The pursuit of beauty is as old as mankind – throughout history, people have tried to improve their attractiveness and enhance their beauty. Aesthetic medicine, often considered as ancient as medicine itself (Krueger et al., 2013), traces its roots back to early civilizations. The ancient Egyptians, for instance, used oils, salt, alabaster, and milk to aesthetically improve the skin of their body and face (Brody et al., 2000). Nowadays, with rapid technological advancements and ongoing developments in medical science, aesthetic medicine offers patients/clients the possibility of seemingly eternal youth and an ideal figure (Napiwodzka-Bulek, 2017). These modern methods provide women as well as men the opportunity to maintain a young look and to age in the best possible way, while also significantly enhancing patients’ quality of life (Welsch, 2005; Asscher, 2014).

The aesthetic medicine market has emerged as a significant segment of the self-financed medical industry (Lin & Yen, 2020). The global aesthetic medicine market size was valued at USD 82.46 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 8.3% from 2024 to 2030 (Grand View Research, 2024). Medical tourism, an influential factor in this growth, exhibited a compound annual growth rate of 15% by 2018 (Chistobaev & Semenova, 2018). In Europe, Spain is notably prominent in aesthetic medicine, with regulations permitting cosmetic procedures for patients as young as 14 years old (Pustułka & Jędrzejczak, 2018).

In Poland, the value of the aesthetic medicine market was estimated at 112 milions USD in 2022 (Posełek, 2024). The rates of individuals interested in improving their external appearance have steadily increased over time. Notably, male patients are becoming more frequent visitors to aesthetic medicine clinics. Men are increasingly self-aware, with their external appearance influencing their sense of attractiveness, self-confidence, and masculinity (Załuski, 2023).

The development of the aesthetic medicine market is shaped by several current trends, including:

• personalized treatments,
• cell-based therapies,
• increased integration of artificial intelligence (AI),
• the use of augmented reality (AR) and virtual reality (VR) technologies, and
• advances in non-surgical technologies.

The distinctive characteristics of the aesthetic medicine market – such as comparatively high costs, lack of public funding, individualized services and outcomes, and subjective assessments of appearance – play a significant role in determining patient satisfaction. Importantly, satisfaction in aesthetic medicine differs significantly from satisfaction with other medical treatments. This distinction underscores the need for further investigation into consumer behavior within this market (Ankiel et al., 2021). A number of factors contribute to the limited understanding of this field, including the following:

• Aesthetic medicine is a relatively new area of medical study, having been the subject of limited research from economic, sociological, and psychological perspectives.
• Services in aesthetic medicine often remain an intimate topic; minimal downtime allows patients to recover privately, which may lead to reluctance in participating in studies and consequently smaller sample sizes (Liao et al., 2019).

The primary objective of the present study was to identify and evaluate the determinants of satisfaction among patients/clients with aesthetic medicine treatments that are based on innovative technologies. A secondary aim was to analyze commonly performed procedures (from the perspectives of both doctors and patients) and assess patient satisfaction levels.

2. Theoretical background

Aesthetic medicine employs minimally invasive methods to prevent skin aging and reduce fat tissue. It is often referred to as “wish-fulfilling medicine,” as its procedures are directed by patients’ desires, seeking to improve their quality of life and mental well-being. One way to define aesthetic medicine is medical interventions to prevent and manage skin aging, thereby enhancing patients’ appearance and subjective well-being (Boon & Tan, 2007; Ankiel et al., 2021). As such, unlike traditional medicine, aesthetic medicine does not seek to treat diseases. Patients seeking such treatments are typically healthy images who wish to enhance their physical image (Rymkiewicz, 2018).

Aesthetic medicine often works in tandem with cosmetology, which focuses on contemporary and evolving treatments. These treatments are tailored to meet consumer demands for preventative care against skin aging (Pfenninger & Fowler, 2011). The integration of medical and cosmetological techniques ensures a comprehensive approach to patient needs.

Table 1 outlines a classification of aesthetic medicine treatments.

The number of aesthetic medicine clinics is increasingly significantly each year, driven by growing consumer interest. Non-surgical treatments, including botulinum toxin A (BoNT-A), hyaluronic acid, other dermal fillers, and laser treatments, have been the fastest-growing procedures in aesthetic medicine over the past five years (Vlahos & Bove, 2015). For instance, the use of botulinum toxin A injections has surged remarkably since their first use in this setting in the mid-1980s (Dover et al., 2018; Carruthers & Carruthers, 1998). Current aesthetic uses of BoNT-A include treating glabellar lines, forehead wrinkles, periorbital and perioral lines, platysmal bands, horizontal necklines, and the masseter muscle, among many other applications (Blitzer et al., 1993; Dorizas et al., 2014). Aesthetic medicine is a rapidly evolving field, propelled by innovative technologies and advancements in treatment materials (Przylipiak, 2017). Modern aesthetic medicine employs a wide range of lasers, energy-based devices, and natural or synthetic fillers (Redaelli & Ignaciuk, 2000). Technological progress has significantly expanded the possibilities for aesthetic treatments, transforming the delivery of skincare within clinical dermatology. In particular, the harmful effects of UV irradiation have spurred the development of therapies aimed at reversing photodamage. Among these, laser technologies have become a cornerstone of skin rejuvenation treatments (Theodorou et al., 2021).

Table 2 presents the growth in innovative aesthetic medicine services since 1965. Research papers since 2018 have highlighted the introduction of the newest technology, High-Intensity Focused Electromagnetic Field (HIFEM) used in body contouring and urinary incontinence treatments, and address the safety and effectiveness of this technology (Jacob & Paskowa, 2018; Samuels, 2018; Samuels et al., 2019). A variety of technologies and less invasive methods to treat a wide range of beauty defects and difficulties in daily life are constantly being improved. The advancement of medical technology, the provision of professional services, and the functional training of aesthetic medical practitioners all significantly contribute to the profitability of enterprises in this field (Skountridaki, 2017).

Consumer satisfaction in aesthetic medicine is understood as a psychological state in which the perceived characteristics of the product or services match consumer expectations (Hunt, 1998). In this context, patient satisfaction depends on the post-treatment effects. Many objective and sub-objective factors influence patient satisfaction, such as treatment availability, the ongoing care process, health improvements health, and service price (Małecka & Marcinkowski, 2007; Ankiel & Kuczyńska, 2018). One of the most important determinants of patient satisfaction is the experience and expertise of the medical doctor who is carrying the treatments, who should provide patients with positive emotions and a sense of safety. Patient trust, doctors’ empathy, attentive listening, and professionalism are critical factors in aesthetic medicine. Moreover, effective communication between doctor and patient provides an opportunity to thoroughly understand the patients’ concerns and fosters a sense of trust in the doctor (Bukowska-Piestrzyńska, 2017).

Patient satisfaction is a key metric for evaluating a healthcare organization’s success. In the United States, physician bonuses are linked to patients’ evaluation of their interaction with doctors (Funk et al., 2012). A variety of tools and measures have been developed to estimate patients satisfaction and evaluate specific outcomes, which are important to receiving aesthetic medicine services (Cohen & Scuderi, 2017). For instance, Likert-type scales may be used to rate patients’ satisfaction, such as the Facial Lines Treatment Satisfaction Questionnaire (Cox et al., 2003). Another instrument is the Facial Line Outcome questionnaire, which evaluates specific outcomes such as perception of attractiveness, self-perception of aging, and extended facial lies, which may project expressions such as stress, anger, or simply looking tired which the patient does not actually feel (Dayan et al., 2019). For a more objective and reproducible assessment of aesthetic procedures and patient satisfaction, the Aesthetic Numeric Analog Scale has been introduced. This scale builds on established tools such as the Wong-Baker FACES Pain Rating Scale and the 11-Point-Box-Scale to create a standardized approach (Funk et al., 2012).

The Net Promoter Score (NPS) is a versatile tool employed across various service industries, including healthcare, to gauge how likely a patient or other consumer is to recommend a service. In this framework, consumers are categorized as Promoters (those who actively recommend the services), Passives (those who are satisfied, but do not actively recommend the services); and Detractors (consumers who actively discourage others from using the services) (Reichheld, 2003). NPS is increasingly being used to measure patient satisfaction , serving as a simple yet effective indicator of overll cosumer experience.

One notable application of NPS is the Family and Friends Test, widely used by the National Health Services (NHS) of the United Kingdom to measure overall patient satisfaction and likelihood of recommendation (Stirling et al., 2013; Wilberforce et al., 2019). The straightforward and unambiguous nature of the NPS methodology has contributed to its growing popularity in consumer satisfaction studies, often outperforming alternative models like SERVQUAL or SERVPERF in terms of usability and ease of interpretation (Parasuraman et al., 1985; Cronin & Taylor, 1992).

However, while NPS and similar tools have been extensively studied and applied in fields like plastic surgery, dentistry, and orthodontics (De Vries et al., 2014; Sharp et al., 2014; Abbas & Karadavut, 2017), aesthetic medicine remains underexplored in the context of consumer satisfaction. Existing research on consumer behavior in healthcare highlights significant gaps in understanding the determinants of satisfaction among patients undergoing aesthetic medicine treatments and the relationship between satisfaction and loyalty of consumers/patients.

The present study, therefore, aims to identify the satisfaction indicators of the clients using innovative aesthetic medicine services based on innovative technologies. Evaluating client satisfaction in this context will enable a more precise identification of patient expectations, allowing practitioners to better align their offerings with client needs.

3. Materials and methods

The research process was structured in two stages, combining both quantitative and qualitative methods to ensure a comprehensive analysis of satisfaction indicators among clients and practitioners of aesthetic medicine services.

Stage 1: quantitative survey
The first stage involved a quantitative survey conducted through direct interviews at aesthetic medicine clinics located in selected large cities in Poland (with more than 500,000 inhabitants) – Poznań, Kraków, Warsaw, Łódź and Gdynia – in the period from May to June 2019. As a research tool, we used a direct interview questionnaire prepared and verified in piloting. The research population included consumers declaring regular use of aesthetic medicine services. The research sample included 745 respondents; the selection of respondents was based on purposive sampling (Tongco, 2007). The reliability of the construction of the questionnaire was verified using the Alpha-Cronbach test (the coefficient was 0.81, indicating good reliability).

Two crucial aspects of the research are worth emphasizing (Ankiel et al., 2021):

• the distinctive research site: large aesthetic medicine clinics in the above-mentioned cities (each with more than 500 patients in their client bases),
• the significant size of the research sample composed of patients of these clinics (n=745).

A review of the existing literature shows that there have been relatively few comparable research attempts in aesthetic medicine, mainly due to the difficulties of obtaining consent for participation of the patients in a direct questionnaire. These challenges arise from the very personal nature of the procedures and the sensitive reasons for their implementation (Angelini & Carmignani, 2017; Betancur et al., 2014).

Stage 2: qualitative survey

The second stage involved a qualitative survey conducted among aesthetic medicine doctors. This phrase aimed to identify the key determinants influencing the development of treatment offerings in aesthetic medicine. The survey was carried out in August 2019, among physicians attending the XXI 5 Continent – Congress Barcelona, and in September 2019, among attendees of the XX International Congress of the Polish Society of Aesthetic Medicine and Anti-Aging in Warsaw. The research sample included 20 respondents – doctors from Poland, the United States, and Spain. Individual in-depth interviews (IDIs) were conducted using a pre-tested direct interview questionnaire to explore the perspectives of medical professionals.

The satisfaction survey of patients using aesthetic medicine services in Poland was carried out using the Net Promoter Score technique.

4. Results and discussion

4.1. Identification the most popular innovative aesthetic medicine treatments

Continuous technological advances and the evolution of aesthetic medicine techniques have significantly expanded the options available to patients, enabling the use of non-invasive or minimally invasive techniques to improve the external appearance and address signs of skin and body aging. The primary goals driving modern aesthetic medicine are to reduce the invasiveness of treatments and shorten the recovery time while maintaining the best possible results.

Striving to meet evolving trends in consumer expectations, manufactures of Energy Based-Devices (EBDs) are continually introducing innovative technologies. Among the latest technologies to be introduced into the aesthetic medicine market is the High-Intensity Focused Electromagnetic Field (HIFEM). In Poland, distribution of HIFEM devices began in 2019. HIFEM is used for body contouring treatments and treating urinary incontinence (Jacob & Paskowa, 2018). Owners of aesthetic medicine clinics and medical doctors can choose among various medical devices and fillers, and other injection materials. However, the wide array of available equipment and materials poses challenges in creating the most effective and appealing service offerings. The selection process requires careful consideration of consumer preferences, market trends, and the specific benefits of each technology. The most popular aesthetic medicine services in medical doctors’ opinions are shown in Table 3.

The most popular aesthetic medicine services selected by medical doctors is hyaluronic acid treatment. Hyaluronic acid is commonly used to fill the wrinkles, giving the client/patient a younger and smoother skin surface. It is also used to enhance the volume of the cheeks and to augment the lips. Its popularity is largely due to the low percentage of post-treatment complications. Other fillers, such as autologous fat tissue or collagen, cause post-treatment complications more often (Carruthers & Carruthers, 2011).

The second most popular aesthetic medicine treatment indicated by medical doctors is botulinum toxin type A injections – one of the most widely used treatments for reducing signs of skin aging. BoNT-A works by inhibiting the secretion of acetylcholine, leading to the relaxation of the facial muscles and making the face appear smoother and younger (Baumann et al., 2016). Additionally, neurologists use it to treat migraine headaches.

The third most popular aesthetic treatment services is laser hair removal (60% of indications by the medical doctors). Laser hair removal is based on selective thermolysis, which targets melanin in the hair to produce a localized thermal effect, damaging the hair follicle and stopping hair growth (Lanigan, 2000). This treatment provides satisfactory post-treatment outcomes.

Next most frequently, medical doctors selected PDO thread treatments (25% indications). PDO threads are applied under the face skin, providing a revitalizing and lifting effect (Przylipiak, 2017). The least popular aesthetic medicine treatment, as indicated by the physicians, is laser telangiectasia removal (with only 10% indications). However, despite its lower popularity, this treatment remains the only effective option for addressing flushing – an involuntary or temporary reddening of the face, neck, and décolletage caused by factors such as temperature changes, physical activity, or consuming hot and spicy foods – and telangiectasia, which involves the chronic dilation of small blood vessels, resulting in visible dark red or light red blotches on the skin.

The popularity of innovative aesthetic medicine services as ranked by the patients surveyed, on the other hand, is shown in Table 4.

Based on patient responses, botulinum toxin type A injections (75% indications) are the most popular aesthetic medicine treatment. This is followed closely by procedures based on hyaluronic acid injections (67% indications). The third most popular selection is laser hair removal (37% indications), with lip augmentation ranking next (34% indications). While this latter service is typically performed by using hyaluronic acid, doctors might also apply collagen or silicon.

The next innovative treatment in the patients’ ranking is microneedle mesotherapy (25% indications). This treatment has gained its popularity due to its versatility in treating various conditions, such as alopecia or selected skin inflammations (Jager et al., 2011). Ranking next are laser telangiectasia removal (20% indications) and fractional ablative laser eCO2 (18% indications). The fractional ablative laser is particularly notable for its effectiveness in scar removal and skin resurfacing. This treatment works by damaging targeted areas of the skin to stimulate the repair process, which shortens collagen fibers and results in smoother, more delicate skin (Alexiades-Armenakas et al., 2008). Despite being one of the most invasive treatments in aesthetic medicine, it remains a highly rated and sought-after service.

The next most popular aesthetic medicine treatments are High Intensity Focused Ultrasounds and Dermapen® treatment and face volumetry (both with 17% indications). Contemporary High Intensity Focused Ultrasounds (HIFU) is one of the most effective methods of non-invasive skin lifting. This treatment’s main aim is to provide to focused thermal damage in deep layers of the skin or fat tissue (Wasiluk, 2007). Dermapen®, in turn, is a treatment that uses superficial skin needling to start skin self-recovery (Hou et al., 2017). Volumetry treatment gives increasing volume to tissues in the checks, jawline, etc. (Baumann et al., 2016). Other treatments were indicated by less than 15% of respondents and were therefore not included in this analysis.

In summary, aesthetic medicine treatments incorporating innovative techniques such as laser therapy and HIFU are very popular due to their fast post-treatment effects and minimal recovery time. The findings of this part of the present study may be invaluable for medical doctors and the owners of aesthetic medicine clinics in shaping their service offerings to better align with patient preferences and expectations.

4.2. Aesthetic medicine services – consumer satisfaction

This study study aimed to evaluate the satisfaction of patients receiving aesthetic medicine treatments based on innovative technologies, performed at aesthetic medicine clinics in large Polish cities. Figure 1 shows the extent to which post-treatment effects complied with patient expectations.

Most of the respondents making use of aesthetic medicine services in large cities in Poland positively assessed the adequacy of those services in meeting their expectations (59% reported that the service was as expected, 16% that it was above their expectations, and 18% that it exceeded significantly beyond their expectations) (Fig. 1). Respondents highlighted such aspects as the clinic’s prestige, its range of facilities (devices and fillers), positive energy, and the quality of the information provided by the clinic. Addressing any post-treatment complications was also deemed crucial, as it enhances patients’ sense of safety (Ankiel et al., 2021). About 6% of respondents indicated that the post-treatment effects were below their expectations, while only 1% indicated that they fell significantly below their expectations.

In this context, it is important to stress that the main contraindication for all aesthetic medicine services is having unreal expectations. Sometimes, the patient’s expectations may be simply impossible to meet. Moreover, in some cases, the patient’s state of health may preclude certain medical procedure from being carried out.

In summary, a majority of respondents were found to be pleased with the outcomes of aesthetic medicine services. Treatments that align with or exceed patient expectations positively influence satisfaction, enhance quality of life, and contribute to better overall well-being. Hence, addressing both procedural outcomes and the holistic patient experience is key to maintaining high levels of satisfaction in aesthetic medicine services.

The main objective of this study was to identify the level of patient satisfaction with aesthetic medicine services rendered in Poland. Additionally, the study assessed the role of clinics as significant determinants in the development of the aesthetic medicine market. It is noteworthy that this study is one of the few large-scale surveys conducted in Poland targeting a broad group of aesthetic medicine clients through direct questionnaire interviews, carried out shortly before the COVID-19 pandemic.

As mentioned earlier, the Net Promoter Score (NPS) methodology was employed for this assessment. In accordance with the NPS procedure, clients of aesthetic medicine clinics were first asked about the likelihood of their recommending the treatments to their friends. Subsequently, respondents were asked about their likelihood of recommending the specific clinic to potential clients. Recommendations were rated on a 0–10 scale, reflecting the level of likelihood. The results for the first issue, analyzed across the general population and various age groups, are presented in Table 5. Gender analysis was excluded due to the relatively small representation of male respondents (12% of the total).

The results in Table 5 show a clear dominance of the Promoters group across all age categories, with values exceeding 70% in every subgroup. This indicates a high likelihood of respondents recommending aesthetic medicine treatments (scored 9 or 10). Conversely, the proportion of Detractors is remarkably low, never exceeding 7%, except for the 51–60 age group, where it slightly increased to 11.8%.

The overall NPS score for the entire population reached 71.4%, reflecting a very positive evaluation and high satisfaction with aesthetic medicine services in Poland. The NPS score was even higher among respondents aged 41–50, exceeding 75%. These findings suggest a consistently high level of consumer satisfaction with aesthetic medicine services, regardless of the patients’ age. Additionally, the results indicate a promising outlook for the growth of aesthetic medicine services in Poland in the coming years.

The study also explored the likelihood of respondents recommending the specific aesthetic medicine clinics they had utilized. These results, presented in Table 6, closely align with the general recommendations for aesthetic medicine treatments. The overall NPS score for clinics was 77.1%, reflecting a high probability of recommendation. This value remained consistently high across all age groups, with a slightly lower score observed in the 51+ age group.

These findings reinforce the conclusion that both treatments and specific clinics offering aesthetic medicine services in Poland are highly regarded by consumers, underscoring the importance of maintaining high service quality and customer satisfaction to drive market growth.

5. Conclusions

The theoretical and empirical literature offers numerous studies on patient satisfaction with medical services, but they mainly focus on aspects related to the patient’s attitude, nurses, emergency room service, or general healthcare staff. Many studies specifically explore plastic surgery services and factors determining patient satisfaction with this type of services, such as clear explanations of problems/conditions, addressing patient questions/worries, involvement in decision-making, information about medications, follow-up care instructions, the use of clear language, the time clinicians spend with the patient, etc. (Clapham et al., 2010; Chen et al., 2018). Aesthetic medicine, being a much younger field of study compared to plastic surgery, has had relatively few studies assessing patient satisfaction.

In Poland, our own study was one of the first to examine patient satisfaction with services offered by aesthetic medicine clinics (Ankiel & Kuczyńska, 2018). However, given the very similar nature of the outcomes achieved by aesthetic medicine and plastic surgery procedures – such as appearance enhancement and the correction of natural defects – based on our findings, it can be assumed that the sets of determinants of patient satisfaction are largely similar across both fields. On the other hand, satisfaction surveys for services provided in offices and clinics must take into account specific factors such as location, decor, equipment quality, professionalism of medical staff, and the level of patient care and service.

More broadly, the present findings support the conclusion that the aesthetic medicine market in Poland is poised for strong growth, both in terms of the number of procedures performed and the overall value of the services provided. The high level of patient recommendations reflects the exceptional quality of aesthetic medicine services available in Poland: the satisfaction levels reported by respondents differ significantly from those observed in other medical services in the country. This is particularly notable given that the overall evaluation of healthcare services in Poland is often considered relatively low compared to other European Union countries.

The present study has a number of practical implications. First of all, it shows that a professional approach to patient care and the delivery of high-quality treatments and accompanying processes are essential for achieving patient satisfaction. In Poland, aesthetic medicine is characterized by high satisfaction ratings, whereas other types of medical services are generally evaluated as being of low satisfaction and poor quality. We believe that this derives from the fact that these services are predominantly provided by private offices and aesthetic medicine clinics. From the marketing point of view, therefore, it is important to build positive relationships with patients before, during, and after each procedure, which results in very positive WOM (word of mouth), necessary for increasing the demand for this type of service. Moreover, in the private sector, implementing innovative technologies as part of various types of treatments is typically a short and simple process (usually with a single decision-maker). Offering cutting-edge treatments is another dimension important for building a positive image and reputation for an office or clinic.

Despite its potential for growth, however, the aesthetic medicine market in Poland faces some limitations. One significant challenge is the affordability of services; many innovative treatments represent a substantial financial burden for patients, limiting access. Moreover, intense market competition could lead to reduced prices for aesthetic medicine services in the future.

The market’s development will also be influenced by factors such as increasing household wealth and advancements in medical technology. However, the lack of public education on aesthetic medicine, particularly in rural areas, and the absence of reimbursement for such services from the state healthcare system are additional barriers.

Further research by the authors of the present study will focus on examining the determinants of consumer satisfaction in aesthetic medicine services in international markets, with a particular emphasis on Western Europe, Turkey, the United States, and Brazil.

References

Abbas, O. L., & Karadavut, U. (2017). Analysis of the Factors Affecting Men’s Attitudes Toward Cosmetic Surgery: Body Image, Media Exposure, Social Network Use, Masculine Gender Role Stress and Religious Attitudes, Aesthetic Plastic Surgery, 41(6), 1454–1462.

Alexiades-Armenakas, M. R., Dover, J. S., & Arndt, K. A. (2008). The spectrum of laser skin resurfacing: Nonablative, fractional, and ablative laser resurfacing, Journal of the American Academy of Dermatology, 58(5), 719–737.

Angelini, A., & Carmignani, G.G. (2017). The consumer experience of aesthetic medicine services. In 20th Excellence in Services International Conference, Conference Proceedings (pp. 11–22), University of Verona.

Ankiel, M. & Kuczyńska, A. (2017). Wyznaczniki satysfakcji klientów korzystających z usług medycyny estetycznej, Studia Ekonomiczne. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach, 330, 7–15.

Ankiel, M., Sojkin B., & Gogołek A. (2021). Determinants of purchasing decisions of innovative aesthetic medicine services in Poland, International Journal of Innovation and Learning, 29(3), 373–386.

Asscher, E.C. (2014). Wish-fulfilling medicine in practice: opinions and arguments of lay people, Journal of Ethics, 40(12), 837–841.

Baumann, L., Dayan, S., Connolly, S., Silverberg, N., Lei, X., Drinkwater, A., & Gallagher, C. J. (2016). Duration of Clinical Efficacy of OnabotulinumtoxinA in Crow’s Feet Lines, Dermatologic Surgery, 42(5), 598–607.

Betancur, D. M., Castaneda, K.M., & Tavera-Mesias, J. (2014). Correlational study of the factors that influence in the recommendation and loyalty of patients of aesthetic medicine Medellín Colombia, Cuadernos de Administracioìn Journal of Management, 33(58), 3–17.

Blitzer, A., Brin, M. F., Keen, M. S., & Aviv, J. E. (1993). Botulinum Toxin for the Treatment of Hyperfunctional Lines of the Face, Archives of Otolaryngology – Head and Neck Surgery, 119(9), 1018–1022.

Boon, K., & Tan, H. (2007). Aesthetic medicine: a health regulator’s perspective, Clinical Governance: An International Journal, 12(1), 13–25.

Brody, H.J., Monheit, G.D., Resnik, S.S., & Alt, T.H. (2000). A history of chemical peeling, Dermatologic Surgery, 26(5), 405–409.

Bukowska-Piestrzyńska, A. (2017). Quality of Medical Services – Based on the Example of Dental Services, Problemy Jakości, 6, 8-16.

Carruthers, J., & Carruthers, A. (1999). Practical Cosmetic Botox Techniques. Journal of Cutaneous Medicine and Surgery, 3(4_suppl), S4–49–S4–52.

Chen, K., Congiusta, S., Nash, I. S., Coppa, G. F., Smith, M. L., Kasabian, A. K., Tanna, N. (2018). Factors Influencing Patient Satisfaction in Plastic Surgery, Plastic and Reconstructive Surgery, 142(3), 820–825.

Chistobaev, A. I., & Semenova, Z. A. (2018). Spatio-Temporal Dynamics of the Global Medical Tourism, Journal of Environmental Management & Tourism, 9, 267–75.

Clapham, P. J., Pushman, A. G., & Chung, K. C. (2010). A Systematic Review of Applying Patient Satisfaction Outcomes in Plastic Surgery, Plastic and Reconstructive Surgery, 125(6), 1826–1833

Cohen, J. L., & Scuderi, N. (2017). Safety and Patient Satisfaction of AbobotulinumtoxinA for Aesthetic Use: A Systematic Review, Aesthetic Surgery Journal, 37(suppl_1), 32–44.

Cox, S.E., Finn, J.C., Stetler, L., Mackowiak, J., & Kowalski, J.W. (2003). Development of the Facial Lines Treatment Satisfaction Questionnaire and initial results for botulinum toxin type A-treated patients, Dermatologic Surgery, 29(5), 444–459.

Cronin, J. J., & Taylor, S. A. (1992). Measuring Service Quality: A Reexamination and Extension, Journal of Marketing, 56(3), 55–68.

Dayan, S., Yoelin, S. G., De Boulle, K., & Garcia, J. K. (2019), The Psychological Impacts of Upper Facial Lines: A Qualitative, Patient-Centered Study, Aesthetic Surgery Journal Open Forum, 1(2).

De Vries, D., Jochen, P., Nikken, P., & de Graaf, H. (2014). The Effect of Social Network Site Use on Appearance Investment and Desire for Cosmetic Surgery Among Adolescent Boys and Girls, Sex Roles: A Journal of Research, 71(9), 283–295

Dorizas, A., Krueger, N., & Sadick, N. S., (2014). Aesthetic Uses of the Botulinum Toxin, Dermatologic Clinics, 32(1), 23–36.

Dover, J. S., Monheit, G., Greener, M., & Pickett, A. (2018). Botulinum Toxin in Aesthetic Medicine, Dermatologic Surgery, 44(2), 249–260.

Funk, W., Podmelle, F., Guiol, C., & Metelmann, H. R. (2012). Aesthetic satisfaction scoring – Introducing an aesthetic numeric analogue scale (ANA-scale), Journal of Cranio-Maxillofacial Surgery, 40(5), 439–442.

Grand View Research (2024). Aesthetic Medicine Market Size, Share & Trends Analysis Report By Procedure Type (Invasive Procedures, Non-invasive Procedures), By Region (North America, Asia Pacific, Europe), And Segment Forecasts, 2024-2030. Grand View Research.
https://www.grandviewresearch.com/industry-analysis/medical-aesthetics-market.

Hou, A., Cohen, B., Haimovic, A., & Elbuluk, N. (2017). Microneedling, Dermatologic Surgery, 43(3), 321–339.

Hunt, H. K. (1991). Consumer Satisfaction, Dissatisfaction, and Complaining Behavior, Journal of Social Issues, 47(1), 107–117.

Jacob C.I., Paskowa K. (2018). Safety and efficacy of a novel high-intensity focused Electromagnetic technology device for non-invasive abdominal contouring, Journal of Cosmetic Dermatology, 17, 783–787.

Jäger, C., Brenner, C., Habicht, J., & Wallich, R. (2011). Bioactive reagents used in mesotherapy for skin rejuvenation in vivo induce diverse physiological processes in human skin fibroblasts in vitro ‒ a pilot study, Experimental Dermatology, 21(1), 72–75.

Krueger N., Luebberding S., Sattler G., Hanke C.W., Alexiades-Armenacas M., Sadick N. (2013). The History of Aesthetic Medicine and Surgery, Journal of Drugs in Dermatology, 7(7), 737–742.

Lanigan, S. W. (2000). Lasers in dermatology. Springer.

Liao, K., Chen, S., & Jhou, Y. (2019). An exploration of factors influencing the potential customers perceived value in aesthetic medicine, Commerce & Management Quarterly, 20(2), 109–130.

Lin, T. T., & Yen, H.-T. (2020). The Criteria of Optimal Training Cost Allocation for Sustainable Value in Aesthetic Medicine Industry, Journal of Risk and Financial Management, 13(7), 149.

Małecka, B., & Marcinkowski, J.T. (2007). Satysfakcja pacjenta czynnikiem kształtującym współczesny rynek usług medycznych, Problemy Higieny Epidemiologicznej, 88(1), 17–19.

Napiwodzka-Bulek, K. (2017). Medycyna estetyczna – humanistyczne dążenie czy „enhancement”? Filozofia Publiczna i Edukacja Demokratyczna, 6(1), 151–166.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A Conceptual Model of Service Quality and Its Implications for Future Research, Journal of Marketing, 49(4), 41–50.

Pfenninger, J. L., & Fowler, G. C. (2011). Pfenninger and Fowler’s procedures for primary care (2nd ed.). Elsevier/Saunders.

Posełek, M. (2024, August 4). Ile jest wart rynek medycyny estetycznej? Fashion Biznes. https://fashionbiznes.pl/wartosc-rynku-medycyny-estetycznej-ile-wynosi/

Prendergast, P.M., & Shiffman, M.A. (2012). Aesthetic medicine: Art and techniques, Springer.

Przylipiak, A. (2017). Medycyna Estetyczna: Podręcznik dla studentów kosmetologii. Wydawnictwo Lekarskie PZWL.

Pustułka, A., & Jędrzejczak, A. (2018, October 14). Medycyna estetyczna to duży biznes. Chcemy być coraz piękniejsi. Retrieved from https://plus.dzienniklodzki.pl/medycyna-estetyczna-to-duzy-biznes-chcemy-byc-coraz-piekniejsi/ar/13601996

Redaelli, A., & Ignaciuk, A. (2000). Medycyna Estetyczna. Wydawnictwo Medycyna Estetyczna.

Reichheld, F.F. (2003). The one number you need to grow, Harvard Business Review (81), 46–55.

Rymkiewicz, D. (2018). Relevance of information and contractual modality of aesthetic medicine: A comparative analysis between Spanish and Polish case-law. Review of European and Comparative Law, 35(4), 103–111.

Samuels J.B. (2018). Nonsurgical Vaginal Rejuvenation. In G. H. Branham, J. S. Dover, H. J. Furnas, M. M. J. Tenenbaum, & A. E. Wulc (Eds.), Advances in Cosmetic Surgery (Vol. 1, pp. 75–84). Elsevier.

Samuels, J. B., Pezzella, A., Berenholz, J., & Alinsod, R. (2019). Safety and Efficacy of a Non-Invasive High-Intensity Focused Electromagnetic Field (HIFEM) Device for Treatment of Urinary Incontinence and Enhancement of Quality of Life, Lasers in Surgery and Medicine, 51(9), 760–766.

Sharp, G., Tiggemann, M., & Mattiske, J. (2014). The role of media and peer influences in Australian womens’ attitudes towards cosmetic surgery, Body Image, 11(4), 482–487

Skountridaki, L. (2017). Barriers to business relations between medical tourism facilitators and medical professionals, Tourism Management, 59, 254–266.

Stirling, P., Jenkins, P.J., Clement, N.D., Duckworth, A.D., & McEachan, J.E. (2019). The Net Promoter Scores with Friends and Family Test after four hand surgery procedures, Journal of Hand Surgery, 44(3), 290–295.

Theodorou, S.J., Chia, Ch.T., & Dayan, E. (2021). Emerging technologies in face and body contouring. Thieme Medical Publishers.

Tongco D. C., (2007). Purposive sampling as tool for informant selection, Ethnobotany Research and Applications, 5, 147–158.

Vlahos, A., & Bove, L. L. (2016). Went in for Botox and left with a rhinoplasty Marketing Intelligence & Planning, 34(7), 927–942.

Wasiluk, M. (2016). Medycyna estetyczna bez tajemnic. Wydawnictwo Lekarskie PZW.

Welsh, W. (2005). Estetyka poza estetyką: o nową postać estetyki. Towarzystwo Autorów i Wydawców Prac Naukowych ‘Universitas’.

Wilberforce, M., Poll, S., Langham, H., Worden, A., & Challis, D. (2018). Measuring the patient experience in community mental health services for older people: A study of the Net Promoter Score using the Friends and Family Test in England, International Journal of Geriatric Psychiatry, 34, 31–37.

Załuski, K. (2023, November 28). Mężczyźni w gabinecie medycyny estetycznej. Co poprawiają i ile to kosztuje? Onet – Jesteś na bieżąco. https://www.onet.pl/styl-zycia/facet-xl/mezczyzni-w-gabinecie-medycyny-estetycznej-co-poprawiaja-i-ile-to-kosztuje/jjm64np,30bc1058

The occurrence of the COVID-19 pandemic in Indonesia prompted the government to enforce physical distancing to reduce the spread of the COVID-19 virus. The implementation of physical distancing has had an impact on several facets of human activity, encompassing the commercial and the non-commercial; for instance, the economy has been affected due to the cessation of several economic activities, and the mandatory social distancing protocols have had the effect of reducing opportunities for face-to-face social interactions (Ilyas, 2021; Mawar et al., 2021). One among these inevitable impacts was that teaching and learning activities could not be carried out by means of face-to-face interaction. The current pandemic condition demands a change in the learning culture, which was previously done conventionally with face-to-face interaction, to a learning activity carried out online (e-learning). e-Learning has been deemed the only way to continue the learning process in Indonesia when experiencing natural disasters or pandemics (Syarifudin, 2020). e-Learning is expected to encourage a more rigorous implementation of social distancing as well as serve as one of the efforts aimed at reducing the spread of the COVID-19 virus (Sadikin & Hamidah, 2020).

The development of information and communication technology (ICT) triggers the development of the online teaching and learning process. The media used in e-learning activities can be in the form of a learning management system (LMS) prepared by educational institutions as online learning media, as well as the use of other media; and the most common media platforms used in Indonesia for e-learning are WhatsApp, Zoom, Google Classroom, Google Meet and email (Pramana et al., 2020). Good preparation is needed in formulating the e-learning process so that the objectives of learning can be achieved and so that the students who pass through the education system under the aegis of the e-learning process become and remain competent in their respective fields of specialisation at the graduation and higher levels, and, at lower levels, achieve the fundamental objectives of the course undergone by them. The quality of elearning implemented during the COVID-19 pandemic is influenced by several factors, namely administrative support, course content, course design, instructor characteristics, learner characteristics, social support and technical support factors (Elumalai et al., 2019). The quality of e-learning administrative and support services, instructor performance and learning course materials have the greatest influence on the overall e-learning service quality (Pham et al., 2018). The e-learning process incorporates several benefits, which can provide flexibility in the learning process and encourage the emergence of independence in learning (Sadikin & Hamidah, 2020). Online learning can help students to obtain learning materials easily and discuss it with experts and lecturers without being limited by constraints of place and time (Hartanto, 2016).

e-Learning also presents several challenges that need to be faced, such as an inadequate internet connection and the lack of a conducive home environment in which the learning process might be carried out, as well as the emergence of a sense of isolation and a decrease in motivation levels due to a lack of face-to-face interaction (Rizvi & Nabi, 2021). Another challenge faced in the e-learning process is the lack of preparation, which encompasses two possible dimensions: an inadequacy in the infrastructural amenities that an educational institution makes available (to students undergoing education through the e-learning mode, as well as to the concerned teaching staff), and a paucity in the preparation of students. Against the backdrop of the fact that the prevalence of conditions such as pandemics and other unusual situations would trigger the adoption of the forced use of e-learning, such lack of preparation would result in the emergence of feelings of being under pressure whilst undergoing the e-learning programme (Abdur Rehman et al., 2021). The other measurable, significant impact caused by the e-learning process is that in terms of student loyalty towards the institution in which they are enrolled. In the absence of a timely resolution being provided, the challenges that students face can create frustration in them, as well as reduce their motivation for learning. Students’ loss of motivation and decrease in their learning performance can also be caused by a lack of support in terms of infrastructure or a lack of social support from lecturers in the e-learning process (Tan, 2021). The challenges that occur in the e-learning process have the potential to exercise a negative impact on students.

A preliminary survey has been conducted on 46 students in Tangerang to observe the phenomenon of the e-learning process being carried out. The survey was conducted by asking six open-ended questions regarding students’ experiences, impressions and obstacles while undergoing e-learning. From the survey conducted, it was found that students experienced both benefits and challenges during e-learning. The benefits are that students can flexibly choose their own time and place in preparing for classes, there are savings in transportation costs and living costs, the digital technology deployed in the e-learning process allows customisation to suit end-user needs, e.g. voice volumes can be adjusted by each participant, and there is a greater accessibility of materials. Universities that have implemented effective e-learning systems have thereby ensured the ease of participants’ learning. One of the main factors that respondents recommend to their universities is the readiness of the e-learning system.

On the other hand, students also experience many challenges and difficulties during e-learning. The common obstacles experienced by these students are problems pertaining to poor internet connection and infrastructural deficits, concentration that is easily divided and disturbed by other things, resulting in the level of interaction and communication getting disrupted, decrease in the level of motivation to study and pay attention to what is going on in the class, boredom, and fatigue in the eyes and body due to staring at the screen for too long; cumulatively, these problems can adversely affect students’ interest in learning. The challenges experienced during the e-learning process also affect the level of interest of students in being active in activities at their university; the survey results reveal that 57% of respondents stated that their level of interest in participating in activities decreased compared to that prevailing prior to commencement of the e-learning process. An inability to carry out direct interaction has been observed to be one of the factors influencing respondents’ absence of interest in participating in university activities. Furthermore, the level of interest in continuing further studies at the university is also affected. The results of the survey reveal that 61% of respondents stated that there was a decline or doubt in the level of interest in continuing their further studies at their university. This is because students have been experiencing boredom with regard to the e-learning process that they had been undergoing for the duration of the COVID-19 pandemic physical distancing restrictions.

This phenomenon has motivated the present research, which focusses on the influence of e-learning service quality factors on student perceived value (SPV) and student satisfaction, and finally on student loyalty. It is important to pay attention to this phenomenon, because if it is ignored, it can have a major impact on the quality of human resources in the future.

Literature Review

e-Learning

The online distance learning process, also known as e-learning, is a process involving learning and teaching activities that are carried out virtually by utilising internet facilities. In the e-learning process, the delivery of learning materials and evaluation of abilities, as well as interactions between lecturers and students, are carried out using ICT (Muhammad et al., 2016). The development of internet technology has facilitated, within the education system, the transition from traditional to modern teaching methods (Elumalai et al., 2019). The deployment of technology in education has provided an opportunity for lecturers and students to utilise the teaching and learning processes, from their respective points of view, in ways that would enable them to reach their full potential. There are several types of e-learning, including: Blended Learning, which is a combination of traditional and online classes, Flipped Classroom, which is a student-centred approach with online learning materials that have been given to students before class, Synchronous Learning, which is a distance learning process with real-time interaction, and Asynchronous Learning, which is a distance learning process with non-real-time interaction (Alqahtani & Rajkhan, 2020).

Relationship between system quality (SYQ) and e-learning service quality (SEQ)

System quality is a measurement of the information system process that focusses on the results of the interaction between the user and the system. The quality of the system can be measured by considering the ease of use (Aditi et al., 2021). System quality determines the characteristics of a system based on several aspects, namely usability, reliability, availability and adaptability, that affect user perceptions of using e-learning systems, and plays an important role in the adoption and use of e-learning systems (Salloum et al., 2019). Previous research has shown that the e-learning system quality is the most important factor influencing the e-learning services quality (Pham et al., 2019). The quality of the system has a crucial role in the use of e-learning systems (Alsabawy et al., 2012; Salloum et al., 2019). Therefore, this study proposes the following hypothesis:

Hypothesis 1: System Quality has a positive and significant influence on e-Learning Service Quality.

Relationship between instructor and course material quality (ICQ) and e-learning service quality (SEQ)

Instructor and Course Material Quality (ICQ) is defined as the quality of the instructor’s role in conveying their knowledge to students and the material used in each learning process (Pham et al., 2018). The characteristics that the instructor would ideally need to focus on include attitude, flexibility, knowledge of learning technology, teaching styles and the ability to motivate students (Alqahtani & Rajkhan, 2020). The learning course material focusses on learning development systems to achieve the goals of educational institutions, which consist of quality content, clarity of objectives, and learning strategies (Alqahtani & Rajkhan, 2020). The quality of an instructor and learning course materials are the factors affecting the quality of e-learning services (Pham et al., 2019). Course content and characteristics of instructors, conjoined as an amalgamated parameter, served as one of the factors that affected the quality of the e-learning that was implemented during the COVID-19 pandemic (Elumalai et al., 2019). The role of instructors is crucial in improving the quality of e-learning to a standard that would enable students to learn effectively during the COVID-19 pandemic (Abbasi et al., 2020). Therefore, this study proposes the following hypothesis:

Hypothesis 2: Instructor and Course Material Quality has a positive and significant influence on e-Learning Service Quality.

Relationship between administrative and support service quality (ASQ) and e-learning service quality (SEQ)

The administrative support factor focusses on supporting infrastructure and learners to enhance their experience. This includes communication assistance, as well as the availability to become a help centre and a training centre (Alqahtani & Rajkhan, 2020). The quality of e-learning support and administration, which exercises an important influence on the degree of attention that students pay to their lessons when undergoing a programme of e-learning, is in turn directly influenced by the level of service provided by university staff, including technical support pertaining to technological issues that may pose a hindrance to the uninterrupted functioning of e-learning sessions (Pham et al., 2018). The quality of support services is assessed through the assistance provided; the assessment is based especially on the support received in relation to online learning. The administrative and support services quality is one of the factors that affect the e-learning services quality (Pham et al., 2019). Administrative support has been one of the factors affecting the quality of the e-learning that was implemented during the COVID-19 pandemic (Elumalai et al., 2019). In determining the quality of e-learning services, what is important is not merely the teaching aspect but also the aspect of administrative services and other services (Arguelles & Busquet, 2016). Therefore, this study proposes the following hypothesis:

Hypothesis 3: Administrative and Support Service Quality has a positive and significant influence on e-Learning Service Quality.

Relationship between e-learning service quality (SEQ) and SPV

The quality of service received by customers is believed to have an influence on the value perceived by customers. Service quality has an impact on the customer’s perception of value; if the quality of the service received exceeds the customer’s expectations, such a phenomenon would effect an increase in the value perceived from the customer’s vantage point (in a generic context referred to as 'perceived value’) (Hapsari et al., 2017). There is a positive and significant relationship between e-service quality and the perceived value of customers who use technology in Indonesia (Aditi et al., 2021). The relationship between eservice quality and perceived value shows that students, as users of online systems, evaluate the degree of worth that they associate with the university based on an assessment of the benefits and disadvantages characterising the system used by them in their interactions with university staff (Kilburn et al., 2016). Therefore, this study proposes the following hypothesis:

Hypothesis 4: e-Learning Service Quality has a positive and significant influence on Student Perceived Value.

Relationship between e-learning service quality (SEQ) and student satisfaction (STS)

The level of customer satisfaction is believed to increase along with the increase in the quality of online-based services (Fida et al., 2020). The satisfaction of students, who are customers in the learning environment, is also influenced by the quality of the e-learning services they receive. The quality of e-learning services provided by universities is believed to have a positive influence on student satisfaction (Annamdevula & Bellamkonda, 2016; Arguelles & Busquet, 2016; Chandra et al., 2018; Doan, 2021; Lunarindiah, 2016; Pham et al., 2019). Therefore, this study proposes the following hypothesis:

Hypothesis 5: e-Learning Service Quality has a positive and significant influence on Student Satisfaction.

Relationship between SPV and student satisfaction (STS)

Perceived value or the customer’s overall assessment of what is received compared to what is given is believed to have a strong influence on customer satisfaction. Several studies mention that the perceived value of customers has a strong influence on the level of satisfaction (Hapsari et al., 2017; Kusumawati & Rahayu, 2020). There is a positive and significant relationship between perceived value and e-satisfaction from customers who use technology in Indonesia (Aditi et al., 2021). The level of SPV also has a positive and significant influence on the level of student satisfaction with regard to the university (Halimatussakdiah et al., 2020; Lunarindiah, 2016). Therefore, this study proposes the following hypothesis:

Hypothesis 6: Student Perceived Value has a positive and significant influence on Student Satisfaction.

Relationship between SPV and student loyalty (STL)

It has been assessed that the perceived value of a customer is a factor that has a significant impact on customer loyalty (Kusumawati & Rahayu, 2020). Perceived value is believed to have a major influence on customer loyalty (Lai et al., 2009). SPV is believed to exercise a positive influence on the concerned student’s degree of loyalty towards their chosen institution of collegiate education (Kilburn et al., 2016), and additionally to influence the intention to continue e-learning activities (Nugroho et al., 2019). Therefore, this study proposes the following hypothesis:

Hypothesis 7: Student Perceived Value has a positive and significant influence on Student Loyalty.

Relationship between student satisfaction (STS) and student loyalty (STL)

The satisfaction received by customers is believed to result in loyal customers, and moreover, when a feeling of satisfaction arises in a customer resultant to experiencing a product or service, this phenomenon associates that particular commodity with an impression of positivity within the customer’s memory (Kusumawati & Rahayu, 2020; Sharma et al., 2020). Previous research stated that a 1% shift in customer satisfaction can result in a 61.7% shift in customer loyalty (Ibojo & Asabi, 2015). Several studies have shown that the level of student satisfaction affects the level of student loyalty (Annamdevula & Bellamkonda, 2016; Arguelles & Busquet, 2016; Chandra et al., 2018; Doan, 2021; Pham et al., 2019). The dissatisfaction of an information system (e-learning) user can cause the user’s unwillingness to continue using the system (Nugroho et al., 2019). Therefore, this study proposes the following hypothesis:

Hypothesis 8: Student Satisfaction has a positive and significant influence on Student Loyalty.

Mediating effects

The quality of service received by the customer/student can affect the perceived value and ultimately can affect the level of satisfaction. Previous research stated that there is a positive and significant relationship between e-service quality and e-satisfaction with the mediation of SPV from customers who use technology in Indonesia (Aditi et al., 2021). e-Service quality will affect the degree of fulfilment of customer’s expectations, and when these are effectively fulfilled, customer satisfaction would be the result (Demir et al., 2020).

The perception of value felt by students in undergoing the e-learning process will affect the level of satisfaction; if a sense of satisfaction is resultantly produced, it will result in students’ loyalty towards the university. Satisfaction has a role as a strong mediator in the relationship between perceived value and continuance intention concerning the use of e-learning (Nugroho et al., 2019). The level of satisfaction of students can mediate between SPV and the intention to use the e-learning system and recommend it to others (Chang, 2013). Therefore, this study proposes the following hypotheses:

Hypothesis 9: e-Learning Service Quality has a positive and significant influence on Student Satisfaction mediated by Student Perceived Value.

Hypothesis 10: Student Perceived Value has a positive and significant influence on Student Loyalty mediated by Student Satisfaction.

The relationships between factors and its mediating effects shown on Figure 1.

Research Methods

The present research was conducted using a quantitative research approach. Data collection was carried out by distributing questionnaires via Google Form to undergraduate students participating in e-learning at six universities in Tangerang. The selected respondents were active students who had previously experienced the offline learning process, and had thereafter participated in e-learning; these were students from batches 2017, 2018 and 2019. The questionnaires were distributed using the social media platforms WhatsApp, Line and Instagram, from April to May 2022. The questionnaires were distributed using several approaches, and in employing these approaches, the power of social media to serve as a tool for preliminary data gathering became apparent, and what also became evident was that social media could exercise a major influence in data collection. The first approach involved sending private chats to known and qualified respondents. In addition, to ensure that other respondents from various universities were also included in the study, a search and screening operation was carried out through the Instagram accounts of several university organisations, which was followed by sending direct messages via Instagram to prospective respondents. Given the immense propagation power of social media, this series of actions can be expected to produce a networking chain that plays a major role in the process of finding respondents and collecting data.

The survey contains a total of 35 questions, where each variable has 5 questions. The survey uses a 5-point Likert scale from 1, which indicates strongly disagree, to 5, which states strongly agree. The questions contain respondents’ perceptions of: (1) the quality of the e-learning website, which consists of the ease of use, visual appearance, delay time on the website and ease of finding information on the website; (2) the quality of the instructors, consisting of their knowledge and their ability to create good and interactive learning environments, as well as whether or not the materials provided are useful and up to date; (3) the quality of administrative and support services, which need to be provided in a way that is fast and relevant to the problem on hand, as well as whether or not the troubleshooting team responsible for attending to these services understands and prioritises user needs upon the experiencing of numerous, simultaneous problems; (4) e-learning service quality, which needs to consist of adequate levels of human resources and infrastructure pertaining to the provision of e-learning services; (5) students’ assessment of the benefits and services obtained against the sacrifices made such as costs, effort and time; (6) satisfaction and pleasant experiences that students obtain; and (7) student loyalty, consisting of overall impressions formed by students about, and the sense of pride felt by them for, their university, as well as the recommendations made by them seeking improvements to be made to their university.

The survey was started by distributing it to 34 respondents for instrument analysis using IBM® SPSS® Statistics 25. The results of the analysis show that all indicators are valid and reliable, and thus it is continued, with the distribution of the survey to the main population to analyse the hypotheses that have been prepared.

The data collected through the questionnaire are analysed with the partial least square structural equation model (PLS-SEM) method using SmartPLS 3.0 by SmartPLS GmbH. The data are analysed in several stages; first, by analysing the outer model through convergent validity testing; thereafter, by measuring Outer Loading and average variance extracted (AVE) through discriminant validity testing; then, by measuring Cross Loading and Fornell-Larcker criterion; and finally, by conducting reliability testing through measurement of composite reliability and Cronbach’s alpha. Then, we analyse the inner model by testing the coefficient of determination (R²), effect size (f²) and structural path coefficients on the proposed hypothesis.

Findings and Results

A total of 309 respondent data elements have been collected from students at six universities in Tangerang. Respondents came from various faculties, and the largest was from the business faculty with a proportion of 30%, followed by the engineering faculty at 24%; and these were followed by the faculties of science, communication, computer, design and law. A total of 76% of respondents came from the 2019 batch, followed by the 2018 batch (as much as 21%), and 2% came from the 2017 batch. Female respondents dominated the characteristics of respondents with a proportion of 61%, compared with male respondents who had a proportion of 39% of all respondents.

In descriptive statistical analysis, it was found that SYQ has an average value of 4.061, with SYQ3 (My university’s e-learning course website provides me with important information.) having the highest average value of 4.262. ICQ has an average value of 4.115, with ICQ5 (The material provided through e-learning is up to date.) having the highest average value of 4.243. ASQ has an average value of 3.906, with ASQ1 (The university staff provide fast service when I have problems pertaining to any of the e-learning sessions or the programme as a whole.) having the highest average value of 3.958. SEQ has an average value of 4.091, with SEQ5 (Overall, the quality of e-learning services provided by my university is good.) having the highest average value of 4.210. SPV has an average value of 3.915, with SPV4 (e-Learning facilitates my needs in terms of knowledge, learning and development.) having the highest average value of 4.104. STS has an average value of 4.073, with STS5 (I am very satisfied doing my studies at my university.) having the highest average value of 4.233. STL has an average value of 4.070, with STL1 (My university gives me a positive impression.) having the highest average value of 4.307.

In the outer model analysis, all indicators in the research model have an Outer Loading value >0.7 and an AVE value >0.5, and thus it can be stated that the indicator has a high level of validity (Hair et al., 2013). Then, all indicators and variables have higher Cross Loading values and AVE square root values for their variables compared to correlations with other variables, and thus it can be stated that all indicators have a good level of discriminant validity (Hair et al., 2013). Then, from reliability testing, it has been ascertained that all variables have composite reliability and Cronbach’s alpha values >0.6, and thus it can be stated that all variables are reliable and consistent for use as research instruments (Ghozali & Latan, 2015).

From the coefficient of determination measurement that can be seen in Table 1, it can be inferred that SYQ, ICQ and ASQ can explain e-learning SEQ by 59.9%, while the remainder is influenced by other variables. Then, SEQ can explain the SPV by 43.6%. SEQ and SPV can explain STS by 66.8%. SPV and STS can explain the STL by 56%. The research model has a moderate classification with an R2 value of 0.5 (Hair et al., 2013).

SEQ, service quality; SPV, student perceived value; STL, student loyalty; STS, student satisfaction.

In the effect size measurement test based on Table 2, an influence can be observed between variables (Hair et al., 2013). ASQ and ICQ have the greatest influence on SEQ, with f² values of 0.224 and 0.223, respectively. Furthermore, SEQ has a large influence on SPV, with an f² value of 0.774. SPV has a greater influence on STS with an f² value of 0.380, in comparison with SEQ having an f2 value of 0.305 on STS. STS has a greater influence on STL with an f² value of 0.371, in comparison with SPV having an f² value of 0.028 on STL.

ASQ, administrative and support service quality; ICQ, Instructor and course material quality; SEQ, service quality; SPV, student perceived value; STL, student loyalty; STS, student satisfaction; SYQ, system quality.

In the path coefficients’ measurement based on Table 3, all path coefficient values are positive, and thus it can be stated that exogenous variables have a positive influence on endogenous variables in each of the proposed relationships. Furthermore, all paths have a t-value >1.96 and a p-value <0.05, indicating that all the relationships between the variables that have been proposed have a statistical significance. The results of the mediation test show that the t-value is >1.96 and the p-value is <0.05, indicating that all the relationships between the variables that have been proposed through the mediating variable have a statistical significance. The direct and indirect relationships between the variables that have been proposed have a statistically significant effect as their results, and thus it can be stated that the mediation model that occurs in these relationships is partial mediation and shows that exogenous variables are able to directly influence endogenous variables through or without mediating variables. The results of the summarised hypothesis analysis can be seen in Table 4.

Structural model

After confirming that our model has satisfactory reliability and validity, we have estimated the parameters of the structural model. The predictive power was estimated with R2 scores and appeared to be high, as its values are 0.304 for e-learning experience and 0.456 for satisfaction (Hair et al., 2012). To assess the significance of path coefficients, the bootstrapping technique with 5,000 subsamples was used (Henseler et al., 2009). As we hypothesised, the value co-creation positively affects e-learning experience (path coefficient = 0.551, t = 17.054, p < 0.0001), supporting H1. Moreover, the e-learning experience is significantly related to satisfaction (path coefficient = 0.676, t = 28.729, p < 0.0001), which provides empirical support for H2 (Table 4).

ASQ, administrative and support service quality; ICQ, Instructor and course material quality; SEQ, service quality; SPV, student perceived value; STL, student loyalty; STS, student satisfaction; SYQ, system quality.

ASQ, administrative and support service quality; ICQ, Instructor and course material quality; SEQ, service quality; SPV, student perceived value; STL, student loyalty; STS, student satisfaction; SYQ, system quality.

Discussion

The COVID-19 pandemic has resulted in a situation wherein universities are required to obligatorily adopt and ensure the availability of e-learning methodologies for their students; given the backdrop of this development, they thus also need, generally speaking, to enhance their awareness levels concerning the application of a framework for monitoring the quality of the coursework available on their e-learning course websites, and carrying out improvements as and when needed. This is done because the e-learning course website is a major part of the online learning process, owing to the fact that every interaction will be carried out through the e-learning course website. Several universities have improved the quality of their e-learning system by improving the functionality as well as the appearance of their website to make it easier to use, as well as by adding servers to share the traffic load emerging from the e-learning course website, so that the processes carried out on the website can run faster and better. The university needs to ensure that their e-learning course website always provides important and useful information for students to support their learning process. Furthermore, when the functionality and appearance of the e-learning course website are designed in such a way that it is not complicated and is easy to use, this will help students to use it effectively, without scope for any confusion to arise in the process. This can be done by continuously improving the system in a way that ensures the perennial satisfaction of the needs of students’ learning process. The university can also facilitate this by designing the functionality and appearance of the e-learning website in such a way that it directly displays important information for students on the homepage, such as information about schedules, task lists/deadlines, forums, and links for synchronous sessions. The improvement in the quality of the e-learning system is also directly proportional to the increase in the positivity characterising the overall student assessment of the services provided by the university in effectively and efficiently facilitating the use of e-learning. Any effort on the part of the university aimed at undertaking an improvement in the online services provided is perceived by students as an advantage conferring them with access to greater-quality online resources, in recognition of the fact that with such improvements being carried out regularly, it becomes possible for students to make a better use of elearning course websites. Improving the quality of e-learning systems is an important factor influencing the improvement of service quality obtained from e-learning (Pham et al., 2019).

The e-learning system that has been built will not work effectively if it is not equipped with good quality instructors and learning materials. eLearning allows instructors to be able to innovate in developing their teaching process to produce a good and interactive learning environment, which is something new and different from offline teaching. Instructors are required to adapt to the new online learning environment, which would facilitate not only the development of their talent and skills but also the development of that particular e-learning platform as a whole. The improvement of the quality of instructors and e-learning course materials is also directly proportional to the increase in the positivity of the overall student assessment of the services provided by the university in facilitating the use of e-learning efficiently and effectively. Students recognise that a heightened quality of teaching and learning materials can produce a more interactive e-learning environment, thus improving service quality, in turn having the effect of increasing students’ motivation to learn. When e-learning is used as the medium for conducting learning activities, this might create a sense of boredom in students owing to the fact of an unmediated expression of the instructor’s personality, as well as face-to-face interaction with the instructor, being missing, and accordingly, to attenuate the likelihood of such an eventuality, instructors need to come up with various means and devices to keep students engaged during virtual communication; the role of the instructor is thus very important in minimising breaks in students’ concentration, and employing effective strategies to this end can ultimately provide better learning outcomes. It is important for universities to pay attention to the characteristics of instructors and learning course material because they are a vital part of the e-learning process. The university can facilitate the instructors to perform better by inviting the assistance of experts from industry, whose help can also be taken to complete or improve the course material hosted online. Curriculum workshops can be conducted to review the curriculum continuously and maintain the same up to date according to conditions in the field. The university can also improve the course material and the knowledge of the instructors by arranging for training or seminars; further, access to new journals can be provided to increase the knowledge of the instructors and ensure the availability of new references in the preparation of course material. Flexibility in the delivery of teaching and instruction will improve the quality of e-learning (Elumalai et al., 2019). Improving the quality of instructors and e-learning course materials is one of the important factors influencing the improvement of the e-learning services quality (Pham et al., 2019).

e-Learning might result in students being confronted with several new problems that are not encountered when they undergo the offline learning process. This creates a necessity for the university to be able to continuously improve administrative and support services for students undergoing e-learning. The university is required to integrate its entire spectrum of online assistance services, in such a way that students can find remedies for all problems faced by them at a single online location, without there being a need for in-person meetings. The improvement in the administrative and e-learning support service quality is also directly proportional to the increase in the positivity of the overall student assessment of the services provided by the university in facilitating the efficient and effective use of e-learning. Students recognise that the readiness and role of the university staff in assisting in the problems faced by students, especially in terms of administration and support for e-learning, will ultimately improve student service assessments. Students, who were previously required to go directly to the administrative office and service assistance to get help, finally obtain the flexibility of being served anywhere and anytime. It is important for universities to improve the quality of e-learning administration and support services, because they are an important part of e-learning implementation (Elumalai et al., 2019). The university can prepare standard procedures for assistance in problems that are generally experienced by students so that faster services are available in solving student problems. The university can always improve administrative services and e-learning support in line with technological developments and together with recognising the potential for new problems to emerge. With this, students will obtain support that can help and facilitate the e-learning process. Improving the administrative and support service quality (ASQ) is one of the important factors influencing the improvement of the e-learning services quality (Pham et al., 2019).

The quality of e-learning services received by students is a reflection of the quality of everything that a university provides to facilitate students to undergo the e-learning process; therefore, the quality of e-learning services provided by the university has an important role to play in determining the degree of positivity of the assessment made by students of the university’s services. To improve the service quality characterising the e-learning process, the university should improve human resources and infrastructure. Improved service quality assessment of e-learning is directly proportional to the perceived value formed by students towards the university. Students will compare the quality of service received with the sacrifices made by them, including costs, energy and time. Good service quality can increase the perceived value of students, if the quality of service received exceeds the expectations of students (Hapsari et al., 2017). Students, as users of e-learning systems, typically arrive at their assessment of the quality of their interaction with the online system provided by the university only after considering the benefits and also the sacrifices made (Kilburn et al., 2016). The students, as individuals who have spent money on education, hope to be able to obtain good quality learning even though it is done online. Accordingly, improving the quality of e-learning services in terms of systems, instructors and course materials, as well as administrative support, will assist in realising the needs of students in undergoing the learning process.

Another top priority for the university is that the quality of e-learning services received by students should undergo continuous improvement. The university needs to pay attention to the perception of its customers, namely its students, to be able to produce a high level of satisfaction with the university. Improved e-learning service quality is directly proportional to the increase in student satisfaction with the university. In arriving at an assessment concerning the quality of e-learning services provided by the university, students ponder over the experience that has been felt during the learning process. With the increasing quality of e-learning services received by students, a pleasant experience would be available in undergoing the e-learning process, and this would also have an impact on the level of satisfaction felt by students (Pham et al., 2019). The perception of value that has been built from students can affect the level of student satisfaction with the university. The increase in value perceptions formed by students is directly proportional to the increase in student satisfaction with the university. Students will compare the sacrifices that have been made, including costs, energy and time, to their experience in undergoing the learning process, and in the end, this affects their level of satisfaction. Students will be more satisfied if the benefits received are comparable to or exceed the sacrifices and expectations. This also raises students’ perceptions of the seriousness of the university in preparing the e-learning process. When the perceived value of students undergoes an increase, this will ultimately lead to a sense of satisfaction from students towards their university (Halimatussakdiah et al., 2020; Lunarindiah, 2016). The university needs to pay attention to the perception of value built by students because it will affect the level of satisfaction of students, as customers of the university.

The perception of value built by students about their university will affect the level of their loyalty towards the university. The increase in value perceptions formed by students is directly proportional to the increase in student loyalty towards the university. Students will compare the sacrifices made, including costs, energy and time, to their experiences in undergoing the e-learning process, wherein if students feel that the benefits received are comparable to or even exceed the sacrifices, there will be a sense of willingness of students to share positive words and recommendations to others regarding the university. Students’ perceived value will also affect their intention to continue e-learning activities (Nugroho et al., 2019). It is important for the university to increase the perception of value from students, so that a sense of student loyalty towards the university can be created. The increase in the perceived value of students in the end also increases their loyalty towards the university (Kilburn et al., 2016).

The level of satisfaction felt by students towards their university will also affect the level of student loyalty towards their university. The increase in student satisfaction is directly proportional to the increase in student loyalty towards the university. The overall impression formed by students concerning their university would be based on their assessment of the experience felt in the course of the e-learning process. The satisfaction obtained by students will generate a sense of loyalty, which would in turn present a positive impression. Dissatisfaction of students in undergoing e-learning will cause a sense of unwillingness to continue e-learning (Nugroho et al., 2019). It is important for the university to attend to the need for ensuring that students are satisfied with the e-learning programme undergone by them and to increase the satisfaction of students, so that a sense of student loyalty towards the university can be created (Pham et al., 2019).

The quality of service received by students in undergoing the e-learning process can affect the value perception of the benefits gained vis-a-vis the sacrifices made; and then, this will affect the level of satisfaction of students with the university. The increase in the positivity of service quality assessment for e-learning is proportional to the increase in student satisfaction with the university mediated by perceived value of students. Students have an expectation with regard to participating in learning activities at their universities, and one of them is that the quality of e-learning provided by the university must be optimal or even excellent. If the quality of e-learning received can meet the expectations that have been formed previously, in the end this will lead to a sense of satisfaction for students towards their university (Demir et al., 2020).

The value perception felt by students regarding the benefits that have been gained vis-a-vis the sacrifices that have been made in undergoing the e-learning process will create a feeling of satisfaction from the students towards the university, since it has apparently undertaken the effort to prepare the e-learning curriculum in a way that would benefit students the most. Furthermore, it will favourably affect the level of student loyalty, and also result in other desirable forms of impact, such as “a positive impression and a sense of pride felt by students; in turn, these phenomena would result in the desire of students to share positive words about and recommend their university, as well as the desire to continue their further studies at the university. The increase in students’ perceived value is proportional to the increase in student loyalty towards the university mediated by student satisfaction. The students’ intentions concerning whether or not to continue the e-learning process are to a large extent contingent upon the satisfaction felt by them resultant to experiencing the e-learning services provided by the universities, and in turn, this satisfaction felt by students has an important role in mediating the perceived value felt by them (Chang, 2013; Nugroho et al., 2019).

Conclusion

The results of the research complement the results of previous studies, in which it was stated that system quality, instructor and course material quality, and administrative and support service quality had a positive and significant influence on e-learning service quality, with administrative and support service quality having the greatest influence. Furthermore, the addition of SPV variables shows that e-learning service quality has a positive and significant influence on SPV and student satisfaction. Good quality of e-learning services will increase the perception of value and the level of satisfaction of students.

Furthermore, SPV has a positive and significant influence on student satisfaction and student loyalty, and student satisfaction has a positive and significant influence on student loyalty. Furthermore, e-learning service quality has a positive and significant influence on STS with the mediation of SPV. An e-learning quality that is well-received by students will lead to the fulfilment of perceptions and expectations formed by students, and in the end, this will lead to a sense of satisfaction for students towards their university. Then, SPV has a positive and significant influence on student loyalty with the mediation of student satisfaction. The value perception formed by students in undergoing the e-learning process will create a feeling of satisfaction from students towards the university, and in the end will affect the level of student loyalty; additionally, it would also result in other desirable forms of impact, such as a positive impression and a sense of pride felt by students; in turn, these phenomena would result in the desire of students to share positive words about and recommend their university, as well as the desire of students to more effectively participate in the learning process and the desire to continue their further studies at the university.

It is important for the university to pay attention to and improve the factors that affect the quality of e-learning services, because the level of this quality is the primary factor influencing the SPV created and sustained towards the university, especially through a comparison of the benefits gained vis-a-vis the sacrifices made; further, in conjunction with the satisfaction of students arising from the experience during the e-learning process, it will in the end increase student loyalty towards the university.

In complementing the research that has been done, there are several things that can be done for further research. In conducting the research, the selection of universities was geographically limited by focussing on the student population in Tangerang, specifically at six universities. Further research can be done by expanding the population, to obtain a wide and comprehensive picture of the results of student perceptions. Research can also be done by focussing on different perceptions of learning. The hybrid learning process that is being developed will produce new perceptions for students, so that students’ perceptions with regard to their undergoing the hybrid learning process can be examined. This research focusses on the perception of learning in theory, so that research can be carried out that focusses on practical learning.

The e-learning process carried out can produce many perceptions for the research model. The research model can be developed by adding other new variables, such as the perception of students’ trust in the university as formed based on the e-learning process, and the perception of learning outcomes from the e-learning process. This can be done to ensure that it is accurately ascertained whether service quality in the e-learning process can have a good impact on student learning outcomes.

From the university’s perspective, while it can, in theory, improve and enhance every aspect involved in the e-learning process, there are several specific actions that can be considered to improve this process. Universities can improve e-learning systems by providing information that helps students when they have difficulties and more clear information about the flow of e-learning. Then, the university can also improve course material and the quality of lecturers by providing facilities that help lecturers to continuously update their knowledge through training, seminars, access to new journals and meetings with experts, professionals or academicians from other universities. Collaboration is very important for each party to be able to produce new learning materials and to be able to fulfil student needs. Furthermore, the function of the student assistance service unit needs to be improved and socialised properly to enable the provision of fast service for students. The role of universities in improving all factors pertaining to the provision of e-learning services will influence the perceptions of students concerning the university’s effectiveness and efficiency in providing these services, which, in turn, can be expected to influence the levels of satisfaction and loyalty of students towards the university. An e-learning service quality that is continuously improved will afford a unique experience for students in terms of knowledge acquisition, and also leave an enduring positive impression in them. Unpredictable conditions such as the COVID-19 pandemic may re-occur at any time in the future, and resultantly cause changes in the learning process. Therefore, the role of universities is very important to be able to facilitate the preparation of the e-learning process properly.

References

1. Abbasi, S., Ayoob, T., Malik, A., & Memon, S. I. (2020). Perceptions of students regarding e-learning during covid-19 at a private medical college. Pakistan Journal of Medical Sciences, 36(COVID19-S4), S57–S61. https://doi.org/10.12669/pjms.36.COVID19-S4.2766
2. AAbdur Rehman, M., Soroya, S. H., Abbas, Z., Mirza, F., & Mahmood, K. (2021). Understanding the challenges of e-learning during the global pandemic emergency: The students’ perspective. Quality Assurance in Education, 29, 259–276. https://doi.org/10.1108/qae-02-2021-0025
3. AAditi, B., Hafizah, H., & Hermansyur, H. (2021). The role of E-services, quality system and perceived value on customer satisfaction: An empirical study on Indonesian SMEs. Journal of Industrial Engineering and Management Research, 2(3), 193–205.
[Online]. Retrieved from https://jiemar.org/index.php/jiemar/article/view/195
4. AAlqahtani, A. Y., & Rajkhan, A. A. (2020). E-learning critical success factors during the covid-19 pandemic: A comprehensive analysis of e-learning managerial perspectives. Education Sciences, 10(9), 1–16. https://doi.org/10.3390/educsci10090216
5. AAlsabawy, A. Y., Cater-Steel, A., & Soar, J. (2012). Identifying the determinants of e-learning service delivery quality. In ACIS 2012 Proceedings of the 23rd Australasian Conference on Information Systems
6. AAnnamdevula, S., & Bellamkonda, R. S. (2016).The effects of service quality on student loyalty: The mediating role of student satisfaction. Journal of Modelling in Management, 11(2), 446–462. https://doi.org/10.1108/JM2-04-2014-0031
7. AArguelles, M. J. M., & Busquet, J. M. B. (2016). Perceived service quality and student loyalty in an online university. International Review of Research in Open and Distributed Learning, 17(4), 264–279.
8. AChandra, T., Ng, M., Chandra, S., & Priyono, P. (2018).The effect of service quality on student satisfaction and student loyalty: An empirical study. Journal of Social Studies Education Research, 9(3), 109–131. https://doi.org/10.17499/jsser.12590
9. AChang, C. C. (2013). Exploring the determinants of e-learning systems continuance intention in academic libraries. Library Management, 34(1), 40–55. https://doi.org/10.1108/01435121311298261
10. ADemir, A., Maroof, L., Sabbah Khan, N. U., & Ali, B. J. (2020). The role of e-service quality in shaping online meeting platforms: A case study from higher education sector. Journal of Applied Research in Higher Education, 13(5), 1436–1463. https://doi.org/10.1108/JARHE-08-2020-0253
11. ADoan, T. T. T. (2021). The effect of service quality on student loyalty and student satisfaction: An empirical study of universities in Vietnam. Journal of Asian Finance Economics and Business, 88, 251–258. https://doi.org/10.13106/jafeb.2021.vol8.no8.0251
12. AElumalai, K. V., Sankar, J. P., Kalaichelvi, R., John, J. A., Menon, N., Alqahtani, M.
S. M., & Abumelha, M. A. (2019). Factors affecting the quality of e-learning during the Covid-19 pandemic from the perspective of higher education students. Journal of Information Technology Education: Research, 19, 731–753. https://doi.org/10.28945/ 4628
13. AFida, B. A., Ahmed, U., Al-Balushi, Y., & Singh, D. (2020). Impact of service quality on customer loyalty and customer satisfaction in Islamic banks in the Sultanate of Oman. SAGE Open, 10(2), 1–10. https://doi.org/10.1177/2158244020919517
14. AGhozali, I., & Latan, H. (2015). Partial Least Square: Konsep, teknik dan aplikasi menggunakan program SMARTPLS 3.0 (2nd ed.). Indonesia: Badan penerbit Universitas Diponegoro.
15. AHair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications. ISBN: 9781-4522-1744-4. 307 pp.
16. AHalimatussakdiah, H., Majid, M. S. Abd., & Azis, N. (2020). Mediating effect of student perceived value on student satisfaction in the Indonesian private higher education institutions. International Journal of Management in Education, 14(6), 611–627. https://doi.org/10.1504/ijmie.2020.10030771
17. AHapsari, R., Clemes, M. D., & Dean, D. (2017). The impact of service quality, customer engagement and selected marketing constructs on airline passenger loyalty. International Journal of Quality and Service Sciences, 9(1), 21–40. https://doi.org/10.1108/IJQSS-07-2016-0048
18. AHartanto, W. (2016). Penggunaan e-learning sebagai media pembelajaran. J. Pendidik. Ekon, 10(1), 1–18
19. AIbojo, B. O., & Asabi, O. M., (2015). Impact of customer satisfaction on customer retention: A case study of a reputable bank in Oyo, Oyo State. Nigeria. International Journal of Management Studies Research, 3(2), 42–53. [Online 2022]. Retrieved from www.arcjournals.org
20. AIlyas, F. (2021). The SWOT analysis on mass physical distancing (PSBB) and restriction on community activities (PPKM) regulation and its economic impact as measures to reduce COVID-19 spread. Akrab Juara J, 6(3), 190–198.
21. AKilburn, B., Kilburn, A., & Davis, D. (2016). Building collegiate E-loyalty: The role of perceived value in the quality-loyalty linkage in online higher education. Contemporary Issues in Education Research, 9(3), 95–102. https://doi.org/10.19030/cier.v9i3.9704
22. AKusumawati, A., & Rahayu, K. S. (2020). The effect of experience quality on customer perceived value and customer satisfaction and its impact on customer loyalty. The TQM Journal, 32(6), 1525–1540. https://doi.org/10.1108/TQM-05-2019-0150
23. ALai, F., Griffin, M., & Babin, B. J. (2009). How quality, value, image, and satisfaction create loyalty at a Chinese telecom. Journal of Business Research, 62(10), 980–986. https://doi.org/10.1016/j.jbusres.2008.10.015
24. ALunarindiah, G. (2016). The influence of corporate image, service quality, perceived value toward student satisfaction and student loyalty. Jurnal Manajemen dan Pemasaran Jasa, 9(2), 239. https://doi.org/10.25105/jmpj.v9i2.1460
25. AMawar, M., Andriyani, L., Gultom, A., & Ketiara, K. (2021). Dampak Sosial Ekonomi Kebijakan Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) di Indonesia. Seminar Nasional Penelitian. LPPM UMJ, (Vol. 1 chap. 8), 1–12
26. AMuhammad, A., Feham, M., Ahmad, F., Naveed, Q. N., & Shah, A. (2016). A study to investigate state of ethical development in e-learning. International Journal of Advanced Computer Science and Applications, 7(4), 284–290.https://doi.org/ 10.14569/ijacsa.2016.070436
27. ANugroho, M. A., Setyorini, D., & Novitasari, B. T. (2019). The role of satisfaction on perceived value and e-learning usage continuity relationship. Procedia Computer Science, 161, 82–89. https://doi.org/10.1016/j.procs.2019.11.102
28. APham, L., Limbu, Y. B., Bui, T. K., Nguyen, H. T., & Pham, H. T. (2019). Does e-learning service quality influence e-learning student satisfaction and loyalty? Evidence from Vietnam. International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0136-3
29. APham, L., Williamson, S., & Berry, R. (2018). Student perceptions of e-learning service quality, e-satisfaction, and E-loyalty. International Journal of Enterprise Information Systems, 14(3), 19–40. https://doi.org/10.4018/IJEIS.2018070102
30. APramana, C., Susanti, R., Violinda, Q., Yoteni, F., Rusdiana, E., Prihanto, Y. J. Nñ Haimah. (2020). Virtual learning during the covid-19 pandemic, a disruptive technology in higher education in Indonesia. International Journal of Pharmaceutical Research, 12(2), 3209–3216. https://doi.org/10.31838/IJPR/2020.12.02.430
31. ARizvi, Y. S., & Nabi, A. (2021). Transformation of learning from real to virtual: An exploratory-descriptive analysis of issues and challenges. Journal of Research in Innovative Teaching and Learning, 14(1), 5–17. https://doi.org/10.1108/jrit-10-20200052
32. ASadikin, A., & Hamidah, A. (2020). Pembelajaran Daring di Tengah Wabah Covid-19. Biodik, 6(2), 109–119. https://doi.org/10.22437/bio.v6i2.9759
33. ASalloum, S. A., Qasim Mohammad Alhamad, A., Al-Emran, M., Abdel Monem, A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7, 128445–128462. https://doi.org/10.1109/ACCESS.2019.2939467
34. ASharma, A., Gupta, J., Gera, L., Sati, M., & Sharma, S. (2020). Relationship between customer satisfaction and loyalty on the internet. Social Science Research Network. https://doi.org/10.2139/ssrn.3913161
35. ASyarifudin, A. S. (2020). Impelementasi Pembelajaran Daring Untuk Meningkatkan Mutu Pendidikan Sebagai Dampak Diterapkannya Social Distancing. Jurnal Pendidikan Bahasa dan Sastra Indonesia Metalingua, 5(1), 31–34. https://doi.org/10.21107/metalingua.v5i1.7072
36. ATan, C. (2021). The impact of COVID-19 on student motivation, community of inquiry & learning performance. Asian Education and Development Studies, 10(2), 308–321. https://doi.org/10.1108/AEDS-05-2020-0084