百科, 牛津高阶英语词典(第四版); Abowd & Mynatt, 2000; Nielsen, 1994	87.5%

条目	因子1: 通达舒畅	因子2: 随景应人
1. 流畅的	0.68
2. 自然的	0.61
3. 可学习的	0.45
4. 愉悦感	0.43
5. 合理性	0.42
6. 符合预期	0.41
7. 普适性		0.58
8. 情景适宜性		0.45
9. 人性化		0.42
方差解释百分比	20.0	12.0
累积方差解释百分比	32.1

条目	因子1: 通达舒畅	因子2: 随景应人
1. 流畅的	0.68
2. 自然的	0.61
3. 可学习的	0.45
4. 愉悦感	0.43
5. 合理性	0.42
6. 符合预期	0.41
7. 普适性		0.58
8. 情景适宜性		0.45
9. 人性化		0.42
方差解释百分比	20.0	12.0
累积方差解释百分比	32.1

效标变量	M (SD)	因子1: 通达舒畅	因子2: 随景应人	量表总分
1. 可用性	4.34 (0.49)	0.44^**	0.13^*	0.25^**
2. 使用意愿	5.64 (0.76)	0.35^**	0.13^*	0.20^**
3. 满意度	5.54 (0.66)	0.39^**	0.25^**	0.31^**