Teleconsultation management in healthcare during the COVID-19 pandemic: The impact of Perceived Justice on satisfaction and Word-Of-Mouth

Abstract

Following the restrictions on movement imposed during the COVID-19 pandemic, the greater demands placed on physicians and many healthcare infrastructures, a large number of health teleconsultation platforms have emerged around the world. The aim of this paper is to obtain a better understanding of the acceptance of teleconsultation platforms by patients. To this end, a survey of 1273 patients was carried out using a teleconsultation platform during the pandemic period in France (from 9 July to 29 October 2020). The questionnaire mobilized existing scales to measure Satisfaction, Perceived Justice and, finally, the intention to disseminate by Word-Of-Mouth (WOM). Data were analysed using a Partial Least Squares approach. The study confirms the impact of Distributive, Informational and Procedural Justice on Satisfaction and the impact of Distributive and Informational Justice on WOM. The findings emphasize the influence of Satisfaction on WOM. The results detect a mediating effect of Satisfaction on WOM and moderating effects of gender, age and long-standing use. This research contributes to both theoretical and practical COVID-19 research and may be used by healthcare professionals to develop teleconsultation services, one of the means of supporting interaction and satisfying patients’ treatment requirements during the pandemic.

Keywords

COVID-19 teleconsultation e-health word-of-mouth perceived justice disruptive technologies

Introduction

COVID-19 (COrona VIrus Disease, 2019) has spread throughout the world, infecting millions of people and causing thousands of deaths (Jin et al., 2020). The rapid level of transmission is mainly due to the concentration of populations and the ease of global travel. To avoid being infected by others, individuals modified their behaviour by trying to reduce direct and close contact with others and by adopting Information and Communication Technologies (ICT). For example, retailers increasingly introduced a click-and-collect option during the lockdowns to maintain their sales and continue to deliver to their customers. Healthcare services also introduced changes involving the digital transformation of patient care using e-services, robots and more precise Health Information Technology, which, even without the current health crisis, increased patient satisfaction and patient experiences (Kraus et al., 2021). Electronic health (eHealth) is the network of technological applications relating to health issues such as teleconsultation. According to Catapan et al. (2021), alternatives to face-to-face medical consultation are already being used in many countries. Healthcare is facing the arrival of a new range of systems, services and applications using electronic communication (Van Den Heuvel et al., 2018) implying both a strong platform credibility (Song et al., 2022) and trust in the medical staff (Guan et al., 2018). Many patients tried to avoid going to hospital or visiting their family doctor during the pandemic and preferred to use telemedicine applications or the Internet of Things (Catapan et al., 2021; Lepore et al., 2018). Using these applications, patients and physicians can communicate 24/7 via a smartphone or a computer equipped with a webcam (Hollander and Carr, 2020).

Telemedicine, including teleconsultation, is based on interactive audio-visual communications (Farahani et al., 2018). This concept allows patients and medical staff to interact, exchange information and share documents, such as medical results, to help the physician to carry out a remote diagnosis and suggest an appropriate treatment. In addition, improving patient empowerment should facilitate the patient engagement in medical decision-making to improve the quality of care and patient safety (Fumagalli et al., 2015; Schwappach, 2010). As reported by Okereafor et al. (2020), telemedicine and online health platforms can potentially protect outpatient practitioners and patients from COVID-19 exposure. Indeed, the main advantages of teleconsultation, as raised in previous research, are mainly the avoidance of contamination, time and cost savings (no travel) and the ability to obtain an early appointment online (Baudier et al., 2021). Zheng, Chang and Chen (2022) demonstrate the impact of paid or free teleconsultation on intention to consult when perceived risks are higher for the free group and the influence of trust more important for the paid group. Mainly due to the fact that the paid group before the consultation tends to check the professional capabilities.

The French Government has supported teleconsultation solutions, even before the outbreak of COVID-19, as since 15 September 2018, medical consultations via videoconferencing in France have been reimbursed at the same rate as physical consultations. During the pandemic, medical practices were able to recover 100% of the cost.

Investigating this field of research is important for three major reasons: (1) The contextual importance, since the pandemic breakout the researchers notice the increased use of the teleconsultation and the difference in the acceptance factors of the teleconsultation before sanitarian crisis (Baudier et al., 2021; Tsai et al., 2019) and after (Baudier et al., 2021); (2) Most of the research focussed on particular medical area, such as, diabetes (Banerjee et al., 2020), urology (Pinar et al., 2020) on physicians’ perception (Chen et al., 2020; Guo et al., 2017) or technology acceptance of teleconsultation using the UTAUT, TAM (e.g. Rahi et al., 2020; Shiferaw et al., 2021) but not on satisfaction; and, finally, (3) The sample size is often low around 100 respondents (Pinar et al., 2020; Shenoy et al., 2020) and over-represented (only men over 66 years old) which could be a concern for the generalization of results.

Current research contributes to methodological robustness thanks to the sample size (1273) collected through a teleconsultation platform with a focus on physicians and theory mobilized (Perceived Justice).

The acceptance and use of remote medical consultation are directly linked to the physician–patient relationship (Lega et al., 2018) especially in terms of their level of listening/empathy (Interpersonal Justice) (Ćwiklicki et al., 2020) or the information provided (Informational Justice), the procedure used (Procedural Justice) and, finally, the service/result obtained (Distributive Justice). The Perceived Justice theory is often mobilized by researchers to measure these dimensions such as Meyer and Williamson (2020) to analyse the impact of negative service encounter or the ethical aspect of Distributive Justice in cancer treatment according to cultural, societal and individual norms (Hadler and Rosa, 2018). Others focussed on the healthcare professionals’ perception (Ghasi et al., 2020). Indeed, according to Pérez–Arechaederra (2019: 289) ‘few works consider users’ justice perceptions in healthcare’ (Pérez–Arechaederra, 2019: 289), as research mainly focus on the accesses to resources (hospital, medical care…) rather than user’s experiences. Nevertheless, recent research emerged to develop the doctor–patient paradigm. Indeed, Le et al. (2019), Chang et al. (2019), and Xing et al. (2019) analysed the doctor–patient relationship by demonstrating that the satisfaction is predicted by the Perceived Justice and results in intention of an offline visit. All three papers mobilized the four dimensions of the Perceived Justice; treat the sample from the Chinese platforms, before COVID-19 and without covering the Word-of-Month dimension. McCollough, Berry and Yadav (2000) and Mattila and Patterson (2004) identified two dimensions namely, Distributive and Interactional. Other research has identified four dimensions: Procedural, Distributive, Interactional and Informational (Chang et al., 2019; Le et al., 2019; Mengstie, 2020; Steiner, 2017). Some studies identified three dimensions, named Procedural, Distributive and Interactional that includes the Interpersonal and Informational Justices (Baudier and Ammi, 2016; De Meyer et al., 2013; Ghasi et al., 2020). Thus, the objective of this study is to use the four dimensions of Perceived Justice (Procedural, Distributive, Interpersonal and Informational) to analyse the satisfaction of patients using a teleconsultation platform to contact a physician during the COVID-19 pandemic in France and their willingness to share their experience via Word-of-Mouth (WOM).

The findings highlight the influence of Satisfaction on WOM, the mediating effect of Satisfaction on WOM and the moderating effects of gender, age and long-standing use. These major contributions might be used by healthcare professionals to develop teleconsultation services, one of the means of supporting patient-doctor interactions and satisfying patients’ treatment requirements during the pandemic.

This article is organized as follows: Firstly, a literature review of the constructs mobilized in the study is presented, followed by hypotheses. Secondly, the methodology and the sample strategy are explained. Thirdly, results are analysed and discussed. Fourthly, conclusion highlights the theoretical and managerial implications of the study. Finally, limitations of the study are raised and future research directions are suggested.

Literature review

Perceived justice

The concept of Perceived Justice emerged from Equity Theory, which refers to customers’ perceptions regarding an exchange, which is considered as fair or not fair in comparison with previous inputs and outcomes (Adams, 1965). Thus, the fairness of the procedures, the interpersonal communications and activities and the outcome are the primary antecedents of customer evaluations (Ding and Lii, 2016). According to Van Knippenbert et al. (2007), how people interact with others can influence their perceptions. This concept has often been adopted with regard to service failures (Blodgett et al., 1993; Sabadie, 2000) and to evaluate marketing impact (Söderlund and Colliander, 2015). Indeed, interest in Perceived Justice theory is growing within the management field (Méatchi and Camus, 2018). Many researchers have demonstrated that Perceived Justice is a multidimensional concept, incorporating three dimensions, namely, Distributive, Procedural and Interactional Justice (e.g. Aggarwal and Larrick, 2012; Maxham and Netemeyer, 2003) or four dimensions such as Distributive, Procedural, Interpersonal and Informational Justice (Chang et al., 2019; Le et al., 2019; Mengstie, 2020; Steiner, 2017). Although the dimensions are independent, analysis of all dimensions is recommended to determine the perception of justice (Smith et al., 1999). Some researchers have studied the impact of Perceived Justice in relation to employees’ health (Robbins et al., 2012), stress and employee well-being (Steiner, 2017), or, more recently, in relation to ethics and eHealth (Nguefack et al., 2020). Articles mobilizing the Perceived Justice theory in the health context are summarized in Table 1.

Table 1.

Perceived Justice applied to the health context.

Application domain	Related research
Health professionals and healthcare workers	Ghasi et al. (2020); Mengstie (2020)
Digital health and ethical decisions	Winters et al. (2020)
EHealth and health communication	Chang et al. (2019)
eHealth, online healthcare services	Le et al. (2019)
Health decision making	Pérez–Arechaederra (2019)
eHealth, doctor–patient interaction and online consultation	Xing et al. (2019)
Global health and bioethics in palliative care	Hadler and Rosa (2018)
Health, well-being and health-promoting work environments	Steiner (2017)
Health communication, vaccination and health behaviour	Lucas and Pierce (2018)
Healthcare industry	De Meyer et al. (2013)
Perception and hospital	Bahrami et al. (2013)
Primary health insurance fund	Sabadie et al. (2006)
Rural healthcare	Bauer (2003)

Distributive justice

Distributive Justice, based on the principle of equity, refers to the expected results regarding the proposed solution/service (Hocutt et al., 2006). It results from a comparison between the result/solution compared to previous experience (De Pechpeyrou et al., 2019; Klaus and Nguyen, 2013; Orsingher et al., 2010). According to Choma et al. (2018), Distributive Justice mediates the relationship between political orientation and the Affordable Care Act (Obamacare). Some studies have mobilized Perceived Justice in studies of the health sector focussing on medical staff. For example, Mengstie (2020) reports that healthcare workers in public hospitals have low perceptions of Distributive Justice, Procedural Justice and Interactional Justice (combining both the Interpersonal and Informational dimensions). For their part, Ghasi et al. (2020) indicate that although increasing age leads to a high perception of Distributive Justice by doctors, age is inversely related to perceptions of Distributive Justice by nurses.

Procedural justice

Lind and Tyler (1988) define Procedural Justice as the methods by which a service can be achieved, such as the speed of response, clarity and accessibility (Blodgett et al., 1997). Leventhal (1980) defined the procedural principles, including uniformity of application, impartiality, accuracy of information, respect for ethics and the opportunity to be part of the process that allows patients to be heard (Del Rio–Lanza et al., 2009). According to Mehra (2018), an increase in consultation time and expertise had an impact on Satisfaction and Word-Of-Mouth.

Interpersonal justice

Interpersonal Justice can be seen as the degree to which authority figures treat subordinates in a respectful manner (Greenberg, 1990). Indeed, Interpersonal Justice, as indicated by Alikaj and Hanke (2021), focuses primarily on behaviour and represents the degree to which people are treated with politeness, dignity and respect by the authorities. Thus, the interpersonal dimension is important for the avoidance of bad faith or lack of respect and their impact on decision-making processes. (Bahri–Ammari and Bilgihan, 2017; Folger and Cropanzano, 1998).

Informational justice

The informational dimension refers to the content of the communication. Kernan and Hanges (2002) formalized Informational Justice as the provision of accurate information with transparent communication. Ellis et al. (2009) view it as the degree to which information is shared openly. Based on Alikaj and Hanke (2021), Informational Justice, focussing on the degree of veracity and timeliness of the explanations provided to those who transmit information, can positively affect individual’s perception. Therefore, Informational Justice, related to authenticity and clarity of information (Colquitt et al., 2001; Greenberg, 1990), reflects the fair distribution of information related to procedures or communication (Lee and Ha, 2020).

Hypotheses

Perceived justice and satisfaction

Satisfaction is closely related to how the consumer was treated and whether he/she considers the problem was solved (Baudier and Ammi, 2016). According to several studies, the three dimensions of Perceived Justice have a direct impact on Satisfaction (Orsingher et al., 2010; Sabadie et al., 2006). Thus, Perceived Justice is a cognitive antecedent explaining Satisfaction. Many researchers have demonstrated the influence of Distributive Justice on Satisfaction (Homburg and Fürst, 2005; Orsingher et al., 2010; Sabadie et al., 2006; Smith et al., 1999; Van Vaerenbergh and Orsingher, 2016). According to Cohen (1988), only Distributive Justice has a moderate impact on Satisfaction. Nevertheless, Maxham and Netemeyer (2003) demonstrate that both Distributive and Procedural Justice dimensions are the antecedents of Satisfaction in claims processing. However, Procedural Justice rarely represents the most effective means of influencing consumer satisfaction (Muhammad and Rana, 2020). Others suggest that Interactional or Procedural Justice are the most significant factors in consumer satisfaction (Voorhees and Brady, 2005). However, Jung and Seock (2017) confirm the importance of Procedural Justice.

Several studies demonstrate that Informational Justice is positively related to post-recovery satisfaction (Gohary et al., 2016; Kussusanti et al., 2019). Indeed, perceived Informational Justice show positive associations with Satisfaction (Vaamonde et al., 2018). These findings indicate that providing adequate, timely and practical explanations of the decision-making process acts as a motivator for employees (Colquitt et al., 2013; Silva and Caetano, 2016), thereby enhancing job satisfaction. Finally, Interpersonal Justice has been found to be positively related to job satisfaction, organizational commitment, task performance, organizational citizenship behaviour and trust in supervisors (Colquitt et al., 2001, 2013)

Given these earlier results, the authors consider that

Distributive Justice has a positive impact on Satisfaction.

Interpersonal Justice has a positive impact on Satisfaction.

Informational Justice has a positive impact on Satisfaction.

Procedural Justice has a positive impact on Satisfaction.

Perceived justice and Word-Of-Mouth

Word-Of-Mouth (WOM) is generally defined as ‘all informal communications directed at other consumers about the ownership, usage, or characteristics of particular goods and services or their sellers’ (Hennig–Thurau and Walsh, 2004: 51). According to previous research, consumers are prone to rely on WOM information to reduce perceived risks prior to service use (Haywood, 1989). Several researchers found that Perceived Justice has a direct impact on WOM (Jung and Seock, 2017; Setyawati, 2019). Nevertheless, other studies found that Interactional and Distributive Justices are stronger factors in the variance of negative WOM than Procedural Justice Factors (Blodgett et al., 1997). According to Davidow (2003), Distributive Justice has the greatest impact on WOM, results confirmed by Namkung and Jang (2010), who reported that positive perceptions of equity generate more positive WOM.

According to Ogonu and Hamilton–Ibama (2020), there is a direct relationship between Interpersonal Justice and WOM. Aguilar–Rojas et al. (2015) report that there are significant differences in WOM intention to complain to a third party in an environment in which consumers receive Interpersonal Justice. With regards to Informational Justice, researchers suggest that Informational Justice results in more favourable WOM intentions (Blodgett et al., 1997). Moreover, others postulate that higher the perception of Informational Justice is, higher will be positive WOM (Blodgett et al., 1993; Clemmer, 1993). Finally, Bhatti and Khattak (2015) find that Informational Justice is significantly moderating the relationship between consumer relationship proneness and WOM after service failure and recovery.

Thus, the authors postulate that

Distributive Justice has a positive impact on WOM.

Interpersonal Justice has a positive impact on WOM.

Informational Justice has a positive impact on WOM.

Procedural Justice has a positive impact on WOM.

Satisfaction and Word-Of-Mouth

According to Lai et al. (2018), WOM is understood as an active form of advocacy for promoting products or services to friends and relatives. Pandey and Deshwal (2018) actively support the finding that satisfaction emphasizes loyalty and WOM. Research shows that satisfied and dissatisfied customers often informally report their level of Satisfaction via WOM (Jung and Seok, 2017). Indeed, Collier and Bienstock (2006) found that dissatisfied consumers are more likely to be involved in negative WOM than consumers who encounter appropriate services are to engage in positive WOM (Afridi et al., 2018; Al-Daoar and Munusamy, 2019; Yasri and Engraini, 2018). Therefore, the level of Satisfaction and the intention to recommend a service provider are highly correlated (Juliarta et al., 2019). It has been demonstrated that, within the medical community, patients still prefer to rely on subjective information, such as WOM provided by other patients (Varkevisser et al., 2012), and Aljaberi et al. (2018) have tested and proved the positive relationships between service quality, patient satisfaction and behavioural intentions in different healthcare contexts. Based on our literature review, the authors propose the following hypothesis:

Satisfaction has a positive impact on WOM

All the hypotheses are summarised in the Figure 1.

Figure 1.

Research model with hypotheses.

Methodology

Research design

A quantitative approach was used to assess the relationships between the constructs chosen for our research model. The questionnaire (Table A1 in Appendix 1) was built using existing scales mobilized by Baudier and Ammi (2016) adapted to test the hypotheses (Table 2). Firstly, the four dimensions of Perceived Justice were mobilized using four items of the Distributive Justice scale, five items of the Procedural Justice scale, three items of the Interpersonal Justice scale and, finally, the three items of the Informational Justice scale The Satisfaction scale was measured using five items and four items were selected to measure the scale of WOM. All items were measured using a 5-point Likert scale from strongly disagree=1 to strongly agree=5.

Table 2.

Scales mobilized.

Theory	Sub-constructs	Definition	Scales used	No of items	Origin of scales
Perceived justice	Distributive justice	Perception of fairness perceived regarding service obtained	Baudier and Ammi (2016)	4	Blodgett et al., 1997; Sabadie et al., 2006
	Procedural justice	Perception of fairness regarding the adaptability of processes during the service to fit user’s expectation	Baudier and Ammi (2016)	5	Sabadie et al., 2006; Tax et al., 1998
	Interpersonal justice	Perception of the person in charge of the service	Baudier and Ammi (2016)	3	Blodgett et al., 1997; Sabadie et al., 2006; Tax et al., 1998
	Informational justice	Perception regarding the Information provided	Baudier and Ammi (2016)	3	Sabadie et al., 2006; Tax et al., 1998
Satisfaction		Level of satisfaction of patients regarding the fact that using this product/service was the right thing to do	Baudier and Ammi (2016)	5	Vanhamme (2002); Zeng et al. (2009)
WOM		The intention to recommend a service/product and to disseminate positive WOM.	Baudier and Ammi (2016)	4	Zeng et al. (2009)

Population and sample

To avoid ethical issues, several elements in the structure and the mode of distribution of this questionnaire have been introduced. Firstly, the survey is not compulsory and is based on the free will of the respondents. Secondly, this questionnaire is anonymous. Thirdly, there is no question concerning the reasons for the teleconsultation and no medical data have been collected. Fourthly, the purpose of the survey was clearly indicated in a short introduction. Finally, the survey was disseminated via the newsletter of a teleconsultation platform to those patients who agreed to receive it, the authors did not access to patients’ e-mail addresses. Data were collected mobilizing a self-administered questionnaire using a Computer-Assisted-Web-Interviewing (CAWI) method. Ex-ante and ex-post tests were run to limit the Non-response Bias. Regarding the ex-ante measures, analyses were done at an item and unit level. For the item level, the responses to all questions were mandatory, so the authors did not have to face a lack of response. For the non-response at a unit level, all respondents were users of the teleconsultation platform and had sufficient experience of the platform to answer the questions. An introduction, including the objective of the survey, the completion time and the name of all researchers involved in the study was added at the beginning of the questionnaire. A first link to the survey was included in the July online newsletter and a reminder was sent beginning of September covering a period of 3 months. Regarding the ex-post measure, the comparison between the two hundred initial and the two hundred late respondents (from 0.09 to 0.23) confirmed the similarity, thus, the consistency of the responses. In addition, as confirmed with the teleconsultation platform, the demographic characteristics of the sample complies with their patient’s profile.

A total sample of 1273 respondents was collected between July 9 and 29 October 2020.Ninety-one percent of the patients clicking on the link answered to the questionnaire. The gender breakdown demonstrates a large proportion of females, representing 63% of the respondents, with the assumption that women are usually more interested in health topics than men (Waldron, 1988). All strata of the population are represented; nevertheless, the younger generation (18–25 years) are less well represented, mainly because they consult physicians less frequently than the older generations and are less concerned about the COVID-19 pandemic. Regarding the professional status of the sample, most respondents are active (63%). As regards the use of the teleconsultation platform, 40% of respondents had experienced more than two online consultations and the majority of respondents had started to use a teleconsultation solution during the COVID-19 period. Finally, 42.3% declared that using teleconsultation allowed them to avoid using the hospital emergency service. The sample characteristics are presented in Table 3.

Table 3.

Sample characteristics.

Gender	N = 1273	%
Female	800	63%
Male	473	37%
Age	N = 1273	%
18–25 years old	29	2%
26–35 years old	159	12%
36–45 years old	267	21%
46–55 years old	325	26%
56–65 years old	264	21%
66 and above	229	18%
Teleconsultation use	N = 1273	%
Less than 3 months	278	22%
3 months to less than 6 months	392	31%
6 months to less than 12 months	412	32%
More than 12 months	191	15%
Professional situation	N = 1273	%
Employee	442	35%
Executive	361	28%
Retired	317	25%
Unemployed	33	3%
Students	18	1%
Other	102	8%

Results

Structural Equation Modelling using a Partial Least Square approach was mobilized to analyse the relationships within the research model and to test the hypotheses using SmartPLS3 software (v3.3.3). This method has become popular in recent decades mainly due to the number of measurements provided for controlling for reliability, validity and the quality of the research (Path weighting Scheme, Maximum Iterations of 300) and for the calculation of moderating effects (Multi-Group-Analysis, Subsamples 5000).

Outer model

Even when the recommended threshold for the Variance Inflation Factor (VIF) is around 3.3, according to Hair et al. (2014: 200), ‘The suggested cut-off for the tolerance value is .10 (or a corresponding VIF of 10.0), which corresponds to a multiple correlation of .95 with the other independent variables. When values at this level are encountered, multicollinearity problem are almost certain’. A VIF exceeding 10 indicated serious multicollinearity, requiring modification. Thus, the following items (DJ4, PJ1, PJ3, INFJ1, SAT2, SAT3, SAT4, SAT5, WOM1, WOM2 and WOM3) have been removed from the model.

Finally, Satisfaction and WOM are measured using a single item. Previous research demonstrate that the reliability and validity of variables such as Satisfaction or Word-of-Month are similar than when mobilizing multi-items (Cheung and Lucas 2014; Joshanloo and Jovanović, 2020; Jovanović and Lazić 2020; Long et al., 2021).

Then, the reliability of the model was verified. Firstly, the analysis confirmed that all loadings were above 0.7. Secondly, the Cronbach’s alpha and Composite Reliability values all reached the recommended threshold of 0.70, certifying the reliability of the outer model. The Average Value Extracted (AVE) – all above 0.5 – confirmed the convergent validity of the research model. The discriminant validity was verified by controlling that the square root (√) of the AVE of each construct was not more correlated with other constructs (Table 4).

Table 4.

Discriminant validity.

	Distributive justice	Informational justice	Interpersonal justice	Procedural justice	Satisfaction	Word-of-Month
Distributive	0.921
Informational	0.836	0.973
Interpersonal	0.771	0.886	0.943
Procedural	0.776	0.822	0.832	0.872
Satisfaction	0.835	0.823	0.771	0.764	1.000
Word-of-Month	0.815	0.787	0.740	0.726	0.850	0.999

The discriminant validity was also controlled by analysing the Heterotrait–Monotrait ratio of the correlations (HTMT=0.76–0.9). Cohen (1988) suggests a maximum value of 0.9 (HTMT_.90). Nevertheless, when the values are closed to 0.9 it is recommended to run a complete bootstrapping procedure (confidence intervals). A confidence interval with the value 1 shows a lack of discriminant validity. As indicated in Table 5, all the HTMT values were below the threshold of 1 (Franke and Sarstedt, 2019; Gaskin et al., 2018; Teo et al., 2008).

Table 5.

Heterotrait-Monotrait ratio – Confidence Intervals.

	Original sample (O)	Sample mean (M)	2.5%	97.5%
INFJ->DJ	0.897	0.897	0.877	0.916
INTJ->DJ	0.826	0.826	0.798	0.853
INTJ->INFJ	0.937	0.936	0.920	0.952
PJ->DJ	0.877	0.877	0.847	0.906
PJ->INFJ	0.915	0.915	0.893	0.934
PJ->INTJ	0.929	0.929	0.902	0.952
SAT->DJ	0.872	0.872	0.849	0.891
SAT->INFJ	0.846	0.845	0.822	0.866
SAT->INTJ	0.793	0.792	0.764	0.820
SAT->PJ	0.829	0.829	0.793	0.862
WOM->DJ	0.851	0.851	0.829	0.873
WOM->INFJ	0.809	0.809	0.781	0.834
WOM->INTJ	0.760	0.759	0.729	0.787
WOM->PJ	0.786	0.786	0.750	0.818
WOM->SAT	0.850	0.850	0.826	0.871

The VIF values of the inner model in Table 6 are below the suggested cut-off of 10 as indicated above.

Table 6.

Inner VIF values.

	Satisfaction	Word-of-Month
Distributive	3.6	4.4
Informational	6.5	6.8
Interpersonal	5.5	5.5
Procedural	3.9	3.9
Satisfaction		4.1

These findings confirm the reliability and validity of the outer research model.

Inner model

The inner research model was measured by analysing for each of the dependent variables the explained variance (R²) and predictive relevance (Q²). The Q² were controlled using the Blindfolding procedure (Omission Distance 7). The relationships between variables were analysed (Bootstrapping – Subsamples 5000) by ensuring that the following parameters were above their recommended thresholds: path coefficient (path-coef >at 0.200), t-value (> 1.96) and p-value (< .05) (Figure 2). Then, the size effect of each of the independent variables on the dependent variables was analysed by controlling for the size effect (f²).

Figure 2.

Inner model. p < .05* p < .01** p < .001***.

The R² (0.757) confirms that the outer model explains a huge amount of Satisfaction defined by Distributive, Informational and Procedural Justice but not by Interpersonal Justice. The impact of Informational and Procedural Justice is positive, direct and significant, but given that the size effect was considered as small^Plus (PJ:f² = 0.016; INFJ: f² = 0.050), the limited influence of both Informational and Procedural Justice on Satisfaction was confirmed. However, with a f² at 0.228, Distributive Justice was shown to have a high^Minus impact on Satisfaction. Finally, the influence of Interpersonal Justice on Satisfaction is not significant. Thus, hypotheses H1, H3 and H4 are validated and H2 rejected. Finally, the model explains 76.6% of Word-Of-Mouth as determined by Satisfaction, Distributive and Informational Justice, with a huge size effect of Satisfaction (f² = 0.249), but not by Interpersonal Justice and Procedural Justice Therefore, hypotheses H5, H7 and H9 are supported and hypotheses H6 and H8 are rejected (Table 4). The predictability of the model (Q²) was tested by running the Blindfolding procedure and analysing the cross-validated Redundancy. The Q² values of Satisfaction (0.750) and Word-Of-Mouth (0.758) were above 0 and close to 1, which confirmed the large predictive relevance of the inner model. The q², measuring the size effect of variables on the predictability of the model (Q²), confirmed the moderate^Plus (0.26) impact of Satisfaction on WOM and the low^Plus impact of Distributive Justice on both Satisfaction (0.08) and Word-Of-Mouth (0.07). Thus, as indicated in Table 7, of the nine hypotheses postulated, six are supported (X) and three are not supported (O).

Table 7.

Inner model.

Construct	Predictor variable	R ²	f²	Path coef	t-Value	p-Value	Q²	H
SAT	DJ	0.757	0.228	0.447	12.235	.000	0.750	X
	INTJ		0.004	0.073	1.781	.075		O
	INFJ		0.050	0.282	6.121	.000		X
	PJ		0.016	0.124	3.177	.001		X
WOM	DJ	0.766	0.069	0.269	7.491	.000	0.758	X
	INTJ		0.002	0.052	1.477	.140		O
	INFJ		0.006	0.095	2.254	.024		X
	PJ		0.001	0.022	0.680	.497		O
	SAT		0.249	0.491	11.857	.000		X

Moderating effect

The Multi-Group-Analysis procedure was used to control the potential moderating effect of gender, age and experience of patients (previously patients before the COVID-19 crisis or not) on the relationships of the research model using the bootstrapping process. To analyse the moderation effect, path coefficients, t-values and p-values were analysed. Results show a moderating impact for gender. Results highlight that the relationship between Informational and Interpersonal Justices and World-of-Month is supported by Male and not supported by Female and that gender has an impact on the relationship between Procedural Justice and Satisfaction supported for Female and not by Male (Table 8)

Table 8.

Moderating effect of gender.

	Male			Female
	Path-coef	t-Value	p-Value	Path-coef	t-Value	p-Value
INFJ->WOM	0.182	2.493	.012	0.063	1.216	.224
INTJ->WOM	0.118	2.371	.018	0.029	0.666	.505
PJ->SAT	0.095	1.483	.138	0.146	3.119	.001

Regarding age, the group of respondents aged between 18 and 25 years was not included in the analysis due to the low sample size (29 respondents). Results highlights that the influence of Informational Justice (INFJ) on Satisfaction (SAT) is supported for all group of ages except for the 36–45 years old group and the impact of Informational Justice on WOM is only supported by the 56–65 years old. Findings confirm that only the relationship between Procedural Justice (PJ) and Satisfaction is supported by respondents aged between 36 and 55 years and over 65 years but rejected by the 26–35 years age group and the 56–65 years old group (Table 9).

Table 9.

Moderating effect of age.

	26–35			36–45			46–55			56–65			More than 66
	Path-coef	t-Value	p-Value	Path-coef	t-Value	p-Value	Path-coef	t-Value	p-Value	Path-coef	t-Value	p-Value	Path-coef	t-Value	p-Value
INFJ -> SAT	0.295	2.623	.008	0.121	1.297	.195	0.262	2.587	.009	0.445	4.719	3.076	0.341	3.137	.001
INFJ -> WOM	0.013	0.1221	.902	0.080	0.792	.428	0.070	0.818	.413	0.245	3.247	.001	0.048	0.429	.667
PJ -> SAT	(-0.115)	1.087	.277	0.178	2.216	.027	0.208	2.419	.015	0.041	0.577	.563	0.181	2.541	.011

As regards the moderating impact of patients’ experiences, the sample was segmented in two parts: the first group is represented by 191 respondents who started to use the platform more than 1 year ago (long-standing use), and the second group includes 670 patients who started to use the platform only 6 months before the survey was carried out. The respondents answered the questions between July and October 2020 and the COVID-19 crisis started in France at the end of January 2020. Hence, the first group with long-standing use started to use the platform before the crisis, whereas the second group started to use the platform after the beginning of the health crisis. Findings highlight that the relationship between Informational Justice and WOM is moderating by the experience as the relation is only supported by the less experienced patients (less than 6 months). Regarding the impact of Interpersonal on WOM opposite results were found as supported by the more experienced patients. Finally, results confirmed that the relationship between Procedural Justice (PJ) and Satisfaction was not supported by the more experienced users (more than 1 year) and was validated by the second group with less than 6 months of experience (Table 10).

Table 10.

Moderating effect of long-standing use.

	More than 1 year			Less than 6 months
	Path-coef	t-Value	p-Value	Path-coef	t-Value	p-Value
INFJ -> WOM	0.047	0.383	.701	0.124	1.967	.049
INTJ -> WOM	0.261	3.064	.002	0.010	0.208	.835
PJ -> SAT	0.064	0.822	.411	0.156	2.437	.015

Mediating effect

The potential mediating effect of Satisfaction for the four dimensions of Perceived Justice and WOM was analysed using the bootstrapping procedure. The analysis confirms the indirect effects except for Interpersonal Justice. As regards Distributive and Informational Justice, the relationships with both Satisfaction and WOM are direct, positive and significant and the indirect effects are also significant. Thus, based on Hair et al. (2017), a partial mediation is raised. However, the relationship of Procedural Justice to WOM is not significant when the indirect effect is significant. Thus, the authors can state that a full mediation is demonstrated.

Discussion

In this paper, the authors investigated the impact of Perceived Justice on patients’ satisfaction and WOM regarding medical teleconsultation during the COVID-19 pandemic. The concept of justice is often raised in studies of health topics (Jokinen et al., 2020). Telemedicine, which provides equity in access to treatment (Nguefack et al., 2020), involves a triadic relationship known as the 3Ps: patients, physicians and provider of the telemedicine platform (Leone et al., 2021). The investigation of the patient-doctor relationships mobilizing the Perceived Justice impact on satisfaction is in line with previous research (e.g. Chang et al., 2019; Le et al., 2019). Three of the Perceived Justice dimensions, namely Distributive, Procedural and Informational Justice had an influence on Satisfaction confirming other studies regarding medical services (e.g. Lee, 2021). Indeed, Liang et al. (2017) found a direct impact of Distributive and Informational Justice on patients’ Satisfaction when implementing a hospital information system, where Procedural Justice had only an indirect impact through Interactional Justice. During the pandemic, medical teleconsultation was perceived as necessary to prevent transmission of the virus (Baudier et al., 2021; Mustafa et al., 2020). However, in the medical context, the doctor–patient relationship is central, and one of the concerns during COVID-19 was the lack of interaction between the patient and physician because of the risk of contamination. Teleconsultation is one way to support these interactions. Surprisingly the dimension of Interpersonal Justice had no impact on Satisfaction. Chang et al. (2019) also did not support the impact of Interpersonal Justice on Satisfaction. Certainly, the medical professional code requires the treatment of all patients in the equal way, the change of the interaction mode (Teleconsultation instead of physical appointment) did not change the interpersonal interaction quality.

Moreover, the level of Satisfaction in telemedical services increased drastically during the COVID-19 pandemic for diabetes patients and diabetes management (Kanc et al., 2020), ambulatory care (Layfield et al., 2020), or for patients with allergies (Fieux et al., 2020). Mustafa et al. (2020) argue that 97% of patients were satisfied with the service provided by teleconsulting platforms. They also found that patients were as satisfied with teleconsultation as they were with a physical consultation and, in some cases, patients demonstrated even greater Satisfaction (Ramaswamy et al., 2020). Finally, Satisfaction is often seen as a mediator of the relationship between Perceived Justice and Word-Of-Mouth (Homburg and Fürst, 2005; Orsingher et al., 2010; Xing et al., 2019).

Out of four measured dimensions of Perceived Justice, two of them, Distributive and Informational Justice had a significant impact on WOM, and two others, Interpersonal and Procedural, did not have assumed effect. Previous studies analysed the correlation between Perceived Justice and WOM, especially in the hospitality and tourism domain (Cai and Qu, 2018; Chih et al., 2017; Ortiz et al., 2017). However, Setyawati (2019) found the opposite results, that is no effect of Distributive Justice on WOM, but a significant impact for Interactional and Procedural Justice. Chen and Kim (2019) found that first-time users of shopping platforms are more sensitive to negative comments, compared to repeat users. Moreover, a mediation effect (full or partial) of Perceived Justice through Satisfaction on WOM was highlighted. Findings are aligned with other research in the health context, where Satisfaction mediated the relationships between the dimensions of Perceived Justice and final outcomes even if it is not WOM (Chang et al., 2019; Le et al., 2019; Xing et al., 2019).

Finally, this study validated the impact of Satisfaction on WOM, which is aligned with previous findings on health services. Al-Daoar and Munusamy (2019) found a significant effect of patients’ Satisfaction on WOM. Pang and Liu (2020) concluded that Satisfaction motivates patients to leave positive online reviews, while dissatisfaction leads to negative reviews.

This study also investigated the moderating effects of age, gender and experience.

Age is still an important moderator where the use of technology is concerned. Out of all relationships, age moderated only three: between Procedural Justice and Satisfaction, Informational Justice and Satisfaction, and Informational Justice and WOM. Two age groups (36–55 and over 66 years old) supported the relationships between Procedural Justice and Satisfaction. As Procedural Justice mainly describes the used method, that teleconsultation can provide, in this instance, contamination avoidance (Baudier et al., 2021). It is possible that the younger age group is generally less concerned about their health and COVID-19. The second group, representing the 56–65-year-olds, rejecting this relationship might have more health issues but prefer still face-to-face over teleconsultation. Nevertheless, the low impact of Procedural Justice on Satisfaction might be related to the need for personal interaction with a physician, as explained by Bozan et al. (2016); indeed, the positive attitude toward technology of elderly patients related directly to their relationships with the physician. Middle age group of 36–45 years old did not show any relation between the quality of information and teleconsultation satisfaction. Probably, this age has both characteristics, technology self-efficacy and medium level of medical consultation need, therefore, the information is perceived equally fair in teleconsultation as it could be by the physical appointment. Similarly, the effect of Informational Justice on WOM by older group (56–65) is explained by the wow-effect of the informational quality of the teleconsultation, so less comfortable with technology the older patients share their experiences more likely, specifically important during COVID-19.

According to Kempf and Palan (2006), gender plays a significant role in communication and WOM in consumer services or technology acceptance (Venkatesh et al., 2012). In this study, Informational and Interpersonal Justice had different effect on WOM among male and female respondents, unexpectedly supported among men. In older research, Procedural and Interactional Justice is more significant for women satisfaction with services (e.g. Martínez–Tur et al., 2001). However, recent research on information systems in a medical context demonstrated that gender has no impact on health mobile applications, or satisfaction of eHealth (Haluza and Wernhart, 2019; Ramaswamy et al., 2020). Besides, Procedural Justice had more significant impact on Satisfaction by female than by male users of teleconsultation, supported also by previous research stated that women are overall higher satisfied with teleconsultation (Tenforde, et al., 2020).

Finally, these findings demonstrated the impact of the user experience, expressed in how long patients have been using the platform. Less experienced users (less than 6 moths) demonstrated the sensitivity to Informational Justice to share their WOM, and Procedural Justice to their satisfaction. This could be explained by the fact that they are not yet comfortable with the remote medical services and they need to focus more on information and procedure used but they do not really care about the physician’s behaviour. At the same time the more experienced users (more than 1 year) were sensitive to Interpersonal Justice and ready to share their feelings and reactions through the WOM. Since they already used the teleconsultation before the pandemic, the technology including procedure or quality of information were not considered as a new experience, therefore they did not feel the necessity to share. Several authors did not find any moderation effects of age, gender or experiences on overall satisfaction of medical teleconsultation usage during the period of COVID-19 (Jannati et al., 2021; Pinar et al., 2020; Shenoy et al., 2020). Such user perceptions of teleconsultation may be the result of rapid implementation due to the health crisis (e.g. Layfield et al., 2020; Kanc et al., 2020).

Conclusion

During the COVID-19 pandemic, eHealth and teleconsultation solutions were enhanced in order to reduce the risk of transmission of the virus during physician–patient consultations, changing the way of communication between patients and healthcare professionals. These solutions meant to help people in their daily life by maintaining their health and well-being. Findings confirm the impact of the three dimensions of Perceived Justice on patient’s Satisfaction.

Firstly, the impact of Distributive Justice confirms that interviewed patients had a positive experience with the remote physician regarding the answer to their needs. Secondly, the effect of Procedural Justice on Satisfaction validates that teleconsultation solution fits their expectations regarding the implemented processes and the way it is organized. Thirdly, Informational Justice emphasizes the patients’ satisfaction regarding the information provided. Nevertheless, physician’s behavioural (Empathy, courtesy…) do not impact Satisfaction. Indeed, even if the doctor–patient relationship was considered as crucial and central in normal time; during the COVID-19 period the means to avoid contamination, as Teleconsultation, became more important than the relationships even in a medical context. Thus, results suggest that teleconsultation, as a possible solution to replace a physical consultation, satisfied patients before and during the COVID-19 pandemic. Finally, the Distributive Justice and Informational Justice are the only dimensions of Perceived Justice to impact WOM emphasising the idea that patients will not disseminate Positive WOM depending on physician’ behaviour (INTJ) or mobilised processes (PJ) but on obtained results. Lastly, this study identified the moderating effects of gender, age and experience (length of use) in the relationships between (1) Procedural Justice and Satisfaction (Age and Experience), (2) Informational Justice and WOM (Gender and Age) and (3) Informational Justice and Satisfaction (Age).

In the context of the health crisis, the use of teleconsultation could be justified by health authorities. Nonetheless, the physician–patient relationship (listening and empathy) remains essential. Satisfaction with telemedical services grew during the COVID-19 pandemic, and this can be considered as a mediator between Perceived Justice and Word-Of-Mouth.

Theoretical contributions

Pandemic COVID-19 became a trigger for the digitalization of services including in the healthcare sector. This paper analyses through the lens of Perceived Justice the triadic relationships between the patient, the physician and the provider of the platform as a third stakeholder. Some studies have investigated the perception of medical staff regarding e-health solutions mobilizing the concept of Perceived Justice. Perceived Justice is often applied in studies involving services. However, the analysis of patients’ perception is also essential in term of Perceived Justice and as far as the authors know, no studies have been conducted at the patient level. The four dimensions of Perceived Justice, to measure their impact on Satisfaction and WOM, were mobilized the Distributive, Informational, Interpersonal and Procedural Justice to analyse their effect independently. Thus, the major theoretical contribution of this research concerns methodology, research context and results. First strength of this paper is in the quality of collected data and the number of respondents. The respondents, using the teleconsultation before and during the sanitarian crisis, still had the choice of the consultation method, as physicians never stopped face-to-face consultation except in case of suspicion of COVID-19. The second strength of the paper is contextual. The study provides the insights of the teleconsultation Satisfaction during one of the biggest pandemics, the results might be compared with other periods, before and after. The unique situation justifies the results as useful for the future studies.

Managerial and practical contributions

As mentioned above, the current pandemic had a revolutionary effect on health sector by boosting the digitalization process and changing the way individuals can access to healthcare. Even if the concept of teleconsultation is not new and was launched before the sanitary crisis only few patients used it mainly for convenience (e.g. time saving, lack of physicians…) or because it was recommended by medical staff, for example for the patient follow up at home. The raise of Information and Communication Technology (ICT), especially with the advent of smartphones, computers, the internet but also smart technologies such as the internet of things or smart home, made the launch of teleconsultation solutions possible. This paper proposes several practical contributions for two of the identified stakeholders: the platform and healthcare management through physicians. Firstly, the platform managers should motivate physicians to continue to consult online and not to stop after the pandemic period, as patients except to have a quick appointment and this will be possible only if physicians propose time slots. In addition, they should incentivise the patients to share their positive online experience to attract new patients via WOM and also to consider their negative feedbacks to improve the functionalities of the platform or solve the potential issues raised.

For the healthcare management, the relationship between the physician and patients remains central. Indeed, all the dimensions of the Perceived Justice highlight the key role of the physicians for the Satisfaction, regarding the diagnostic and solutions proposed, their ability to consider patients’ opinions and finally their behaviour. Thus, some specific skills such as their ability to listen, their empathy are critical and must be reinforced for remote consultations. Thus, healthcare professionals and health regulators need to adapt and market their services to fit with patients’ expectations when integrating digital solutions.

The implementation of teleconsultation during COVID-19 was the appropriate solution for continuous medical support and should be considered in the future.

Limitations and future research

This study has some limitations, which can be regarded as opportunities for future research. Firstly, the survey focused on users only and it would be interesting to understand the factors predicting behaviour and to identify potential barriers for other patients who have not previously used teleconsultation services. Secondly, the data were collected in France, which is very favourable to teleconsultation in that the government offers 100% reimbursement to practitioners for the service during the COVID-19 pandemic. Hence, extending the study to other countries could produce different results. The third limitation is about obtained analysis running the SmartPLS3 software. Even if the VIF values are below the threshold of 10, some correlations are a little too high between the latent variables and could generate multicollinearity problems and thus, moderate part of our results. In addition, the heterotrait–monotrait ratio of correlations (HTMT) are also below but close to the maximum of 1, even if the two other measures of the discriminant validity are fine. This result could represent a limit. And, finally, the present results represent the situation during this pandemic, but they could change once the health crisis has subsided.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Patricia Baudier

Appendix

Table A1.

Measurement items.

Distributive Justice (DJ)

Regarding your teleconsultation, would you say that

DJ1. You got your questions answered

DJ2. Compared to a face-to-face experience with your doctor, you are satisfied with the response

DJ3. In comparison with the experience of those around as regards similar questions, you are satisfied with the answer from the teleconsultation doctor

DJ4. Overall, the doctor met your expectations during the teleconsultation

Procedural justice (PJ)

Regarding the teleconsultation process, would you say that

PJ1. The doctor gave you the impression of adapting his/her answers to better answer your questions

PJ2. You were able to give your opinion

PJ3. Your opinion was taken into account

PJ4. You were satisfied with the duration of your consultation

PJ5. You were able to quickly get an appointment

Interpersonal justice (INTJ)

Regarding the doctor, would you say that he/she

INTJ1. Was concerned about your problem

INTJ2. Showed a good level of listening

INTJ3. Was polite

Informational justice (INFI)

INFJ1. Tried to answer your health issue

INFJ2. Gave an answer that inspired you with confidence

INFJ3. Provided clear information about your health condition

Satisfaction (SAT)

Do you consider that

SAT1. Choosing teleconsultation was a good choice

SAT2. Contacting a teleconsultation platform was the right thing to do

SAT3. You are satisfied with the information provided by the physician

SAT4. You are satisfied with your contact (physician)

SAT5. Overall, you are satisfied with the service offered

Word-of-Mouth (WOM)

Would you be ready to

WOM1. Recommend the teleconsultation platform to those who ask for your advice

WOM2. Recommend the teleconsultation platform to your relatives

WOM3. Spread positive WOM to others about the teleconsultation platform

WOM4. Overall, recommend the use of a teleconsultation platform

References

Adams

(1965) ‘Inequity in social exchange’. In: Berkowitz

(ed) Advances in Experimental Social Psychology. New York, NY: Academic Press, Vol. 2, pp. 267–299.

Afridi

Haider

Alam

(2018) Impact of service quality on word of mouth communication: mediating role of customer satisfaction. Journal of Managerial Sciences. 12(1): 1–10.

Aggarwal

Larrick

(2012) When consumers care about being treated fairly: the interaction of relationship norms and fairness norms. Journal of Consumer Psychology 2(1): 114–127.

Aguilar-Rojas

Fandos-Herrera

Flavian-Blanco

(2015) What May lead you to recommend and revisit a hotel after a service failure instead of complaining? International Journal of Contemporary Hospitality Management 27(2): 214–235.‘

Al-Daoar

RMA

Munusamy

(2019) Effect of personnel care quality of private healthcare providers on Arab patients’ satisfaction and word-of-mouth communication: an empirical research in India’. International Journal of Engineering and Advanced Technology 9(1): 5374–5383.

Alikaj

Hanke

(2021) An examination of the link between leader motivating language and follower interactional justice. International Journal of Business Communication. DOI: 10.1177/23294884211011240.

Aljaberi

Juni

Al-Maqtari

, et al. (2018) Relationships among perceived quality of healthcare services, satisfaction and behavioural intentions of international students in Kuala Lumpur, Malaysia: a cross-sectional study. BMJ Open 8(9): e021180.

Bahrami

Hashemi Gazar

Montazeralfaraj

, et al. (2013) Determinants of perceived organizational justice among hospital employees. Interdiciplinary Journal of Contemporary Research in Business 5(7): 441–448.

Bahri-Ammari

Bilgihan

(2017) The effects of distributive, procedural, and interactional justice on customer retention: an empirical investigation in the mobile telecom industry in Tunisia. Journal of Retailing and Consumer Services 37: 89–100.

10.

Banerjee

Chakraborty

Pal

(2020) Teleconsultation and diabetes care amid COVID-19 pandemic in India: scopes and challenges. Journal of Diabetes Science and Technology 14(4): 714–715.

11.

Baudier

Ammi

(2016) Impact des dimensions de la justice perçue sur la satisfaction dans le cadre de la gestion des réclamations sur twitter: intégration de la dimension d’intimité sociale dans l'échelle de mesure de la justice interactionnelle. Revue française du marketing 23(257): 23–38.

12.

Baudier

Kondrateva

Ammi

, et al. (2021) Patients’ perceptions of teleconsultation during COVID-19: a cross-national study. Technological Forecasting and Social Change 163: 120510.

13.

Bauer

(2003) Distributive justice and rural healthcare: a case for e-health. The International Journal of Applied Philosophy 17(2): 241–252.

14.

Bhatti

Khattak

(2015) Antecedents and outcomes of consumer relationship proneness after service failure and recovery-moderating role of informational justice: a research on pakistani mobile telecom industry. Journal of Marketing and Consumer Research 14: 73–102.

15.

Blodgett

Granbois

Walters

(1993) The effects of Perceived Justice on complainants negative Word-Of-Mouth behavior and repatronage intentions. Journal of Retailing 69(4): 399–428.

16.

Blodgett

Hill

Tax

(1997) The effect of distributive, procedural and Interactional Justice on post-complaint behavior. Journal of Retailing 73(2): 185–210.

17.

Bozan

Parker

Davey

(2016). IEEE, pp. 3105–3114.A closer look at the social influence construct in the UTAUT Model: an institutional theory based approach to investigate health IT adoption patterns of the elderly. In: 49th Hawaii International Conference on System Sciences, Koloa, HI, 5–8 January 2016

18.

Cai

(2018) ‘Customers’ perceived justice, emotions, direct and indirect reactions to service recovery: moderating effects of recovery efforts. Journal of Hospitality Marketing & Management 27(3): 323–345.

19.

Catapan

Willemann

MCA

Calvo

MCM

(2021) Structure and work process for implementing medical teleconsultation in the Brazilian National Health System, a cross-sectional study using 2017-2018 data. Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil 30: e2020305.

20.

Chang

Hsu

Wang

, et al. Integration of online and offline health services: the role of doctor-patient online interaction. Patient Education and Counseling 102(10): 1905–1910.

21.

Chen

Kim

(2019) Role of the perceived justice of service recovery: a comparison of first-time and repeat visitors. Tourism and Hospitality Research 19(1): 98–111.

22.

Chen

Lan

Chang

, et al. (2020) ‘Exploring doctors’ willingness to provide online counseling services: the roles of motivations and costs. International Journal of Environmental Research and Public Health 17(1): 1–12.

23.

Cheung

Lucas

(2014) Assessing the validity of single-item life satisfaction measures: results from three large samples. Quality of Life Research 23: 2809–2818.

24.

Chih

Chiu

Lan

, et al. (2017) Psychological contract violation: impact on Perceived Justice and behavioral intention among consumers. International Journal of Conflict Management 28(1): 103–121.

25.

Choma

Barnes

Braun

, et al. (2018) Dissecting the politics of “Obamacare”: the role of distributive justice, deservingness, and affect. Journal of Applied Social Psychology 48(11): 634–642.

26.

Clemmer

(1993) An investigation into the relationship of fairness and customer satisfaction with services. In: Cropanzano

(ed) Justice in the Workplace: Approaching Fairness in Human Resource Management. Hillsdale, NJ: L.Erlbaum Associates, pp. 193–207.

27.

Cohen

(1988) Statistical Power Analysis for the Behavioral Sciences’. 2nd edition. Mahwah, NJ: Lawrence Erlbaum Associates.‘

28.

Collier

Bienstock

(2006) Measuring service quality in e-retailing. Journal of Service Research 8(3): 260–275.

29.

Colquitt

Conlon

Wesson

, et al. (2001) Justice at the millennium: a meta-analytic review of 25 years of organizational justice research. The Journal of Applied Psychology 86(3): 425–445.

30.

Colquitt

Scott

Rodell

, et al. (2013) Justice at the millennium, a decade later: a meta-analytic test of social exchange and affect-based perspectives. The Journal of Applied Psychology 98(2): 199–236.

31.

Ćwiklicki

Schiavone

Klich

, et al. (2020) Antecedents of use of e-health services in Central Eastern Europe: a qualitative comparative analysis. BMC Health Services Research 20(1): 171–215.

32.

Davidow

(2003) Organizational responses to customer complaints: What works and what doesn’t. Journal of Service Research 5(3): 225–250.

33.

De Meyer

Svensson

Petzer

, et al. (2013) Investigating perceived justice in South African health care. Journal of Contemporary Management 10(1): 156–175.

34.

De Pechpeyrou

Nicholson

Lakad

(2019) Réclamation et satisfaction: l’effort perçu du client rebat les cartes. Marketing Trends Congress: 1–17.

35.

Del Río-Lanza

Vázquez-Casielles

Díaz-Martín

(2009) Satisfaction with service recovery: Perceived Justice and emotional responses. Journal of Business Research 62(8): 775–781.

36.

Ding

Lii

(2016) Handling online service recovery: effects of perceived justice on online games. Telematics and Informatics 33(4): 881–895.

37.

Ellis

Reus

Lamont

(2009) The effects of procedural and informational justice in the integration of related acquisitions. Strategic Management Journal 30(2): 137–161.

38.

Farahani

Firouzi

Chang

, et al. (2018) Towards fog-driven IoT eHealth: promises and challenges of IoT in medicine and healthcare. Future Generation Computer Systems 78: 659–676.

39.

Fieux

Duret

Bawazeer

, et al. (2020) Telemedicine for ENT: effect on quality of care during Covid-19 pandemic. European Annals of Otorhinolaryngology, Head and Neck Diseases 137(4): 257–261.

40.

Folger

Cropanzano

(1998) Organizational Justice and Human Resource Management. Thousand Oaks, CA: Sage, 7.

41.

Franke

Sarstedt

(2019). Heuristic versus statistuics in discriminant validity testing: a comparison of four procedures. Internet Research 29(3): 430–447.

42.

Fumagalli

Radaelli

Lettieri

, et al. (2015) Patient Empowerment and its neighbours: clarifying the boundaries and their mutual relationships. Health Policy (Amsterdam, Netherlands) 119(3): 384–394.

43.

Gaskin

Godfrey

, et al. (2018) Successful system-use: It’s not just who you are, but what you do. AIS Transactions on Human-Computer Interaction 10(2): 57–81.

44.

Ghasi

Ogbuabor

Onodugo

(2020) Perceptions and predictors of organizational justice among healthcare professionals in academic hospitals in South-Eastern Nigeria. BMC Health Services Research 20: 301–312.

45.

Gohary

Hamzelu

Alizadeh

(2016) Please explain why it happened! How perceived justice and customer involvement affect post co-recovery evaluations: a study of Iranian online shoppers. Journal of Retailing and Consumer Services 31: 127–142.

46.

Greenberg

(1990) Organizational justice: yesterday, today, tomorrow. Journal of Management 16(2): 399–432.

47.

Guan

Wang

Jin

, et al. (2018) Knowledge contribution behavior in online Q&A communities: an empirical investigation. Computers in Human Behavior 81: 137–147.

48.

Guo

Fang

, et al. (2017) How doctors gain social and economic returns in online health-care communities: a professional capital perspective. Journal of Management Information Systems 34(2): 487–519.

49.

Hadler

Rosa

(2018) Distributive justice: an ethical priority in global palliative care. Journal of Pain and Symptom Management 55(4): 1237–1240.

50.

Hair

Jr Black

Babin

, et al. (2014) Multivariate Data Analysis, Pearson New International Edition. 7th edition. Essex, UK: Pearson, p. 729.

51.

Hair

Hult

GTM

Ringle

, et al. (2017) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd edition. Thousand Oaks: Sage.

52.

Haluza

Wernhart

(2019) Does gender matter? Exploring perceptions regarding health technologies among employees and students at a medical university. International Journal of Medical Informatics 130: 103948.

53.

Haywood

(1989) Managing word of mouth communications. Journal of Services Marketing 3(2): 55–67.

54.

Hennig-Thurau

Walsh

(2004) Electronic word-of-mouth: motives for and consequences of reading customer articulations on the internet. International Journal of Electric Commerce 8(2): 51–74.

55.

Hocutt

Bowers

Donavan

(2006) The art of service recovery: fact or fiction?. Journal of service marketing 20(3): 199–207.

56.

Hollander

Carr

(2020) Virtually Perfect? Telemedicine for Covid-19. The New England Journal of Medicine 382(18): 1679–1681.

57.

Homburg

Fürst

(2005) How organizational complaint handling drives customer loyalty: an analysis of the mechanistic and the organic approach. Journal of Marketing 69(3): 95–114.

58.

Jannati

Nakhaee

Yazdi-Feyzabadi

, et al. (2021) A cross-sectional online survey on patients' satisfaction using store-and-forward voice and text messaging teleconsultation service during the COVID-19 pandemic. International Journal of Medical Informatics 151: 104474.

59.

Jin

Liu

Cui

, et al. (2020) Novel coronavirus pneumonia emergency in Zhuhai: impact and challenges. The Journal of Hospital Infection 104(4): 452–453.

60.

Jokinen

Stolt

Suhonen

(2020) Ethical issues related to eHealth: an integrative review. Nursing Ethics 28(5): 969733020945765–19.

61.

Joshanloo

Jovanović

(2020) The relationship between gender and life satisfaction: analysis across demographic groups and global regions. Archives of Women's Mental Health 23(3): 331–338.

62.

Jovanović

Lazić

(2020) Is longer always better? A comparison of the validity of single-item versus multiple-item measures of life satisfaction. Applied Research in Quality of Life 15(3): 675–692.

63.

Juliarta

Hidayanto

Pinem

, et al. (2019) Complaint Handling through Social Media: Perceived Justice and Customer Satisfaction’, WBC 2019. London, UK: IADIS Press, pp. 301–308.

64.

Jung

Seock

Y-K

(2017) Effect of service recovery on customers’ perceived justice, satisfaction, and word-of-mouth intentions on online shopping websites. Journal of Retailing and Consumer Services 37: 23–30.

65.

Kanc

Komel

Kos

, et al. (2020) H(ome)bA1c testing and telemedicine: high satisfaction of people with diabetes for diabetes management during COVID-19 lockdown. Diabetes Research and Clinical Practice 166: 108285.

66.

Klaus

Nguyen

(2013) Exploring the role of the online customer experience in firms multi-channel strategy: an empirical analysis of the retail banking services sector. Journal of Strategic Marketing 21(5): 429–442.

67.

Kempf

Palan

(2006) The effects of gender and argument strength on the processing of word-of-mouth communication. Academy of Marketing Studies Journal 10(1): 1–18.

68.

Kernan

Hanges

(2002) Survivor reactions to reorganization: antecedents and consequences of procedural, interpersonal, and informational justice. The Journal of Applied Psychology 87(5): 916–28.

69.

Kraus

Schiavone

Pluzhnikova

, et al. (2021) Digital transformation in healthcare: analyzing the current state-of-research. Journal of Business Research 123: 557–567.

70.

Kussusanti

Tjiptoherijanto

Halim

, et al. (2019) Informational justice and post-recovery satisfaction in e-commerce: the role of service failure severity on behavioral intentions. The Journal of Asian Finance, Economics, and Business 6(1): 129–139.

71.

Lai

IKW

Hitchcock

, et al. (2018) The influence of word of mouth on tourism destination choice: Tourist–resident relationship and safety perception among Mainland Chinese tourists visiting Macau. Sustainability 10(7): 2114.

72.

Layfield

Triantafillou

Prasad

, et al. (2020) Telemedicine for head and neck ambulatory visits during COVID-19: evaluating usability and patient satisfaction. Head & Neck 42(7): 1681–1689.

73.

Chang

, et al. (2019) Why do patients move from online health platforms to hospitals? The perspectives of fairness theory and brand extension theory. International Journal of Environmental Research and Public Health 16(19): 3755.

74.

Lee

(2021) Informational justice, cognitive trust, and satisfaction: purchasers’ perspective of healthcare distribution market. Journal of Distribution Science 19(2): 5–14.

75.

Lee

(2020) The impact of interactional justice and supply-chain collaboration on sustainable scm performance: the case of multinational pharmaceutical firms. The Journal of Asian Finance, Economics, and Business 7(2): 237–247.

76.

Lega

Vendramini

Festa

, et al. (2018) Myth #4: The Healthcare System Can Be Fixed by Treating it More as a Business. Cham, Switzerland: Springer, pp. 103–121.

77.

Leone

Schiavone

Appio

, et al. (2021) How does artificial intelligence enable and enhance value co-creation in industrial markets? An exploratory case study in the healthcare ecosystem. Journal of Business Research 129: 849–859.

78.

Lepore

Metallo

Schiavone

, et al. (2018) Cultural orientations and information systems success in public and private hostitals: preliminary evidences from Italy. BMC Health Services Research 18(1): 554.

79.

Leventhal

(1980) What should be done with equity theory? In: Social exchange. Boston, MA: Springer, pp. 27–55.

80.

Liang

Tao

, et al. (2017) Influence of mechanism of patient-accessible hospital information system implementation on doctor–patient relationships: a service fairness perspective. Information & Management 54(1): 57–72.

81.

Lind

Tyler

(1988) The Social Psychology of Justice. New York, NY: Plenu.

82.

Long

Song

Duan

, et al. (2021) Research on the mechanism of the influence of E-WOM dispersion on consumers return intention. Journal of Physics: Conference Series 1944(1): 012023.

83.

Lucas

Pierce

(2018) Justice, perceived threat and vaccination intention in the USA. Health Promotion International 33(1): 27–37.

84.

Mattila

Patterson

(2004) Service recovery and fairness perceptions in collectivist and individualist contexts. Journal of Service Research 6(4): 336–346.

85.

Martínez-Tur

Ramos

Peiró

, et al. (2001) Relationships among perceived justice, customers’ satisfaction, and behavioral intentions: the moderating role of gender. Psychological Reports 88(3): 805–11.

86.

Maxham

III Netemeyer

(2003) Firms reap what they sow: the effects of shared values and perceived organizational justice on customers evaluations of complaint handling. Journal of Marketing 67(1): 46–62.

87.

McCollough

Berry

Yadav

(2000) An empirical investigation of customer satisfaction after service failure and recovery. Journal of Service Research 3(2): 121–137.

88.

Méatchi

Camus

(2018) L’injustice perçue à l'égard de la tarification par le revenue management : une analyse de ses manifestations affectives. Management & Avenir 99(1): 143–164.

89.

Mehra

(2018) Does consultation time influence patient perception of provider communication style, patient satisfaction, and word-of-mouth recommendation in India? A mediation analysis. Asia-Pacific Journal of Health Management 13(1): 26–38.

90.

Mengstie

(2020) Perceived organizational justice and turnover intention among hospital healthcare workers. BMC Psychology 8(19): 19.

91.

Meyer

Williamson

(2020) General and specific perceptions of procedural justice: factors associated with perceptions of police and court responses to domestic and family violence. Australian & New Zealand Journal of Criminology 53(3): 333–351.

92.

Muhammad

Rana

G-E

(2020) Mediating role of customer forgiveness between perceived justice and satisfaction. Journal of Retailing and Consumer Services 52: 101886.

93.

Mustafa

Yang

Mortezavi

, et al. (2020) Patient Satisfaction with telemedicine encounters in an allergy/immunology practice during the COVID-19 pandemic. Annals of Allergy, Asthma & Immunology 125(4): 478–479.

94.

Namkung

Jang

(2010) Effects of perceived service fairness on emotions, and behavioral intentions in restaurants. European Journal of Marketing 44(9/10): 1233–1259.

95.

Nguefack

EGA

Essomba

Ndiparah

, et al. (2020) Ethics in Telemedicine and Telehealth: a Literature. Research Journal of Advanced Engineering and Science 5(4): 67–70.

96.

Ogonu

Hamilton-Ibama

EOL

(2020) Interpersonal justice and customer behaviour of beverage manufacturing firms in port Harcourt, Nigeria. Nigerian Journal of Management Sciences 21(1&2).

97.

Okereafor

Adebola

Djehaiche

(2020) Exploring the potentials of telemedicine and other non-contact electronic health technologies in controlling the spread of the novel coronavirus disease (COVID-19). International Journal in IT and Engineering (IJITE) 8(4): 1–13.

98.

Orsingher

Valentini

De Angelis

(2010) A meta-analysis of satisfaction with complaint handling in services. Journal of the Academy of Marketing Science 38: 169–186.

99.

Ortiz

Chiu

T-S

Wen-Hai

, et al. (2017) Perceived justice, emotions, and behavioral intentions in the Taiwanese food and beverage industry. International Journal of Conflict Management 28(4): 437–463.

100.

Pandey

Deshwal

(2018) The influence of medical course experience on satisfaction, loyalty, and word-of-mouth in Indian medical colleges. Procedia Computer Science 132: 84–91.

101.

Pang

PCI

Liu

. (2020), Why do consumers review doctors online? Topic modeling analysis of positive and negative reviews on an online health community in China. In: Proceedings of the 53rd Hawaii International Conference on System Sciences, Mau, HI, 7–10 January 2020

102.

Pérez-Arechaederra

(2019) The Psychology of Perceived Justice in Shared Health Care Decision Making’, Social Psychology and Justice. 1st edition. Abingdon, UK: Routledge.‘

103.

Pinar

Anract

Perrot

, et al. (2020) Preliminary assessment of patient and physician satisfaction with the use of teleconsultation in urology during the COVID-19 pandemic. World Journal of Urology: 1–6.

104.

Rahi

Khan

Alghizzawi

(2020) ‘Factors influencing the adoption of telemedicine health services during COVID-19 pandemic crisis: an integrative research model’. Enterprise Information Systems 15(6): 769–793.

105.

Ramaswamy

Drangsholt

, et al. (2020) Patient satisfaction with telemedicine during the COVID-19 pandemic: retrospective cohort study. Journal of medical Internet research 22(9): e20786.

106.

Robbins

Ford

Tetrick

(2012) Perceived unfairness and employee health: a meta-analytic integration. The Journal of Applied Psychology 97(2): 235–72.

107.

Sabadie

. (2000) Les Apports Des Théories De La Justice Au Marketing Des Services. Montréal, Canada: Actes du Congrès International de L'Association Française du Marketing, 133–147.

108.

Sabadie

Prim-Allaz

Llosa

(2006) Contribution des éléments de gestion des réclamations à la satisfaction : les apports de la théorie de la justice. Recherche et Applications en Marketing 21(3): 47–64.

109.

Schwappach

(2010) Review: engaging patients as vigilant partners in safety: a systematic review. Medical Care Research and Review : MCRR 67(2): 119–48.

110.

Setyawati

(2019) The influence of perceived justice in service recovery on complainant satisfaction and word-of-mouth intention at hotel In Jakarta. Jurnal Ilmiah Ekonomi Bisnis 23(3): 221–238.

111.

Shenoy

Ahmed

Paul

, et al. (2020) Switching to teleconsultation for rheumatology in the wake of the COVID-19 pandemic: feasibility and patient response in India. Clinical Rheumatology 39: 2757–2762.

112.

Shiferaw

Mengiste

Gullslett

, et al. (2021) Healthcare providers’ acceptance of telemedicine and preference of modalities during COVID-19 pandemics in a low-resource setting: an extended UTAUT model. Plos One 16(4): e0250220.

113.

Silva

Caetano

(2016) Organizational justice across cultures: a systematic review of four decades of research and some directions for the future. Social Justice Research 29: 257–287.

114.

Smith

Bolton

Wagner

(1999) A model of customer satisfaction with service encounters involving failure and recovery. Journal of Marketing Research 36: 356–372.

115.

Söderlund

Colliander

(2015) Loyalty program rewards and their impact on perceived justice, customer satisfaction, and repatronize intentions. Journal of Retailing and Consumer Services 25: 47–57.

116.

Song

Guo

, et al. (2022) Making mobile health information advice persuasive: an elaboration likelihood model perspective. Journal of Organizational and End User Computing 34(4): 1–22.

117.

Steiner

(2017) Organizational justice for understanding employee health and well-being. Organizational Justice: 160–180.

118.

Tax

Brown

Chandrashekaran

(1998) Customer evaluations of service complaint experiences: implications for relationship marketing. Journal of Marketing 62(2): 60–76.

119.

Tenforde

Borgstrom

Polich

, et al. (2020) Outpatient physical, occupational, and speech therapy synchronous telemedicine: a survey study of patient satisfaction with virtual visits during the COVID-19 pandemic. American Journal of Physical Medicine & Rehabilitation 99(11): 977–981.

120.

Teo

Srivastava

Jiang

(2008) Trust and electronic government success: an empirical study. Journal of Management Information Systems 25(No3): 99–132.

121.

Tsai

J-M

Cheng

M-J

Tsai

H-H

, et al. (2019) Acceptance and resistance of telehealth: the perspective of dual-factor concepts in technology adoption. International Journal of Information Management 49: 34–44.

122.

Vaamonde

Omar

Salessi

(2018) From organizational justice perceptions to turnover intentions: the mediating effects of burnout and job satisfaction. Europe’s Journal of Psychology 14(3): 554–570.

123.

Van Den Heuvel

Groenhof

Veerbeek

, et al. (2018) eHealth as the next-generation perinatal care: an overview of the literature. Journal of Medical Internet Research 20(6): e202.

124.

Van Knippenbert

De Cremer

Wisse

(2007) Leadership and fairness: the state of the art. European Journal of Work and Organizational Psychology 16(2): 113–140.

125.

Van Vaerenbergh

Orsingher

(2016) Service recovery: an integrative framework and research agenda. The Academy of Management Perspectives 30(3): 328–346.

126.

Vanhamme

(2002) La satisfaction des consommateurs spécifique à une transaction : définition, antécédents, mesures et modes. Recherches et Applications en Marketing 17(2): 55–85.

127.

Varkevisser

Van Der Geest

Schut

(2012) Do patients choose hospitals with high quality ratings? Empirical evidence from the market for angioplasty in the Netherlands. Journal of Health Economics 31(2): 371–8.

128.

Venkatesh

Thong

(2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly 36(1): 157–178.

129.

Voorhees

Brady

(2005) A service perspective on the drivers of complaint intentions. Journal of Service Research 8(2): 192–204.

130.

Waldron

(1988) Gender and health-related behavior. In: Gochman (ed). Health Behaviour: Emerging Research Perspectives. Boston, MA: Springer, 193–208.

131.

Winters

Venkatapuram

Geniets

, et al. (2020) Prioritarian principles for digital health in low resource settings. Journal of Medical Ethics 46(4): 259–264.

132.

Xing

Hsu

Chang

, et al. (2019) How does online doctor–patient interaction affect online consultation and offline medical treatment? Industrial Management & Data Systems 120(1): 196–214.

133.

Yasri

Engriani

(2018) An analysis on brand image and switching intention of flight companies’ customer with word-of-mouth (WOM) as the intervening variable. Advances in Economics, Business and Management Research 57: 347–358.

134.

Zeng

Chen

, et al. (2009) Determinants of online service satisfaction and their impacts on behavioral intentions. Total Quality Management & Business Excellence 20(9): 953–969.

135.

Zheng

Chang

Chen

, et al. (2022) An investigation of patient decisions to use eHealth: a view of multichannel services. Journal of Organizational and End User Computing 34(4): 1–24.