Physician voice characteristics and patient satisfaction in online health consultation

Journal Pre-proof Physician Voice Characteristics and Patient Satisfaction in Online Health Consultation Shan Liu, Muyu Zhang, Baojun Gao, Guoyin Jiang

PII:

S0378-7206(18)30688-8

DOI:

https://doi.org/10.1016/j.im.2019.103233

Reference:

INFMAN 103233

To appear in:

Information & Management

Received Date:

19 August 2018

Revised Date:

10 November 2019

Accepted Date:

11 November 2019

Please cite this article as: Liu S, Zhang M, Gao B, Jiang G, Physician Voice Characteristics and Patient Satisfaction in Online Health Consultation, Information and amp; Management (2019), doi: https://doi.org/10.1016/j.im.2019.103233

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Physician Voice Characteristics and Patient Satisfaction in Online Health Consultation

Dear Editor of Information & Management, Enclosed is a revised manuscript, entitled “Physician Voice Characteristics and Patient Satisfaction in Online Health Consultation.” Please accept it as a candidate for publication in the Information & Management.

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This study examines the collective effects of voice and physician characteristics on patient satisfaction based on 35,597 voice-based medical services provided by physicians in a mobile health community. Results show patient satisfaction is positively influenced by the physician’s speech rate but negatively affected by the average spectral centroid of consultation voice. A fast speaker and speech with neural emotion are more likely to be associated with higher patient satisfaction than a slow speaker and speech with low and high emotion states. However, these effects are weak for physicians with high professional capital, which suggests a substitute role for voice characteristics. Finally, this paper is our original unpublished work and has not been submitted to any other journal for reviews. Thank you very much for considering our submission!

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Yours sincerely,

Shan Liua, Muyu Zhanga, Baojun Gaob*, Guoyin Jiangc a School

of Management, Xi’an Jiaotong University, Xi’an, China 710049 and Management School, Wuhan University, Wuhan, China 430072 c School of Public Administration, University of Electronic Science and Technology of China, Chengdu, China 611731

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b Economics

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Physician Voice Characteristics and Patient Satisfaction in Online Health Consultation Shan Liua, Muyu Zhanga, Baojun Gaob*, Guoyin Jiangc a School

of Management, Xi’an Jiaotong University, Xi’an, China 710049 and Management School, Wuhan University, Wuhan, China 430072 c School of Public Administration, University of Electronic Science and Technology of China, Chengdu, China 611731

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b Economics

Shan Liu

Tel: +86-15807106158 Email address: [email protected] Mailing address: School of Management School, Xi’an Jiaotong University, Xi’an 710049 China Muyu Zhang Email address: [email protected] 1

Mailing address: School of Management School, Xi’an Jiaotong University, Xi’an 710049 China Baojun Gao* (Corresponding author) Email address: [email protected] Mailing address: Economics and Management School, Wuhan University, Wuhan, China 430072 Guoyin Jiang Email address: [email protected] Mailing address: School of Public Administration, University of Electronic Science and Technology of China, Chengdu, China 611731

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Abstract This study examines the collective effects of voice and physician characteristics on patient satisfaction based on 35,597 voice-based medical services provided by physicians in a mobile

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health community. Results show patient satisfaction is positively influenced by the physician’s speech rate but negatively affected by the average spectral centroid of

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consultation voice. A fast speaker and speech with neural emotion are more likely to be associated with higher patient satisfaction than a slow speaker and speech with low and high

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emotion states. However, these effects are weak for physicians with high professional capital, which suggests a substitute role for voice characteristics.

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Keywords: Mobile consultation; voice characteristic; speech rate; average spectral centroid;

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patient satisfaction; professional capital

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1. Introduction With the proliferation of smartphones and communication technology, mobile health communities have become increasingly significant in the provision of online health consultation services for patients anytime and anywhere [1, 2]. In such communities, patients seek suitable physicians they want to consult with and provide descriptions of their health conditions on the application terminal of the community. The physician then responds and provides diagnosis and advices based on their descriptions. The process of physician–patient

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interaction commonly involves at least three consultation patterns, namely, written, telephone, and voice consultations [3, 4]. The voice consultation is increasingly popular with the wide application of the mobile speech function [4]. Compared with written consultation, the use of

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voice can enhance the interaction rate and clearly deliver the empathy of physicians to patients [5]. Voice consultation also outshines telephone consultation because the former not

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only has lower cost but also allows physicians to submit deliberate responses by giving them

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enough time for assessing the health condition of patients.

Although the interaction pattern of voice consultation has evident advantages, the influence of the critical voice characteristics of physicians on patient satisfaction in such an

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online health consultation is unclear. The majority of prior studies examines patient satisfaction from the perspective of service process or are based on written content [6-9] but

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fail to show solicitude for voice. The understanding of this issue is significant because

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physicians can identify appropriate approaches to use voice in improving the satisfaction of patients, and mobile health communities can optimize voice functions to enhance online interactions between physicians and patients. Therefore, this study aims to explore how the characteristics of physicians’ voice affect patient satisfaction in online health consultation developed by mobile health communities. Voice characteristic is one of the most basic features of human beings and has attracted

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wide attention. Previous research has corroborated that the speech features of service providers, such as acoustic pitch and vocal attractiveness, significantly affect customer perception of service quality, product evaluation, satisfaction, and purchase intention [10-12]. These studies focus on the field of marketing and the economic aspect of services resulting from voice characteristics. However, online health consultation, the focus of our study, significantly differs from the context of marketing. Online medical service is associated with the basic quality of life and mortality. Thus, the effectiveness of service interactions between

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physicians and patients is more important than other types of services [6, 13]. In online health consultation, physicians not only provide informational support (e.g., recommending appropriate drugs to patients), but also offer emotional support to patients.

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Such a support contains nurturance and warmth, including encouragement, empathy, trust, affection, and other positive aspects that can reduce the pressure or other negative emotions

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in patients [14, 15]. Physicians and patients are also limited in non-face-to-face consultation

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(i.e., only the written text or voice and no expression and action), which requires a high level of social care from physicians [16]. Thus, the exploration of voice characteristics is significant and unique in online health consultation because patient satisfaction involves

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considerable public welfare attributes and social benefits. Such an investigation can help physicians to take advantage of their voice, improve the quality of intangible and emotional

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supports for patients, and develop good physician–patient relationships.

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The effects of the characteristics of physician consultation voice on patient satisfaction may vary because of the heterogeneity of physicians. However, the roles of some key physician characteristics, such as professional capital, are rarely discussed. Professional capital is a type of special, rare, long-term, and valuable capital that belongs to social professionals, as it is related to the strength of power (in structural society, physicians are “born” to be dependent) [17, 18]. The professional capital reflects the ability and status of a

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physician and may be the most important factor that affects the physician’s choice of patients [19]. In the interaction between physicians and patients, the information provided by the physician is certified. The strong perception of high professional capital, such as the aura of the moon form, diffuses to the surrounding and may conceal other characteristics (e.g., voice) of physicians [20]. Therefore, this research attempts to investigate how the professional capital of physicians influences the effects of their voice characteristics on patient satisfaction in online health consultation.

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Voice data offer rich information that reflects the characteristics and attitude of service providers toward service receivers. However, only several basic acoustic features have been considered in the past research, such as acoustic pitch, tone, and loudness [11, 21, 22]. With

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the rapid development of speech recognition technology, scientists excavate additional advanced indicators (e.g., spectral centroid, an indicator that measures the “brightness” of

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voice) from the spectrum of voice, which describes the individual speech better [23]. The

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current research focuses on two significant advanced indicators, namely, speech rate and average spectral centroid, which affect the hearing and physical feelings of listeners [24, 25] but have been rarely examined in previous research.

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We investigate two voice characteristics for several reasons. First, online health consultation provides two types of social support for patients, namely informational and

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emotional, which may significantly influence patient satisfaction [26-28]. Among voice

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characteristics, speech rate relates to informational support because speech rate reflects the amount of information revealed [29]. Average spectral centroid is associated with emotional support because the former is the barycenter of the voice spectrum and displays the sentimental state of the speaker [24, 30]. Therefore, these two characteristics of voice play important roles in online health consultation. Second, speech rate and average spectral centroid are the most critical features of voice [25, 31]. While speech rate reflects the length

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of voice, average spectral centroid captures the voice spectrum or range. Length and spectrum constitute the two basic elements of voice. Third, other voice characteristics, such as average fundamental frequency and average short-time zero-crossing rate, are unlikely to be associated with the information and emotion of online health consultation and thus, may not be as representative as speech rate and average spectral centroid. Therefore, this study explores the influences of speech rate and average spectral centroid. We aim to address the following research questions:

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(1) How do the critical voice characteristics of physicians (i.e., speech rate and average spectral centroid) influence patient satisfaction in online health consultation?

(2) How does the professional capital of physicians change the relationship between

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physician voice characteristics and patient satisfaction?

To address these research questions, we collected physician consultation voice and

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patient ratings from online consultation services of the “Chun Yu Physician” app (the largest

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mobile health community in China). We employ speech recognition and spectral analysis to extract the characteristics of physician consultation voice. We also develop an empirical model to test our hypotheses. Our economic specification model examines the patient rating

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of each consultation service as a function of the speech rate and average spectral centroid by controlling certain factors of physician and consultation levels. We also perform a robustness

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check and additional analysis to enrich our study.

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We identify several significant findings. Physicians with high speech rate and neutral average spectral centroid are very likely to satisfy patients in online health consultations. However, the effects of speech rate and average spectral centroid are weak for physicians with high professional capital. Our robustness check further reveals that the effect of speech rate is not positive when physicians speak too fast. Such an effect is insignificant for physicians with a senior title or working in Grade-A tertiary hospitals. Speech rate and

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average spectral centroid also interact with each other to negatively influence patient satisfaction. This result indicates that high speech rate (but not extremely too fast) in a neutral emotional state is optimal for physicians in online health consultations. This research adds novel insights into the literature on online health by investigating patient satisfaction from the perspective of voice characteristics. We extend the voice indicators in speech recognition to the context of online health consultation. We also offer a new perspective to service management literature by determining the effects of two service

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interaction characteristics ignored in service management, namely, the speech rate and average spectral centroid. We identify two acoustic features significant in online expert service. In addition, this study provides insights into the research on service provider

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characteristics by examining the moderating role of physician professional capital. We reveal the joint effects of service providers and their voice characteristics on service outcomes.

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This study is structured as follows. Section 2 presents the literature and the testable

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hypotheses for subsequent empirical analyses. Section 3 discusses our data and research methodology. Section 4 provides the empirical results, robustness check, and additional analysis. Sections 5 and 6 discuss and summarize our findings, respectively.

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2. Literature Review and Hypothesis Development Online health consultation allows patients to obtain satisfactory medical services at anytime

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and anywhere [1, 32]. Patient satisfaction refers to the degree to which patients feel satisfied

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with the health service provided by the physician [33]. With the prevalence of “patientcentered” philosophy [34], the question of how to improve patient satisfaction has received increasing attention in the research of health community. Scholars have also identified various consultation and physician characteristics that may affect patient satisfaction. In the context of written consultation, researchers have intensively investigated the influence of physician service delivery process [6], interpersonal trust [9], and service pricing [7] on

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patient satisfaction. However, previous studies have rarely focused on voice consultation. The use of voice consultation provides numerous voice data that differ from the traditional text data. Voice consultation has unique features and varies from person to person [35]. It generally involves considerable information and clearly shows empathy and emotional support to patients. Considering the advantages of voice consultation, this study aims to explore the mechanism of how physician voice characteristics influence patient satisfaction. 2.1. The Effect of Physician Voice Characteristics

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Previous research corroborates that the social support from physicians is associated closely with patient satisfaction and improves the health conditions of patients [8, 26-28]. Social support denotes the assistance and care from others in a social interaction [36, 37]. Two

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significant types of social support in online health consultation are proposed, namely, informational and emotional support [27, 38]. Informational support provides advice,

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knowledge, suggestions, and guidance to assist patients in addressing their issues. Emotional

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support refers to the offer of emotional concerns, such as trust, encouragement, empathy, understanding, and caring [39, 40]. Among voice characteristics of physicians, two significant characteristics (i.e., speech rate and average spectral centroid) associated closely

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with informational and emotional support emerge. While speech rate may reflect the amount of information and knowledge that physicians possess, average spectral centroid captures the

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emotions of physicians [24, 41]. Although previous literature documents the effectiveness of

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the two types of social support, the understanding of informational and emotional support from the perspective of voice is lacking. Speech rate is one of the most important phonetic features. This speech feature is

measured by the number of speech units of a given type, produced within a given amount of time [25]. During online consultation, fast speakers often provide sufficient information for patients who may feel high levels of support because of considerable communication and

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suggestions provided by physicians [42, 43]. Physicians with a higher speech rate exhibit more care for patients because such physicians provide information with urgency [44]. Fast speakers are regarded as more persuasive than slow speakers, because the speech rate functions as a credibility cue by indicating that the speaker has more knowledge and expertise [45]. A potential concern is that speaking fast may reduce comprehension, which may deteriorate the quality of information. However, a previous study has found that fast speaking in the normal range apparently does not interfere enough with reception to disrupt

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comprehension [45]. Moreover, fast statements elicit more attention and effectively induce changes in attitude or behavior [46]. In sum, physicians with high speech rate may provide a large amount of information with care, attention, and expertise in a persuasive manner that

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provides potential considerable support for patients. In such situations, patients are likely to

propose the first hypothesis as follows:

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feel that physicians with high speech rate are helpful and will satisfy their concerns. Thus, we

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H1. The speech rate of physician consultation voice positively influences patient satisfaction such that a high speech rate of physicians is associated with a high level of patient satisfaction.

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Another characteristic of consultation voice is average spectral centroid, which may affect patient satisfaction in a way that provides emotional support. Consistent with previous

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literature, we divide the spectrum of voice into multiple segments and use the mean value of

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spectral centroid in each segment to denote it [47]. Spectral centroid refers to “the center of gravity of the power spectrum.” [30] It is an average frequency weighted by the normalized energy values of each frequency component in the spectrum, and has a robust correlation with the impression of “brightness” of a sound [48, 49]. Prior research confirms that the spectral features (e.g., spectral centroid) of voice depend on the speaker and the emotional state of speaking [30]. Thus, emotion plays a pivotal role in the formation of spectral centroid.

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With the enhancement of the average spectral centroid, the emotional state of speakers is changed from neutral to joy, and when it rises to a high degree, the emotional state turns to anger [24]. Therefore, the average spectral centroid of consultation voice reflects the emotional state of physicians when they provide medical service in online health consultation. On the basis of the emotion regulation view, average spectral centroid, which reflects physician service emotion, significantly affects patient satisfaction. Emotion regulation view contends that individuals should pay more attention to regulate positive or negative emotion

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expressions without damaging the relationship with others in effective social interactions [50, 51]. Physician–patient communication is one of the most important social interactions, and patient evaluation of consultation experience is associated with physicians’ emotional state.

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Physicians with low levels of emotion control and increased anger and anxiety lead to suspicions in their professional capabilities and frequent complaints of patients [52]. Such

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physicians are also likely to report medical errors [53]. On the contrary, low average spectral

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centroid shows high empathy and thus is evaluated as positive by patients, who express high satisfaction and compliance with physicians’ medical advices [54]. Previous studies also indicate that patients are sensitive to physician emotion and always want them to render

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service in a serious attitude and express sympathy in the neutral emotional state [55-57]. Therefore, with high average spectral centroid, whatever the emotional state of a physician,

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the patient satisfaction might be reduced. Thus, we hypothesize the following:

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H2. The average spectral centroid of physician consultation voice negatively influenced patient satisfaction, such that high average spectral centroid of physician voice was associated with low-level patient satisfaction. 2.2. The Moderating Effects of Physician Professional Capital

The existing literature has investigated the impacts of the characteristics of service providers (e.g., gender, title, age, and service attitude) on service receiver satisfaction [6, 57]. The same

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service delivered by service providers with different characteristics can cause diverse satisfaction in service receivers. Previous research also proves that professional capital is a significant element that reflects the ability and status characteristics of service providers, as well as impacts their choice behavior and satisfaction [19]. It is defined as “a set of renewable resources developed by good education within a territory of social practice, and belongs to social professionals (e.g., teachers, physicians, and lawyers).” [17] In the context of online health consultation, the professional capital of physicians can be

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represented by their clinical titles, working years, and hospitals of employment. When patients surf on the mobile health community and choose the suitable physician they want to consult, these type of official certifications (e.g., professional titles, working years, and

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related organizations and institutions) assist them in evaluating the individual and social advantages of physicians and in making decisions on the choice behavior of medical service

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[19]. Thus, in the case of limited understanding on physicians, professional capital is the

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main factor that patients take into consideration. According to the theory of halo effect, the strong perception of a person’s main characteristics influences and weakens the perception of their secondary features. In other words, as a main characteristic of physicians, professional

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capital conceals the influence of other secondary characteristics, such as voice characteristic on patients, and this concealment effect increases with the improvement of physician

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professional capital [20]. Therefore, when patients consult with a physician of high

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professional capital, their attention may focus on the physician title, working years, and hospital grade and weakens the influence of voice characteristics. Accordingly, on the basis of the analysis presented above, we hypothesize the following: H3. Physician professional capital negatively moderates the positive effect of speech rate on patient satisfaction in online health consultation such that the positive effect is weaker for physicians with higher professional capital.

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H4. Physician professional capital positively moderates the negative effect of average spectral centroid on patient satisfaction in online health consultation such that the negative effect is weak for physicians with high professional capital. Figure 1 presents the research model. Voice Characteristic

Physician Characteristic H1 (+) Informational

Speech rate H3 (-) Professional capital

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H4 (+)

Patient satisfaction

Average spectral centroid

H2 (-) Emotional

Figure 1. Research Model

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3. Research Methodology 3.1. Data Acquisition and Processing

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Data were collected from “Chun Yu Physician,” one of the largest mobile health communities

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in China. Founded in July 2011, the site has attracted over 92 million users and 500,000 registered physicians as of January 2017. This mobile community offers an interaction

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platform where patients and physicians can communicate with each other through voice. Patients seek a suitable physician for consultation, provide details on their health conditions

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and ask questions. Then, physicians provide medical suggestions through a voice message. The abundant voice data lay a foundation for further analysis.

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Figure 2 shows the process of data acquisition and processing. First, we develop a Python program to automatically scrape the personal information of physicians who use voice to consult. Figure 3 shows that for each physician, we collected many items, including physician name, department, title, working years (extract from physicians’ label or educational background), service price, consultation times, favorable rating, hospital, peer evaluation, and specialization.

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2. Collected all the service data from each physician’s consultation page.

4. Calculate the average spectral centroid and other two spectrum characteristics.

3. Calculate the speech rate by using the technology of speech recognition.

Physician Information Data Set

……

1

1_10

…

…

...

…

Ave spectral centroid

……

3

3.65

745.06

……

…

38

Speech rate

…

1

Satisfaction

…

1

…

Doctor Service ID ID

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……

…

Doctor Working Title ID years

Voice Characteristic Data Set

…

1. Scrape the personal information of physicians who use the voice to consult.

…

…

745.06

……

…

3

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38

……

3.65

…

1

Ave spectral centroid

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1_10

…

...

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Working Satisfaction Speech rate years

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Doctor Service Title ID ID

…

Final Data Set for this research

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Figure 2. Process of Data Acquisition and Processing

Department and Title

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Working years Favorable rating

Consultation times

Peer evaluation

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Specialization

Hospital

Service price

Figure 3. Data Items of Physician Second, we collect all service data (e.g., physician consultation voice and patient evaluation) from the consultation page of each physician from November 1, 2017 to January

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31, 2018. Notably, considering that physicians and patients may communicate many times per service, we collect the link addresses of the voice files for each consultation and download them automatically, renaming according to the orders of their creation. Finally, we apply the procedure of Python to read the voice files in turn, and then combine them into a single file and generate the output. Thus, for each service, we only store one combined voice file. This process facilitates the subsequent spectral analysis. Third, we extract the speech rate of physician consultation voice at each service. The

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specific procedure for calculating it is as follows: Speech _ Rate  Words _ Number /Duration _ Time

(1)

We write a procedure of Python to extract the duration time (Duration_Time) of the

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voice. We then utilize the Baidu voice recognition API to collect the identified text and calculate its amount (Words_Number). This tool was provided by the Baidu artificial

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intelligence (AI) open platform (the largest AI platform in China), and its accuracy rate is

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higher than 90%. Finally, we use the amount of text contained in unit time to indicate the speech rate (e.g., if a 10-s-long voice contained 50 words, then the speech rate is five words

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per second).

Fourth, we obtain the spectrum characteristics of voice by using spectral analysis. Figure 4 shows the process in detail. Consistent with the literature, we first preprocess the original

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speech signal through framing and windowing [58]. Then, we calculate the fundamental

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frequency by using the cepstral analysis [49], which includes steps, such as fast Fourier transformation (FFT) and inverse fast Fourier transformation (IFFT). An example is shown in Figure 5. Subsequently, we use the value times that the signal value crosses the zero axis in each frame to indicate a short-time zero-crossing rate. Then, we extract the spectral centroid from the voice spectrum. The specific method for its calculation is as follows.

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Spectral _ Centroid 

N F /2 f s  NF 2 2   k S (k ) /  S (k )  N F  k 1 k 1 

(2)

For this equation, fs indicates the frequency of sampling in speech signal processing. The speech signal is a kind of energy wave that can be regarded as composed of countless points. However, because of the relative limitations of the storage space, only points of the wave are sampled. In other words, sampling can transfer a continuous-time signal to a discrete-time signal. A common unit of sampling frequency is Hz (i.e., Hertz) and means “samples per

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second” (e.g., 22.05 kHz is 22,050 samples per second). Following previous literature, the sampling frequency selected in this study is 16 kHZ [59]. S(k) represents the FFT of the speech signal x(n) and is calculated by using the following equation:

n 1

j

2 nk NF

(3)

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NF

S (k )   x(n)e

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Where NF indicates the number of processing points for FFT calculation. Finally, for the single combined voice in each medical consultation service, we use the mean values of the

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spectrum characteristics to represent the average fundamental frequency, average short-time zero-crossing rate, and average spectral centroid.

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Speech signal Framed

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Windowed

S(k)

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FFT

Log (·) IFFT

Fundamental Frequency

Short-time Zero-crossing Rate

Spectral Centroid

Figure 4. Process of Calculating Spectrum Characteristics

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Amplitude

1 Waveform

0.5 0 -0.5 -1

0

5

10

15

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Time (s) 600 400 200 0

0

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15 Time (s)

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Frequency (Hz)

Pitch track

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Figure 5. An example of Fundamental Frequency Determination

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Fifth, we merge the data obtained previously and generate two datasets, namely, “physician information dataset” and “voice characteristic dataset.” Because the datasets

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include physician ID items, we link the two datasets by physician ID to form the final dataset for our empirical analysis. In our sample, 343 physicians in 16 departments provide 35,597

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medical services from November 1, 2017 to January 31, 2018. Among the 343 physicians, nearly 50.4% are women, 31.7% have a senior title (i.e., archiater or associate archiater), and

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had a mean of 16.4 years of working. As such, our empirical analysis utilizes a sample ideal for voice characteristic research in terms of size and physician diversity.

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3.2. Research Variable

3.2.1. Dependent and Independent Variables

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The dependent variable is patient satisfaction. Figure 6 reveals that patients must leave a rating to the physician service when their online health consultation is finished, namely, satisfied, neutral, and unsatisfied. Thus, Patient_Satisfaction is equal to 1 if the patient felt unsatisfied, 2 if the patient felt neutral, and 3 if the patient felt satisfied.

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Satisfied

Figure 6. The Evaluation of Health Consultation Service The primary independent variables are speech rate and average spectral centroid within physician consultation voice. Furthermore, to investigate the moderating role of physician professional capital as stated in H3 and H4, we add the indicator Professional_Capital to the

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model. Notably, in the context of online health, using the physician’s title, working years, or hospital grade to represent professional capital alone is insufficient. Although several physicians have a lower title because of the lack of academic achievements and other reasons,

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many years of practice have accumulated rich experience and their professional capital is still

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relatively high. Similarly, physicians who have acquired a senior title at a young age are competent. The grade of the hospital where physicians work remains an important feature

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that can partially represent ability and social prestige. Thus, in this study, we perform a factor analysis on the three features (i.e., working years, clinic title, and grade of the hospital) and

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use the factor score to indicate the professional capital of physicians. Specifically, the three important features are denoted as Working_Years, Title, and Hospital_Grade. Working_Years

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is an integer that ranges from 4 to 48. Title is equal to 1 if the physician has a senior title (i.e., archiater or associate archiater) and 0 if the physician has a nonsenior title (i.e., chief doctor

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or resident doctor). Hospital_Grade is equal to 1 if the physician works in a Grade-A tertiary hospital and 0 if the physician works in a non-Grade-A tertiary hospital. 3.2.2. Control Variables

Following previous literature [6, 9, 60], we add a set of control variables to capture the confounding effects caused by the physician- or consultation-level factors. The specific effects of the physician-level items are captured partially by the physician

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gender, department, title, working years, service price, and favorable rating. However, considering that physicians’ medical ability is the most critical factor affecting patient satisfaction, title, and working years do not well measure this trait. Therefore, we use 342 dummy variables for physician ID (integer value ranging from 1 to 343) to control the fixed effect of physicians. This set of dummies control the physician title, working years, gender, age, service price, department, hospital, and city as well as physician professional capital, medical ability, and personality traits. Within our short data collection period (i.e., from

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November 1, 2017 to January 31, 2018), the basic characteristics of physicians mentioned above do not change significantly.

The consultation-level control variables are as follows: Considering other speech

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features (e.g., average fundamental frequency and average short-time zero-crossing rate, denoted as Ave_Fund_Frequency and Ave_Szcr, respectively) may affect patient satisfaction,

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we add these two control variables into the model. Moreover, the total amount of information

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provided by physicians might influence patient satisfaction. More information may indicate a higher level of conscientiousness on the part of the physician and more care for the patient. Thus, we add a variable into the model to control the total number of words contained in a

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physician’s voice for each service. This variable is denoted as Total_Information. In line with previous literature [6], we also add the number of voices contained in a single service

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(denoted as Voice_Times) into the model to control the frequency of interactions that may

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influence patient satisfaction. Furthermore, the speech rate of physicians might decrease as the duration time of voice increases. We address this concern by incorporating the variable denoted as Duration_Time into the model. Notably, to control the common fluctuations in patient satisfaction over time, we include the fixed effects of months in all regression models. Table 1 presents a list of all variables and their definitions.

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Table 2 exhibits the descriptive statistics of key variables and indicates a significant difference between the maximum and minimum values of the speech rate and average spectral centroid, thereby ensuring the diversity of voice characteristics. Furthermore, patient satisfaction exhibits a clear L-shaped pattern because half of them had a score of 3 [61]. Table 3 shows the correlations among the above variables. Table 1. Description of Variables Variable Dependent Variable Patient_Satisfaction

Measures

Control variables Ave_Szcr Ave_Fund_Frequency

The mean value of times that the signal value crosses the zero axes in each frame of the consultation voice spectrum. The mean value of the fundamental frequency in consultation voice spectrum. The total number of words contained in consultation voice for each service. The duration time of voices contained in each service The number of voices contained in each service A set of dummies control for the fixed effect of physicians. Two dummies control for the fixed effect of months.

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Total_Information Duration_Time Voice_Times Physician Month

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Professional_Capital (PC)

The number of words contained in the consultation voice per second. The mean value of the average frequency weighted by the normalized energy values of each frequency component in the consultation voice spectrum. A factor score by performing factor analysis on physicians’ working years, clinic title, and hospital grade.

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Independent Variables Speech_Rate Ave_Spectral_Centroid

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The extent to which the patient felt satisfied at the end of the medical service (1 = unsatisfied, 2 = neutral, 3 = satisfied).

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Table 2. Descriptive Statistics of Variables. Mean 2.57 3.60 765.17 0.00 0.32 16.37 0.59 580.06 164.52 8.25

Standard deviation 0.63 0.91 187.12 1.00 0.47 9.25 0.49 377.72 103.93 8.70

Min 1.00 0.52 144.20 -1.15 0.00 4.00 0.00 18.00 18.16 1.00

35.26 290.11

10.81 34.73

8.86 161.25

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Statistic Patient_Satisfaction Speech_Rate Ave_Spectral_Centroid PC Title Working_Years Hospital_Grade Total_Information Duration_Time Voice_Times Ave_Szcr Ave_Fund_Frequency

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Median 3.00 3.64 745.75 -0.49 0.00 15.00 1.00 494.00 139.94 5.00 33.74 288.70

Max 3.00 6.68 2678.92 2.73 1.00 48.00 1.00 2140.00 500.02 34.00 108.04 446.66

Table 3. Variable Correlations. (1) 1 0.05 -0.11 -0.05 -0.03 -0.07 0.06 0.09 0.08 0.07 -0.08 -0.05

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

1 0.07 -0.18 -0.14 -0.20 0.13 0.25 -0.12 0.20 -0.11 -0.16

1 -0.04 -0.04 -0.03 -0.09 -0.03 -0.07 -0.02 0.44 0.16

1 0.90 0.90 0.04 0.005 0.08 0.01 0.01 0.09

1 0.63 0.05 0.01 0.07 0.02 0.003 0.07

1 -0.02 -0.01 0.07 -0.01 0.03 0.09

1 0.12 0.08 0.11 -0.08 -0.08

1 0.90 0.80 -0.03 0.01

1 0.71 0.01 0.07

1 -0.02 0.01

1 0.19

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3.3. Empirical Model

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Variable 1 Patient_Satisfaction 2 Speech_Rate 3 Ave_Spectral_Centroid 4 PC 5 Title 6 Working_Years 7 Hospital_Grade 8 Total_Information 9 Duration_Time 10 Voice_Times 11 Ave_Szcr 12 Ave_Fund_Frequency

Patient rating (integer value ranging from 1 to 3) is a censored and ordered variable, which is

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not distributed normally. Therefore, this research uses the ordinal logistic model [62, 63]. We specify the following model by accommodating the nonlinear effects of the independent

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variables on Uij, a latent variable that represents patient evaluation on medical service i provided by the physician j.

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U ij   0  1Speech _ Rateij   2 Ave _ Spectral _ Centroidij  3 Speech _ Rateij  PC j 

 4 Ave _ Spectral _ Centroidij  PC j  5 Ave _ Szcrij   6 Ave _ Fund _ Frequencyij 

(4)

7Total _ Inforamtionij   Physician j   Month   ij '

'

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The observed variable Patient_Satisfactionij is determined from Uij using the following

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rule:

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Patient _ Satisfactionij

1 if U ij   1   k  2 if  1  U ij   2  3 if U ij   2

(5)

In the formula presented above, k denotes the realized value of the rating posted by

patients,  1 and  2 , which are the cut-off parameters that determine the intervals for each rank of the patient ratings. The probability of an observed outcome Patient_Satisfactionij corresponds to the region of probability distribution where Uij falls between  1 and  2 . The predicted probability is computed as follows: 20

 Pr( patient _ satisfactionij  k )  In    U ij   k -1 , k {2,3} 1  Pr( patient _ satisfactionij  k ) 

(6)

We investigate the effects of the two main characteristics of physician consultation voice on patient satisfaction as well as the moderating role of physician professional capital. In Equation (4), this research focuses mainly on β1, β2, β3, and β4, which captures the main effects of speech rate, average spectral centroid, and the moderating role of professional capital, respectively.

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In Equation (4), we control the factors of physician and consultation levels. We control the other two voice characteristics, namely, the average fundamental frequency (Ave_Fund_Frequencyij) and average short-time zero-crossing rate (Ave_Szcrij), which may

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affect patient satisfaction. We include the total number of words contained in the physician consultation voice (Total_Informationij) into the model. Vectors λ and δ control the fixed

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effects of physician and time, respectively, by including dummies for physician ID (integer value ranging from 1 to 343) and months (November, December, and January). We estimate

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the intercepts τk (k = 2-3), which capture the range of distribution associated with Uij. In Equation (4), the independent variable, PCj, refers to the professional capital of

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physician j, calculated by using the factor analysis. Therefore, to check the robustness of our results, after obtaining the benchmark results using PCj as the independent variable, we

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replace the variable with Titlej, Working_Yearsj, and Hospital_Gradej. Through this

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procedure, we can verify whether the hypotheses are supported for the different measurements of physicians’ professional capital, thereby strengthening the robustness of our findings.

4. Result 4.1. Hypothesis Testing

Table 4 shows the results of our estimation. Column (1) is the benchmark model without the

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two interaction terms, whereas column (2) is the full model that includes the moderating effects of PC and the main effects of Speech_Rate and Ave_Spectral_Centroid. The result of each model is consistent. Table 4. Effects of Voice Characteristics on Patient Satisfaction

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Dependent Variable: Patient_Satisfaction (1) (2) Speech_Rate 0.0776** 0.0764** (0.0263) (0.0263) Ave_Spectral_Centroid -0.0016*** -0.0016*** (0.0001) (0.0001) Speech_Rate×PC -0.0445** (0.0170) Ave_Spectral_Centroid×PC 0.0003** (0.0001) Ave_Szcr -0.0032* -0.0034* (0.0014) (0.0014) Ave_Fund_Frequency -0.0010* -0.0010* (0.0005) (0.0005) Total_Information -0.00003 -0.00001 (0.0002) (0.0002) Duration_Time 0.0010 0.0009 (0.0005) (0.0005) Voice_Times -0.0024 -0.0024 (0.0023) (0.0023) *** Intercept-2 7.3735 7.3568*** (1.0227) (1.0262) Intercept-3 5.2091*** 5.1914*** (1.0223) (1.0256) Physician FE YES YES Month FE YES YES Observations 35,597 35,597 2 R 0.2352 0.2357 chi2 7,530.11*** 7,547.74*** Notes: 1. Asymptotic standard errors robust to heteroskedasticity are shown in parenthesis. The null hypothesis that coefficients are equal to zero is tested using robust standard errors. 2. *, **, and *** illustrate significance at the 0.05, 0.01, and 0.001 levels, respectively. 3. All estimates control for the fixed effects of physicians and months.

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Table 4 shows that the coefficient of Speech_Rate with patient satisfaction is significantly positive (p < 0.01), thereby suggesting that the speech rate of physician consultation voice is positively related to patient satisfaction. Physicians with a high speech rate tend to receive high patient satisfaction. Thus, H1 is supported. H2 predicted that the average spectral centroid of physician consultation voice negatively affects patient satisfaction. Table 4 shows the results of hypothesis testing. The

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coefficient of Ave_Spectral_Centroid with Patient_Satisfaction is significant and negative (p < 0.001). Thus, the average satisfaction decreases when the average spectral centroid of physician consultation voice increases. Thus, H2 is supported. Column (2) of Table 4 reveals that the coefficients of the two interaction terms are significant at the 0.01 significance level. Given that the main effect of speech rate is positive and the interaction of Speech_Rate×PC is negative; the positive effect of the physician speech rate on patient satisfaction is small for physicians with high professional capital

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provided that other things are equal. Therefore, the effect of physician speech rate on patient satisfaction is significantly moderated by physicians’ professional capital, and the positive effect is weak for physicians with high professional capital. Thus, H3 is supported.

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Similarly, the main effect of average spectral centroid is negative and the coefficient of Ave_Spectral_Centroid×PC is positive, which indicates that as the professional capital of

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physicians’ increases, the negative effect of average spectral centroid on patient satisfaction

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decreases. Therefore, the negative relationship between the average spectral centroid of physician consultation voice and patient satisfaction is moderated by physician professional capital. Thus, H4 is supported.

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Table 4 also presents the estimated results for the control variables of voice level. The coefficient of Ave_Fund_Frequency is negative (p < 0.05), which demonstrates that patients

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tend to submit low ratings as the average fundamental frequency of physician consultation

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voice increases. According to the extant research, the pitch of voice enlarges with the increase of average fundamental frequency [64]. A high-pitched voice has a negative influence on the recipient of message [65] and hence reduces patient satisfaction. 4.2. Robustness Check 4.2.1. Clinic Title, Working Years, and Hospital Grade

We replace the independent variable of physicians’ professional capital (PC) with their clinic

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Title (Title), working years (Working_Years), and hospital grade (Hospital_Grade) to check result robustness. We then re-execute the ordinal regression model indicated in Equation (4). Table 5 presents the results. Table 5. The Moderating Role of Clinic Title, Working Years, and Hospital Grade Speech_Rate Ave_Spectral_Centroid Speech_Rate×Title Ave_Spectral_Centroid×Title

Dependent Variable: Patient_Satisfaction (2) (3) 0.1520*** 0.1251*** (0.0402) (0.0317) -0.0021*** -0.0018*** (0.0002) (0.0001)

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(1) 0.1010*** (0.0286) -0.0017*** (0.0001) -0.0755* (0.0351) 0.0004* (0.0002)

-0.0046* (0.0019) 0.00003*** (0.00001)

Speech_Rate×Working_Years Ave_Spectral_Centroid×Working_Years

-0.0961** (0.0337) Ave_Spectral_Centroid×Hospital_Grade 0.0004* (0.0002) Ave_Szcr -0.0033* -0.0035* -0.0029* (0.0014) (0.0014) (0.0014) Ave_Fund_Frequency -0.0010* -0.0010* -0.0010* (0.0005) (0.0005) (0.0005) Total_Information -0.00002 -0.000004 0.00001 (0.0002) (0.0002) (0.0002) Duration_Time 0.0010 0.0009 0.0009 (0.0005) (0.0005) (0.0005) Voice_Times -0.0024 -0.0024 -0.0024 (0.0023) (0.0023) (0.0023) Intercept-2 7.3766*** 7.3625*** 7.3952*** (1.0275) (1.0237) (1.0254) Intercept-3 5.2118*** 5.1971*** 5.2296*** (1.0270) (1.0233) (1.0250) Physician FE YES YES YES Month FE YES YES YES Observations 35,597 35,597 35,597 R2 0.2355 0.2357 0.2356 2 *** *** chi 7,539.87 7,548.79 7,545.55*** Notes: 1. Asymptotic standard errors robust to heteroskedasticity are shown in parenthesis. The null hypothesis that coefficients are equal to zero is tested using robust standard errors. 2. *, **, and *** illustrate significance at the 0.05, 0.01, and 0.001 levels, respectively. 3. All estimates control for the fixed effects of physicians and months.

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Speech_Rate×Hospital_Grade

Columns (1) to (3) of Table 5 indicate that the main effect of speech rate is positive, and the interaction effects are negative. Similarly, the main effect of the average spectral centroid is negative, and the interaction effects are positive. Therefore, the hypotheses are supported 24

for the replaced independent variables. However, considering the strong moderating effect of physician title (β3 = -0.0755, p < 0.05) and hospital grade (β3 = -0.0961, p < 0.01) in columns (1) and (3) of Table 5, respectively, we speculate that the influence of speech rate on patient satisfaction is not statistically significant for physicians with a senior title or physicians working in a Grade-A tertiary hospital. To address this concern, we separate the total data into several subsamples and implement the benchmark model again. The results are shown in Table 6.

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Table 6. The Regression for Subsamples

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Dependent Variable: Patient_Satisfaction (1) (2) (3) (4) Speech_Rate 0.0486 0.0903** 0.0397 0.0932* (0.0454) (0.0324) (0.0358) (0.0398) Ave_Spectral_Centroid -0.0011*** -0.0018*** -0.0013*** -0.0020*** (0.0001) (0.0001) (0.0001) (0.0001) Ave_Szcr -0.0106*** 0.0009 -0.0051** 0.0017 (0.0022) (0.0018) (0.0017) (0.0025) Ave_Fund_Frequency -0.0035*** 0.0001 -0.0020*** 0.0004 (0.0008) (0.0006) (0.0006) (0.0007) Total_Information -0.0002 0.0001 -0.0002 0.0004 (0.0003) (0.0002) (0.0002) (0.0002) Duration_Time 0.0020* 0.0004 0.0011 0.0001 (0.0008) (0.0007) (0.0007) (0.0008) Voice_Times -0.0052 -0.0007 -0.0011 -0.0046 (0.0039) (0.0029) (0.0029) (0.0040) Intercept-2 8.1107*** 6.8924*** 7.7319*** 7.2562*** (1.0534) (1.0630) (1.0360) (1.0809) Intercept-3 5.8697*** 4.7619*** 5.5891*** 5.0592*** (1.0515) (1.0617) (1.0351) (1.0787) Physician FE YES YES YES YES Month FE YES YES YES YES Observations 11,280 24,317 21,066 14,531 R2 0.2375 0.2346 0.2306 0.2364 2 *** *** *** chi 2,444.26 5,093.50 4,285.97 3,154.43*** Notes: 1. Asymptotic standard errors robust to heteroskedasticity are shown in parenthesis. The null hypothesis that coefficients are equal to zero is tested using robust standard errors. 2. *, **, and *** illustrate significance at the 0.05, 0.01, and 0.001 levels, respectively. 3. All estimates control for the fixed effects of physicians and months.

In columns (1) and (2) of Table 6, we perform the analysis for physicians with a senior

title and physicians with a nonsenior title separately, without Title as the moderator. Only the speech rate of physicians with a nonsenior title was found to have a positive effect on patient satisfaction, but this effect is insignificant for physicians with a senior title. Similarly, we 25

perform the analysis for physicians working in a Grade-A tertiary hospital and a non-GradeA tertiary hospital separately, without Hospital_Grade as the moderator. The results are shown in columns (3) and (4) of Table 6. Only the speech rate of physicians working in a non-Grade-A tertiary hospital has a positive effect on patient satisfaction, but this effect is insignificant for physicians working in a Grade-A tertiary hospital. Nevertheless, the influence of the average spectral centroid is significant in any case. Physicians with senior titles are generally older (compared with physicians with nonsenior titles), and the speech rate

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slows down significantly with the increase of age [66]. A senior title and working in a GradeA tertiary hospital provide strong trustworthiness of physicians, which may substitute the effect of a fast speech rate. Thus, the positive influence of speech rate becomes insignificant

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in such situations. 4.2.2. Non-Monotonic Effect of Speech Rate

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In our analysis, we find that a high speech rate of physicians is associated with a high level of

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patient satisfaction. However, a potential concern is that too fast speech rate may prevent patients from processing information easily thereby reducing their satisfaction. To address this concern, we first add the quadratic term of speech rate to the benchmark model and

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attempt to find a reverse U-shape relationship between physician speech rate and patient satisfaction. As shown in column (1) of Table 7, the result indicates that the effect of the

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quadratic term is insignificant. Therefore, our sample does not provide enough evidence for a

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reverse U-shape relationship.

We perform piecewise regression [67-69] by incorporating linear splines of Speech_Rate

to the benchmark model to further examine the relationship between speech rate and patient satisfaction. Splines create a segmenting specification in which functions are combined at predefined intervals of an aimed variable. This design mainly investigates the influence of speech rate located in different segments on patient satisfaction. Physician speech rate in our

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sample ranges from 0.52 to 6.68 and the distribution is skewed to the right. The speech rate of roughly 99% of the observations had lower than 5.49. Thus, to test the negative effects generated by a too fast speech rate, we consider a quartile-based specification and place knots at the 97th, 98th, and 99th percentiles of Speech_Rate, representing 5.16, 5.29, and 5.49, respectively. The estimation results for selecting different knots are shown in columns (2) to (4) of Table 7. Table 7. Non-Monotonic Effect of Speech Rate

Speech_Rate^2

(1) 0.0067 (0.0844) 0.0101 (0.0113)

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Speech_Rate

Dependent Variable: Patient_Satisfaction (2)-97th (3)-98th (4)-99th 0.0868** 0.0882** 0.0883*** (0.0273) (0.0271) (0.0267)

-0.2663 -0.4374 -0.8473* (0.1956) (0.2439) (0.3552) *** *** *** -0.0016 -0.0016 -0.0016*** Ave_Spectral_Centroid -0.0016 (0.0001) (0.0001) (0.0001) (0.0001) Ave_Szcr -0.0032* -0.0032* -0.0032* -0.0032* (0.0014) (0.0014) (0.0014) (0.0014) -0.0010* -0.0010* -0.0010* Ave_Fund_Frequency -0.0010* (0.0005) (0.0005) (0.0005) (0.0005) Total_Information -0.000004 -0.0001 -0.0001 -0.0001 (0.0002) (0.0002) (0.0002) (0.0002) Duration_Time 0.0009 0.0011* 0.0011* 0.0011* (0.0005) (0.0005) (0.0005) (0.0005) Voice_Times -0.0025 -0.0024 -0.0024 -0.0024 (0.0023) (0.0023) (0.0023) (0.0023) Intercept-2 7.4880*** 7.3422*** 7.3373*** 7.3361*** (1.0316) (1.0230) (1.0230) (1.0229) Intercept-3 5.3235*** 5.1777*** 5.1728*** 5.1714*** (1.0312) (1.0226) (1.0225) (1.0224) Physician FE YES YES YES YES Month FE YES YES YES YES Observations 35,597 35,597 35,597 35,597 R2 0.2352 0.2352 0.2353 0.2353 *** *** *** 2 7,530.94 7,531.87 7,533.12 7,535.06*** chi Notes: 1. Asymptotic standard errors robust to heteroskedasticity are shown in parenthesis. The null hypothesis that coefficients are equal to zero is tested using robust standard errors. 2. *, **, and *** illustrate significance at the 0.05, 0.01, and 0.001 levels, respectively. 3. All estimates control for the fixed effects of physicians and months.

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Spline_Variable

We find that the coefficient of Speech_Rate remains significantly positive in all columns, thereby suggesting that the speech rate of physician consultation voice is positively related to patient satisfaction when its value is lower than 5.16, 5.29, or 5.49. In columns (2) and (3),

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the coefficient of Spline_Variable is negative and insignificant. Hence, the influence of speech rate on patient satisfaction is negative but insignificant when its value is higher than 5.16 or 5.29. However, the coefficient of Spline_Variable is significantly negative in column (4), such that the influence of speech rate on patient satisfaction is negative and significant when its value exceeds 5.49. This finding may be explained in part by the view that a too fast speech rate (e.g., the speech rate higher than 5.49) reduces comprehension [70] or makes listeners more likely to judge the speaker as patronizing [71]. As such, the relationship

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between the speech rate of physician consultation voice and patient satisfaction is not positive when the speech rate is extremely fast. 4.2.3. Time-Varying Effects

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Illness varies highly with different seasons and the number of consultations may influence the quality of medical service and further affect patient satisfaction. For example,

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the incidence of influenza has a certain seasonality, generally starting from December and

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coming to March, in China. During this period, physicians may receive many consultations every day and they might not have enough energy to treat each patient meticulously, which may reduce their satisfaction. Considering such situations, we speculate that the effects

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caused by physicians’ voice characteristics may vary in different months. Thus, we investigate the time-varying effects of main variables by incorporating the interaction terms

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of voice characteristics and month dummies (January is the baseline) to the benchmark model.

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The results are shown in Table 8. In columns (1) and (2) of Table 8, the coefficients of interaction terms are insignificant, thereby suggesting no significant differences in the effects of voice characteristics in different months.

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Table 8. Time-Varying Effects

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Dependent Variable: Patient_Satisfaction (1) (2) Speech_Rate 0.0956** 0.0775** (0.0315) (0.0263) -0.0016*** -0.0015*** Ave_Spectral_Centroid (0.0001) (0.0001) Speech_Rate×Nov -0.0215 (0.0308) Speech_Rate×Dec -0.0362 (0.0315) Ave_Spectral_Centroid×Nov -0.0001 (0.0002) Ave_Spectral_Centroid×Dec 0.000003 (0.0002) Ave_Szcr -0.0032* -0.0032* (0.0014) (0.0014) -0.0010* -0.0010* Ave_Fund_Frequency (0.0005) (0.0005) Total_Information -0.00003 -0.00003 (0.0002) (0.0002) Duration_Time 0.0010 0.0010 (0.0005) (0.0005) Voice_Times -0.0024 -0.0024 (0.0023) (0.0023) *** Intercept-2 7.3083 7.3363*** (1.0243) (1.0243) Intercept-3 5.1439*** 5.1718*** (1.0239) (1.0238) Physician FE YES YES Month FE YES YES Observations 35,597 35,597 2 R 0.2352 0.2352 7,531.46*** 7,531.37*** chi2 Notes: 1. Asymptotic standard errors robust to heteroskedasticity are shown in parenthesis. The null hypothesis that coefficients are equal to zero is tested using robust standard errors. 2. *, **, and *** illustrate significance at the 0.05, 0.01, and 0.001 levels, respectively. 3. All estimates control for the fixed effects of physicians and months.

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4.3. Additional Analysis

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Considering the combination of voice characteristics may cause different effects on recipients, we try to investigate the interaction effect between speech rate and average spectral centroid on patient satisfaction to enrich our findings. We reestimate the benchmark model and incorporate the interaction term. The results shown in Table 9 reveal that the coefficient of Speech_Rate×Ave_Spectral_Centroid is negative. Hence, as the speech rate increases, the negative influence of average spectral centroid on patient satisfaction is amplified. Such

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results conform to the general understanding that speaking fast with a negative emotion is not a good idea. From another point of view, with the increases in average spectral centroid, the positive effect caused by speech rate decreases. This finding may be explained in part by the view that negative emotion inhibits the persuasiveness of medical suggestions [72]. Thus, the best choice for physicians during online consultation may be speaking quickly (but not too fast) in a neutral emotion state. The high average spectral centroid has a direct negative influence on patient satisfaction and weakens the positive effect lead by a fast speech rate.

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Table 9. The Interaction of Two Main Voice Characteristics

Dependent Variable: Patient_Satisfaction 0.1935** (0.0597) Ave_Spectral_Centroid -0.0010*** (0.0002) Speech_Rate×Ave_Spectral_Centroid -0.0001* (0.0001) Ave_Szcr -0.0032* (0.0014) Ave_Fund_Frequency -0.0011* (0.0005) Total_Information -0.00004 (0.0002) Duration_Time 0.0011* (0.0005) Voice_Times -0.0024 (0.0023) Intercept-2 6.9772*** (1.0401) Intercept-3 4.8126*** (1.0396) Physician FE YES Month FE YES Observations 35,597 R2 0.2353 2 chi 7,534.63*** Notes: 1. Asymptotic standard errors robust to heteroskedasticity are shown in parenthesis. The null hypothesis that coefficients are equal to zero is tested using robust standard errors. 2. *, **, and *** illustrate significance at the 0.05, 0.01, and 0.001 levels, respectively. 3. All estimates control for the fixed effects of physicians and months.

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Speech_Rate

5. Discussion and Implications 5.1. Discussion

This research investigates the effects of characteristics within a physician consultation voice on patient satisfaction in an online health consultation. Patients are likely satisfied when the 30

physician speech rate is fast (but not extremely fast) and the average spectral centroid of the voice spectrum is low. However, physician professional capital moderates the effects of the two voice characteristics on patient satisfaction. By using voice data obtained from a mobile health community, we develop an ordinal logistic regression model for hypothesis testing. Although potential endogeneity concerns may exist, we control many factors of physician and consultation levels to address this concern. This process may alleviate endogeneity issues to an extent.

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Our analysis obtains three critical findings. First, patient satisfaction is positively influenced by the speech rate but negatively affected by the average spectral centroid. Only the speech rate of physicians with a nonsenior title or working in a non-Grade-A Tertiary

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hospital has a positive effect on patient satisfaction. By contrast, this effect is insignificant for physicians with the senior title or working in Grade-A tertiary hospitals. Moreover, the effect

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of speech rate on patient satisfaction is not positive when physicians speak too fast (e.g.,

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faster than 5.49 words per second). These findings provide new evidence for contradictory effects that lead by a high speech rate in previous literature. Most researchers emphasize that the positive effect of fast speech rate correlates with high information support and persuasion

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effect [29, 41, 45], but others speculate that a high speech rate may impede information processing and hence, negatively impact recipients [70]. In the unique context of online

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health consultation, we identify these two types of effects created by a high speech rate

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located in different segments (i.e., lower or higher than 5.49 words per second). Furthermore, the negative effect caused by the average spectral centroid is consistent with the view that the voice with high average spectral centroid contains negative emotions [24]. Second, the effect of voice characteristics on patient satisfaction is weak for physicians with high professional capital. The result provides new knowledge that professional capital, as an important feature of physicians, not only has a direct effect on patients [17, 19] but also

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curtails the effects caused by the voice characteristic (a feature relatively less important). This finding is in line with the theory of the halo effect, which contends that strong features dominate the influence of weak features [20]. Third, the interaction effect of speech rate and average spectral centroid on patient satisfaction is negative. Speech is made up of sound waves that vary in frequency and intensity. The different combinations of various speech characteristics may create various influences on recipients [12, 73]. For instance, the voice intensity of advertisements

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positively moderates the negative effect of voice intonation on the acceptance of advertising message, whereby as voice intensity increases, the negative effect of voice intonation decreases [73]. In our research, we investigate a new combination (i.e., speech rate and

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average spectral centroid) and obtain a negative interaction effect. This finding may be explained in part by the view that negative emotion inhibits the persuasiveness of medical

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suggestions [72]. Our findings highlight the positive effect of a speech rate and the negative

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influence of average spectral centroid on patient satisfaction as well as the moderating role of physician professional capital. These results contribute to the formulation of the norm of online health voice consultation and the design of a mobile healthcare consultative system.

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5.2. Theoretical Implications

Our research provides theoretical contributions in the following ways. First, this research

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contributes to the online health literature by examining voice characteristic through the

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generalization of the effects of speech rate and average spectral centroid within physician consultation voice on patient satisfaction. The results elucidate that voice characteristics increase patient satisfaction. Physicians can take advantage of their voices to enhance patient satisfaction and build good relationships with patients. This finding differs significantly from previous studies related to voice and service satisfaction, which focus only on the economic value of voice [10-12]. In addition, the design and data limitations of prior literature restrict

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the generalizability of their findings. By contrast, our research uses a unique mobile health consultation dataset, including the real voice of 343 physicians from the “Chun Yu Physician” application. The diversity of physicians and patients enhances the generalizability of the effects of voice characteristics on service evaluation. In sum, this study extends a voice characteristic analysis to online health literature by using unique voice characteristics and rich real data. Second, this study contributes to voice characteristic literature by identifying the effects

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of voice spectral features and their interaction on patient satisfaction. Our results verify the differentiated effects of two voice characteristics (i.e., speech rate and average spectral centroid), which have rarely been examined in previous research related to acoustic features

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[11, 21, 22]. We find that patient satisfaction is negatively influenced by average spectral centroid but positively affected by the speech rate. Such findings may be applicable in other

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contexts related to voice characteristics. Furthermore, speech rate and average spectral

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centroid negatively interact with each other in affecting patient satisfaction. This finding provides novel insights into the interactive effects of various voice characteristics because previous literature focuses on the direct effect of each voice feature. Such an investigation

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could optimize the best voice by combining different acoustic features. Our findings enrich social support literature by integrating informational and emotional

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support with the perspective of voice characteristics. This research reveals the potential

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internal mechanism of how speech rate and average spectral centroid influence patient satisfaction through informational and emotional support. The results demonstrate that different voices may generate various feels of support for patients. We indicate further that these two types of support may interact with each other. Therefore, future research can investigate other voice characteristics that are related to information and emotion to gain additional insights.

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Third, this research provides insights into the service management literature by considering the moderating role of service providers’ professional capital on the linkage of voice characteristics with patient satisfaction. Prior research seldom examines the integrated effects of a service provider and voice characteristics. However, this study verifies clearly that service providers’ (i.e., physicians in this study) professional capital interacts with voice characteristics in influencing service recipient satisfaction in medical service delivery. We provide theoretical evidence that the strong perception of physician professional capital

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weakens the relationship between voice characteristics and patient satisfaction. 5.3. Practical Implications

This study also provides practical implications for physicians and managers of the mobile

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health community. First, physicians can take advantage of their speech rate to improve patient satisfaction, especially for physicians with low professional capital. According to our

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results, the speech rate of physician consultation voice has a positive effect on patient service

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evaluation. A fast speaker is perceived as informational, persuasive, and knowledgeable. This type of feeling could improve the informational support of medical suggestions and hence enhance patient satisfaction. Therefore, the speech rate plays a role in forming a good

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physician–patient relationship, and this mechanism could be widely used in the field of medicine. For instance, physicians not only provide online health consultation but also supply

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medical services in offline hospitals. The two types of services may sometimes conflict [3],

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and physicians tend to pay more attention to the latter because patient satisfaction with faceto-face offline consultation is more closely related to their professional stability [26]. In this case, physicians are unable to focus well on assessing the health condition of online patients and provide medical advices by using a slow voice that contains more pauses and decreases the patient evaluation of information quality. Therefore, in a mobile health community, we suggest that physicians should carefully consider patients’ health conditions and provide

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suggestions with a fast speech rate (but not too fast) in their spare time. Second, conversely, the average spectral centroid of consultation voice spectrum has a negative relationship with patient satisfaction. This result validates that physicians should express a neutral emotional state (compared with joy and anger emotional state) when they provide voice consultation service. Different from original written consultations, voice consultations contain significant emotional information that is easily perceived. Thus, physicians should pay considerable attention to their service emotions. For instance,

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physicians’ emotional state is significantly influenced by their heavy workload and offline medical disputes [74]. The negative emotions generated in offline work may be brought to the process of online consultation and contained in the physician’s answering voice.

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Therefore, the negative emotions formed offline should be adjusted and medical services should be provided by using a voice that contains a neutral emotional state.

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Third, our study offers implications for the design of a mobile healthcare consultative

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system. Numerous physicians simply treat patients’ questions without genuinely responding or understanding their health conditions, because of the nonvisual interaction in online health consultation [6]. Thus, with the rapid development of speech recognition and spectrum

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analysis technology, the mobile health community can add a real-time voice monitoring system to enhance the patient satisfaction and build a favorable mobile consultation

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environment. Only the consultation voice that is qualified can be sent to the patient by

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identifying whether the voice is clear and fluent and contains a mild service attitude through the analysis of their acoustic and spectral features. 5.4. Limitations and Future Research

This study has several limitations. First, certain illness characteristics, such as the severity of illness, may influence patient satisfaction. In our study, we control the fixed effect of physicians. The department of the physician from whom the patients ask for help reveals the

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type of disease that the patient may have. However, given that the severity of illness relates to privacy issues and such data are lacking in the platform, we regard this lack as a limitation for our further understanding. Second, patient characteristics, such as gender, age, educational background, and consultation history may influence patient satisfaction. However, the identification of users is unobservable in the given mobile health community because of patient privacy protection. Thus, future research may obtain more insights if patient characteristics are present. Finally, the short time span (three months) of data restricts the

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study of the seasonal trend of our model. “Chun Yu Physician” only displays the latest consultation history to save storage resources or for other reasons. Further research can collect data periodically and obtain a sample covering a longer period to enrich our findings.

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Several directions can be explored for future research. First, in this study, we only consider the mean value of voice characteristics in spectrum. Future research could analyze

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how patient satisfaction is influenced by the dynamic changes of voice characteristics (e.g.,

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the variance of spectral centroid). Second, the voice and written text of physicians are key elements in online health consultation. Future study could investigate their interactive effect on patient satisfaction. Moreover, identifying the diverse effects caused by them and

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excavating the mechanism behind it may also be an interesting direction. Third, the effects of voice characteristics in other mobile platforms where professionals (e.g., teachers, physicians,

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and lawyers) provide service by using their voices can be examined. This research may

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provide additional insights into the effects of voice characteristics in other fields. Fourth, future research can apply and extend our process of data acquisition and processing to extract more important voice characteristics from different types of phonetics. Fifth, the factors that relate to physician voice characteristics (e.g., physician gender, age, and city) may moderate the effects of voice characteristics. Therefore, additional moderating variables can be added into the model to enrich our findings.

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6. Conclusions This study provides new knowledge by exploring the joint effects of voice- and physicianrelated factors on patient satisfaction in online health consultations. Patients feel more satisfied when the physician speech rate is fast (but not too fast) and the average spectral centroid of the voice spectrum is low. This finding determines the social benefits resulting from voice characteristics and identifies the effects of two unique voice characteristics. Another contribution lies in the moderating role of physician characteristics. The results

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corroborate the view that physician professional capital weakens the effects of voice characteristics on patient satisfaction. Therefore, physicians with low professional capital should pay more attention to their voice characteristics and take advantage of their voices to

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improve patient satisfaction.

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References

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Acknowledgments This work was supported by the National Natural Science Foundation (NSFC) Programs of China [91646113, 71722014, 71471141, and 71771182]. We also appreciate the support of the Youth Innovation Team of Shaanxi Universities “Big data and Business Intelligent Innovation Team” and Shaanxi Superiority Funding Project for Scientific and Technological Activities of Overseas Scholars (No.2018017).

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Biographies Shan Liu is a Professor at the School of Management in Xi’an Jiaotong University. His research interests focus on IT project management, data analytics, and healthcare information management. He has published more than 40 refereed publications including papers that have appeared in Information & Management, European Journal of Operational Research, Journal of Operations Management, Information Systems Journal, European Journal of Information Systems, Management Decision, International Journal of Project Management, International Journal of Medical Informatics, and International Journal of Information Management.

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Muyu Zhang is a Ph.D. student at the School of Management in Xi’an Jiaotong University. His research interests are in the areas of business analytics and social media.

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Baojun Gao is a Professor of Management Science at Wuhan University. He received his B.E., M.Sc., and Ph.D. from Xian Jiaotong University. His research interests are in the areas of business analytics and social media. His research has appeared in Tourism Management, Decision Support Systems, Information & Management, IEEE Transactions on Engineering Management, IEEE Transactions on SMC: Systems, Electronic Commerce Research and Applications, China Economic Review, and Service Science.

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Guoyin Jiang is a Professor of University of Electronic Science and Technology of China. His research interests include decision support systems, simulation, and ecommerce. He published more than 50 papers in journals such as European Journal of Operational Research, Information Sciences, IEEE/ACM Transactions on Networking, International Journal of Project Management, Industrial Management & Data Systems, Physica A, IEEE Sensors Journal, SIMULATION, International Journal of Mobile Communication and others.

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