The study recruited ten clinical nurses using purposive sampling combined with maximum variation sampling, a technique designed to capture a wide range of experiences across different departments, seniority levels, and clinical backgrounds. Data were collected through face-to-face, semi-structured interviews, a method that allows participants to speak freely while the interviewer probes specific themes. The researchers then applied framework analysis, a structured qualitative approach in which data are systematically charted against an organizing framework, in this case the five classic gaps described by the SERVQUAL model. To ensure the findings were trustworthy, the team followed Lincoln and Guba’s criteria for qualitative rigor and reported the work according to the COREQ checklist, a 32-item standard for transparent reporting of qualitative research.

The SERVQUAL model, originally developed for service industries, conceptualizes quality as the distance between what customers expect and what they perceive they received, and it breaks that distance into five diagnostic gaps: the knowledge gap between customer expectations and management’s understanding of them, the standards gap between management perception and service specifications, the delivery gap between specifications and actual service, the communication gap between what is delivered and what is promised externally, and ultimately the gap between expectation and perception. Mapping the nurses’ accounts onto this architecture, the researchers identified five core themes and thirteen sub-themes, each aligning with one or more of these gaps. The framework gave structure to complaints that might otherwise have remained anecdotal, turning individual frustrations into a diagnosable map of organizational failure points.

The first theme, a demand for optimization of medical service processes, corresponds to the knowledge gap. Nurses who became patients described convoluted admission procedures, unclear pathways between departments, and waiting sequences that seemed designed without any consideration of the patient’s perspective. As staff members, they understood the logic behind each step, yet experiencing the process firsthand revealed how disorienting and inefficient it felt from a hospital bed. This insider-outsider contrast is precisely what makes the dual perspective so valuable: managers who design processes rarely walk through them as patients, and ordinary patients lack the technical vocabulary to articulate exactly where the design breaks down.

The second and fourth themes both mapped onto the delivery gap, the distance between the service a hospital specifies and the service it actually provides. Participants pointed to shortcomings in the hospitalization environment and in service details, from physical discomforts on the ward to small failures of coordination that compounded during a stay. Separately, they described deviations in nursing professional competence and execution, moments when the care delivered did not match the professional standard that nurses themselves know should apply. That nurses criticized their own profession’s execution is notable; it suggests the delivery gap is not merely a resource problem but also one of consistency, supervision, and the everyday drift that occurs when standards are not actively enforced.

A third theme, insufficient nurse-patient communication and humanistic care, aligned with the standards gap, and the researchers interpreted its interpersonal communication component as an extension of the communication gap. Participants reported that during their own hospitalizations, explanations were thin, emotional support was scarce, and the human dimension of care, the listening, the reassurance, the acknowledgment of fear, was often absent. Technical competence was present, but the relational fabric that transforms procedures into care was frayed. This finding resonates with a large body of nursing literature showing that patients judge quality at least as much by how they are treated as by what is done to them, and it suggests that communication standards within the hospital were not sufficiently specified or operationalized.

The most striking discovery, however, emerged from a cross-cutting analysis that the authors termed being assumed to know. Because the participants were hospital employees, providers simply withheld the information, explanations, and follow-up that are routinely offered to lay patients. Staff assumed a nurse-patient already understood her diagnosis, her medications, and her discharge plan, so they skipped the briefings, the teach-back conversations, and the check-in calls that form the backbone of patient education. This silent omission manifested both as a knowledge gap, since the institution failed to recognize what this particular class of patients actually needed, and as a failure of interpersonal communication at the level of individual care encounters. The phenomenon reveals a subtle form of discrimination by expectation: being an insider paradoxically reduced the quality of care received.

The implications of being assumed to know extend well beyond the ten nurses interviewed. Any hospital employs hundreds of clinicians, administrators, and students who eventually become patients, but the underlying mechanism, tailoring communication based on assumptions about who the patient is, applies to anyone perceived as knowledgeable, including physicians treated at their own institutions, returning patients familiar with the system, and health professionals’ family members. The study suggests that patient-centered communication should be a default protocol rather than a judgment call, because assumptions about a patient’s knowledge are frequently wrong and almost never verified. A fifth theme, limited awareness of the hospital’s advantageous disciplines, was interpreted as a staff-facing extension of the knowledge gap, indicating that even employees lacked full information about the institution’s centers of excellence, a signal of internal communication weaknesses.

Methodologically, the study demonstrates the analytical power of pairing a dual-perspective population with a mature service quality framework. Framework analysis allowed the researchers to remain anchored to SERVQUAL’s five gaps while still letting new patterns, such as being assumed to know, surface from the data, and the two interpretive extensions they propose show how a decades-old model can be adapted to novel contexts. The single-center design and small sample of ten participants limit generalizability, and the authors themselves frame their findings as generating context-specific strategies that warrant further evaluation rather than as universally applicable prescriptions. Qualitative work of this kind is hypothesis-generating by design, and the natural next step is to test whether the identified gaps respond to targeted interventions, such as standardized communication checklists or process redesign informed by patient walkthroughs.

The broader significance of the research lies in its alignment with the World Health Organization’s vision of integrated, people-centered health services. Traditional quality evaluations rely heavily on patient satisfaction scores, which are known to be inflated by politeness, low expectations, and fear of consequences, and they systematically miss the latent gaps that only insiders can see. By asking nurses to evaluate their own hospital as patients, the study effectively recruited expert auditors who cannot be fooled by the facade of quality. If hospitals routinely incorporated the dual perspective of staff-turned-patients into their quality improvement cycles, they would gain a diagnostic instrument that is cheap, ethically straightforward, and uniquely sensitive to the seams where promised care and delivered care come apart. For a profession built on caring for others, the study is a reminder that the most revealing test of a hospital’s quality may be how it treats its own.

Subject of Research: Nursing service quality gaps examined through nurses' dual experiences as care providers and patients using the SERVQUAL model

Article Title: Nurse as patient: a qualitative study on nursing service quality gaps based on the SERVQUAL model

Article References: Nurse as patient: a qualitative study on nursing service quality gaps based on the SERVQUAL model. (n.d.). https://doi.org/10.1186/s12912-026-05495-x

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05495-x

Keywords: nursing, SERVQUAL model, service quality, qualitative research, patient experience, nurse-patient communication, hospital care, framework analysis, patient-centered care, BMC Nursing, Zhejiang University, humanistic care

The research team, led by Xinke Liu and Mingqing Wei of Dongzhimen Hospital at Beijing University of Chinese Medicine, together with colleagues including Jing Shi and Jinzhou Tian, conducted a cross-sectional online survey of physicians in January 2025. The final analytic sample comprised 393 physicians drawn from 27 provincial-level regions of China, a geographic spread that gives the results unusually broad coverage for a study of this kind. Rather than simply asking doctors for general impressions, the investigators built a dedicated measurement instrument called the Chinese Medicine Service Quality scale, or CMSQ, which integrates general service-quality principles familiar from mainstream health services research, the philosophy of patient-centered care, and service characteristics that are specific to traditional Chinese medicine, such as herbal decoction services and the evaluation of TCM clinical practice.

The technical rigor of the scale’s validation is a central feature of the study. The six-domain CMSQ demonstrated high internal consistency, with a total Cronbach’s alpha of 0.945 and domain-level alphas ranging from 0.784 to 0.896, values that indicate the items within each domain reliably measure a coherent underlying construct. Confirmatory factor analysis supported the proposed six-factor structure, with a comparative fit index of 0.946, a Tucker-Lewis index of 0.938, a root mean square error of approximation of 0.056, and a standardized root mean square residual of 0.053, all of which fall within conventional thresholds for acceptable model fit. Importantly, the authors also tested measurement invariance and found it supported across hospital levels and hospital types, meaning the instrument measures the same constructs in tertiary referral centers and smaller community institutions alike, a prerequisite for fair comparisons between different tiers of the Chinese health system.

When the scores themselves were examined, a clear pattern emerged. Physicians rated pre-visit responsiveness most favorably, with a domain mean of 4.31 on the scale, followed closely by appointment service accessibility at 4.30. These findings suggest that the front end of the TCM care pathway, the scheduling systems and the way institutions respond to patient inquiries before an appointment, has improved considerably and is now seen by providers as a relative strength. At the other end of the ranking, medication supply services received the lowest domain rating at 3.96, and within that domain, the waiting time for herbal decoctions stood out as the single lowest-rated item in the entire survey, with a mean of 3.61 and a standard deviation of 1.22. For a system in which individually prepared herbal formulas are a cornerstone of treatment, a bottleneck at the decoction stage represents a tangible friction point that patients experience directly.

The subgroup analyses added a geographic and institutional dimension to the picture. Among the physician and institutional characteristics examined, city tier showed the most consistent differences in service-quality ratings, indicating that the level of urban development of a physician’s location is more strongly associated with perceived service quality than factors such as hospital level or hospital type. This pattern echoes well-known disparities in Chinese healthcare more broadly, where megacities concentrate resources, specialist expertise, and modern infrastructure, while smaller cities often struggle to match the same operational standards. The fact that such disparities appear even in physicians’ assessments of their own institutions underscores how deeply structural these differences are.

Perhaps the most consequential analysis concerned the link between service quality and physicians’ willingness to recommend their institutions, a proxy for institutional loyalty and confidence that has parallels in patient recommendation metrics used worldwide. Using ordinal logistic regression with adjustment for covariates, the researchers found that higher recommendation intention was associated with three domains in particular: pre-visit responsiveness, with an adjusted odds ratio of 1.278 and a 95 percent confidence interval of 1.112 to 1.469 and a p value below 0.001; medication supply services, with an adjusted odds ratio of 1.132 and a confidence interval of 1.036 to 1.237 and a p value of 0.006; and TCM practice evaluation, with an adjusted odds ratio of 1.144 and a confidence interval of 1.019 to 1.284 and a p value of 0.022. In plain terms, physicians who felt their institutions were responsive before visits, reliable in supplying medications, and supportive of quality TCM clinical practice were significantly more likely to speak well of those institutions.

To capture the physicians’ own priorities in their own words, the team also collected open-ended suggestions and analyzed 283 substantive responses using latent Dirichlet allocation, a topic-modeling technique that identifies recurring themes in large text corpora without imposing predefined categories. The modeling surfaced three broad clusters of priorities. The first concerned institutional capacity and clinical standardization, reflecting a desire for stronger organizational foundations and more consistent clinical protocols. The second centered on clinical quality, affordability, and the care experience, linking the technical excellence of TCM practice to the financial accessibility of services and the overall patient journey. The third cluster addressed operational management, workforce development, and service accessibility, pointing to the everyday machinery of scheduling, staffing, training, and logistics that determines whether a well-designed service model actually functions in practice.

Taken together, the results sketch a system in which the visible, patient-facing elements of TCM care have advanced faster than the back-office operations that support them. Physicians clearly appreciate improvements in responsiveness and appointment access, but the weak ratings for medication supply and decoction waiting times suggest that the supply chain for herbal medicines, from procurement through preparation to dispensing, has become the limiting factor in perceived service quality. The regression findings reinforce this interpretation, because medication supply services emerged as one of only three domains independently associated with recommendation intention. An institution that cannot reliably deliver the medicines its physicians prescribe undermines both clinical trust and staff morale, regardless of how skilled its practitioners may be.

The study also carries methodological significance for the broader field of complementary and integrative medicine research. Service-quality assessment in TCM has long been hampered by the absence of validated, provider-centered instruments that respect the modality’s distinctive features, from individualized herbal formulations to the integration of traditional diagnostic methods with modern clinical workflows. By demonstrating strong reliability, acceptable confirmatory factor fit, and measurement invariance across institutional strata, the CMSQ offers researchers and administrators a reusable framework that can be deployed in future surveys, quality-improvement programs, and policy evaluations. The authors position it as a preliminary but psychometrically supported, physician-centered instrument, and its open-access publication means that other teams can scrutinize, replicate, and extend the validation work.

The authors, whose work was supported by the Strategic Research and Consulting Project of the Chinese Academy of Engineering and several provincial and national research programs, emphasize that strengthening operational reliability must go hand in hand with enhancing TCM-specific clinical quality and organizational support. The study was approved by the Institutional Review Board of Dongzhimen Hospital and conducted in accordance with the Declaration of Helsinki. As traditional Chinese medicine continues to expand within China’s integrated health system and attracts growing international interest, this survey provides a rare quantitative baseline from the people best placed to judge where the system works and where it strains: the physicians on the front line. Their message is consistent and actionable, celebrating genuine gains in accessibility and responsiveness while demanding focused investment in the medication supply chain, workforce development, and the standardization of clinical practice that will determine whether the next generation of TCM services can match the expectations of both providers and patients.

Subject of Research: Physician-assessed service quality of traditional Chinese medicine healthcare in China

Article Title: Assessing traditional Chinese medicine service quality from physicians’ perspectives: a nationwide survey in China

Article References: Assessing traditional Chinese medicine service quality from physicians’ perspectives: a nationwide survey in China. (n.d.). https://doi.org/10.1186/s12906-026-05549-2

Image Credits: AI Generated

DOI: 10.1186/s12906-026-05549-2

Keywords: traditional Chinese medicine, service quality, physician survey, health services research, CMSQ scale, psychometric validation, medication supply, herbal decoction, patient-centered care, China, ordinal logistic regression, topic modeling

The study arrives at a moment when the hospitality sector, which contributes roughly 10 percent of global GDP, is under intense pressure to reinvent itself. India’s travel and tourism market is expected to grow from an estimated US$75 billion in FY20 to US$125 billion by FY27, with international tourist arrivals projected to reach 30.5 million by 2028 and the sector supporting around 40 million jobs. At the same time, global AI spending is forecast to exceed $300 billion by 2026, and McKinsey estimates that generative AI could add between $2.6 trillion and $4.4 trillion annually to the world economy. Against that backdrop, the authors set out to answer a deceptively simple question: which factors actually convert AI-powered personalization into satisfied, loyal guests?

The research team, led by Prashant Chaudhary of Dr. Vishwanath Karad MIT World Peace University, together with Nilesh Kate and Sujoy Kumar Jana of the Pune Institute of Business Management and Pradip Padhye of Symbiosis International University, built a conceptual model containing seven constructs: service quality, expectancy, response time, perceived value, customer experience, perceived risk and customer satisfaction. Drawing on models such as the European Customer Satisfaction Index and expectancy-value theory, they formulated twelve hypotheses about how these variables interact. Data were collected through a structured questionnaire distributed via Google Forms using convenience sampling, with all respondents over 18 years old and most drawn from southern India. The instrument comprised twenty-five items rated on five-point Likert scales, covering everything from the punctuality of AI-driven services to perceptions of price fairness and data privacy.

Methodologically, the analysis was rigorous by the standards of survey-based hospitality research. Confirmatory factor analysis verified the measurement model, with fit indices including the Chi-square/degree of freedom ratio, the Comparative Fit Index, the Tucker-Lewis Index and the root mean square error of approximation all falling within acceptable ranges. Reliability was strong: the Cronbach’s alpha of 0.870 comfortably exceeded the conventional 0.70 threshold, and both convergent validity, with construct reliability above 0.7 and average variance explained above 0.5, and discriminant validity were established. The team then applied structural equation modelling using IBM AMOS 20.0 and SPSS 22.0 to test the causal structure implied by their hypotheses, examining standardized path coefficients across the full network of relationships.

The headline finding is striking in its clarity. Perceived value emerged as the strongest and most consistent predictor of customer satisfaction, with a standardized path coefficient of 0.65. In other words, guests who felt that AI-assisted hospitality services delivered cost-effectiveness, convenience and benefits worth the price paid were the ones most satisfied and most inclined toward loyalty. Service expectancy, in turn, was the strongest predictor of perceived value at 0.60, and also significantly shaped perceived risk at 0.25. This chain, from expectancy through value to satisfaction, formed the backbone of the supported model and suggests that how much customers believe in and engage with AI technologies fundamentally determines how much value they perceive, and therefore how satisfied they become.

Response time told a more complicated story. It significantly influenced perceived value with a negative coefficient of −0.10, a counter-intuitive result the authors interpret as reflecting a preference for empathetic, human-like interaction over sheer automated speed. Faster AI-mediated responses were associated with somewhat lower perceived value in this sample, hinting that pure automation without warmth may feel hollow to guests who still crave human connection. At the same time, response time positively shaped perceived risk at 0.39, indicating that slower AI responses heightened worries about privacy and reliability. These nuanced dynamics suggest that the tempo of AI service delivery matters, but not in the simplistic way that faster-is-always-better intuition would predict.

Perhaps the most provocative results are the ones that failed to materialize. Seven of the twelve hypotheses were rejected. Service quality showed no significant direct effect on perceived value, perceived risk or customer experience, a pattern the authors attribute partly to shared-variance suppression, since expectancy and response time, which correlated moderately with service quality, absorbed much of the explanatory power. Customer experience likewise had no significant direct effect on satisfaction, and perceived risk did not significantly predict satisfaction at all. The authors caution that the perceived risk items were worded in a reassuring, trust-oriented direction rather than as threat measures, so the null result should be read as evidence that trust was already accounted for through expectancy and value, not that privacy concerns are irrelevant to Indian consumers.

These departures from Western findings carry real theoretical weight. Prior studies, including work by Wirtz and colleagues on frontline service robots and research highlighting customer discomfort with automation-driven lack of transparency, have emphasized the risks and ethical complexities of AI in service settings. The Indian data instead suggest a region-specific pattern: when the value proposition of AI, such as efficient, personalized and convenient service, is tangible and evident, consumers demonstrate a higher tolerance for AI-related risk and data privacy concerns. The young, price-conscious, largely student-dominated sample, 66 percent of whom were students aged mostly between 18 and 30, may amplify this utilitarian orientation, and the authors are careful to flag the convenience sampling and southern-India concentration as limitations that warrant caution in generalizing to older or non-student segments.

The practical implications for hotel chains, budget accommodations and online travel platforms are nonetheless concrete. The findings recommend that hospitality operators integrate AI into systems, processes and communication channels to raise service expectancy, and blend AI-powered analytics with human intervention to achieve something approaching hyper-personalization. AI tools enhanced with multilingual capabilities can break language barriers in booking experiences, while automation frees human staff for tasks requiring empathy and creativity. According to a Dun & Bradstreet survey cited in the study, 100 percent of surveyed Indian organizations have AI projects underway, around 73 percent report measurable returns, and 69 percent plan to increase AI investments. An Adobe survey of more than 5,000 Asia-Pacific consumers found that nearly 95 percent of Indian consumers trust AI-powered technologies to improve their experience, a strikingly higher figure than in New Zealand at 54 percent or Australia at 57 percent.

Ultimately, the study’s novelty lies in identifying perceived value and service expectancy, rather than service quality alone, as the critical levers through which AI integration can lift consumer satisfaction and loyalty in emerging markets such as India. The authors propose a revised, more parsimonious model in which expectancy and response time act primarily through perceived value and risk to drive satisfaction, and they call for future research using stratified national samples, risk-worded measurement instruments, and longitudinal or experimental designs to establish causality. For an industry racing toward digital transformation in the post-pandemic landscape, the message is clear: algorithms and chatbots may deliver the service, but it is the perception of value, carefully calibrated and honestly communicated, that wins the guest. AI, deployed strategically and blended with human ingenuity, becomes a catalyst for loyalty, but only when guests can see, in hard terms, what the technology is worth to them.

Subject of Research: The effects of artificial intelligence-enabled personalization on customer satisfaction and customer experience in the Indian hospitality industry

Article Title: The effects of artificial intelligence enabled personalization on customer satisfaction and customer experience in the Indian hospitality industry

Article References: Chaudhary, P., Kate, N., Jana, S. K., & Padhye, P. (2026). The effects of artificial intelligence enabled personalization on customer satisfaction and customer experience in the Indian hospitality industry. Discover Artificial Intelligence, 6(1), Article 1183. https://doi.org/10.1007/s44163-026-02250-8

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02250-8

Keywords: artificial intelligence, hospitality industry, customer satisfaction, customer experience, personalization, perceived value, India, structural equation modelling, service quality, perceived risk, loyalty, emerging markets