Development and initial validation of the antibiotic use behavior assessment questionnaire based on the com-b model among university students

Development and initial validation of the antibiotic use behavior assessment questionnaire based on the com-b model among university students Indriyati Hadi Sulistyaningrum1, Hadi Sarosa2, Erki Arfianto1, Prasojo Pribadi3 1 Pharmacy Department, Faculty of Pharmacy, Universitas Islam Sultan Agung, Semarang, Indonesia 2 Physiology Department, Medical Faculty, Universitas Islam Sultan Agung, Semarang, Indonesia 3 Pharmacy Department, Faculty of Health Sciences, Universitas Muhammadiyah Magelang, Magelang, Indonesia Corresponding author: Prasojo Pribadi (p[email protected]) Received 23 August 2025♦ Accepted 27 October 2025♦ Published 1 December 2025 Citation: Sulistyaningrum IH, Sarosa H, Arfianto E, Pribadi P (2025) Development and initial validation of the antibiotic use behavior assessment questionnaire based on the com-b model among university students. Pharmacia 72: 1–10. https://doi.org/10.3897/ pharmacia.72.e169411 Abstract Antibiotic resistance is a serious threat to global health and is estimated to cause 10 million deaths per year by 2050. Therefore, this study aimed to design a comprehensive, valid, and reliable antibiotic use behavior questionnaire based on the COM-B model theory. The research method used was a cross-sectional observational study with a purposive sampling technique. A total of 258 participants aged 18 years and ovlder received the questionnaire. They were selected using convenience sampling from various population groups. Data analysis was performed using exploratory factor analysis (EFA). Factor extraction was conducted using principal component analysis and varimax rotation. The research results showed that there are 6were six valid constructs and 17 statement items. The six constructs that emerged were identity (professional and objective), social pressure, subjective, peer support, behavior, and emotion. Cronbach’s alpha reliability testing showed that most constructs had values above 0.70, indicating they were reliable. These results suggest that the COM-B model antibiotic use behavior questionnaire is both valid and reliable. The implications of this research indicate that the developed questionnaire can be used to identify antibiotic use behavior based on the capability, opportunity, and motivation components making it easier for healthcare professionals and educators to provide more targeted interventions or guidance. Keywords antibiotic, COM-B model, reliability, validity Introduction Antibiotic resistance poses a significant threat to world health, with projections indicating it could result in 10 million fatalities annually by 2050 if effective policies are not enacted (Duan et al. 2021a). Antibiotics are a fundamental resource, and antibiotic resistance is acknowledged as one of the most significant risks to global public health, especially in Indonesia. Indonesia ranks among the top five countries anticipated to experience the largest percentage increase in antibiotic consumption by 2030. Sulistyaningrum et al. (2022) reported a rise in antibiotic utilization Copyright Sulistyaningrum IH et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Pharmacia 72: 1–10 DOI 10.3897/pharmacia.72.e169411 Research Article Sulistyaningrum IH et al.: Development and initial validation of the antibiotic2 at pharmacies during the epidemic due to the escalating threat of antibiotic resistance (WHO 2022). The incidence of antibiotic resistance attributed to microorganisms in Indonesia is on the rise. Annually, 1.27 million individuals succumb to drug-resistant illnesses. This alarming trend emphasizes the need to implement improved infection control measures and public awareness campaigns. Addressing the root causes of antibiotic misuse and enhancing surveillance systems are crucial steps toward combating this growing health crisis (Kemenkes 2022). Healthcare personnel and patients share responsibility for antibiotic resistance, as there is a direct association between excessive antibiotic utilization and the development of antibiotic resistance. This trend is largely attributable to improper prescribing by healthcare professionals (Davey et al. 2002; Goossens et al. 2005; Costelloe et al. 2010; Morgan et al. 2011; Roque et al. 2013). Duan et al. (2021b) observed that antibiotic resistance arises from the variable utilization of prescribed antibiotics, frequently attributable to inadequate adherence to medical guidance, the sharing of surplus medications, and/or the self-administration of antibiotics for treatment (Duan et al. 2021b; Arfianto et al. 2023). The issue of resistance impacts not just human therapy but also the transmission of resistance between animals and people, which remains an area of investigation. Numerous studies released in recent years indicate that the administration of antibiotics in animals may facilitate the development of resistant infections in both animals and humans (Endtz et al. 1991; van den Bogaard and Stobberingh 2000; Institute of Medicine (US) Committee on Health Literacy 2004; Marshall and Levy 2011; Alexander et al. 2017; Tang et al. 2017). In patients, this is predominantly attributable to excessive medicine usage, incomplete treatment regimens, self-medication through sharing medications, reserving doses for future use, or procuring pharmaceuticals without a prescription. Antibiotics are frequently stored medications in homes throughout Central Java (Davey et al. 2002; Roque et al. 2013; Sulistyaningrum et al. 2023). Comprehending attitudes and determinants that affect decision-making, as well as converting behavioral intents into enduring behavioral modifications, are increasingly acknowledged as essential tools for combating antibiotic resistance (Jones et al. 2015). Research is required to comprehensively understand the factors influencing antibiotic utilization behavior across all domains (human health and livestock) to facilitate the implementation of effective interventions that encourage judicious antibiotic usage (Fischer et al. 2019). The COM-B model serves as an extensive framework that incorporates multiple elements affecting behavior modification (Michie 2014). This model was constructed based on multiple health behavior theories, including the theory of planned behavior, the health belief model, social cognitive theory, protection motive theory, self-determination theory, the transtheoretical model, and the health action process approach (Michie et al. 2011). Multiple meta-analyses suggest that these theories can explain up to 37% of behavioral variation, considered a significant contribution (Young 2006) by standards (Cohen 1988). Examples encompass the theory of reasoned action (McEachan et al. 2016), the health belief model (Harrison et al. 1992), social cognitive theory (Young et al. 2012), self-determination theory, and the transtheoretical model (Plotnikoff et al. 2013). The COM-B model is anticipated to account for a greater extent of behavioral diversity, possibly surpassing the predictive power of alternative behavioral theories. COM-B is utilized for (a) directing data collection and analysis in qualitative research, (b) formulating behavioral treatments (Barker et al. 2016), and (c) evaluating findings from systematic reviews (Simon and West 2015). However, despite its widespread use, there is no standard tool fully capable of measuring all six domains in the COM-B model. This absence of a tool hinders the evaluation of the model’s predictive validity and the mechanisms of COM-based interventions on behavior change. Only two previous questionnaires have been evaluated for reliability and validity. Howlett et al. (2019) determined that psychological capabilities and reflective motivation predict physical activity behavior, while Ayton et al. (2021) demonstrated that their instrument possesses sufficient internal consistency and construct validity in assessing individuals’ perceptions of their ability, opportunity, and motivation to engage in exercise. Nevertheless, neither study incorporated an evaluation of questionnaire acceptance levels, thus limiting comprehension of the questionnaire’s pertinence and appropriateness from the user’s viewpoint. The lack of test–retest analysis constrains the evaluation of the reliability of the employed items. A more succinct COM-B instrument is also required. The COM-B questionnaire presently comprises a variable number of items, from 10 (Stevely et al. 2018) to 194 (Balku et al. 2017), the latter being deemed excessively lengthy for practical use. Consequently, the creation of a novel generic self-assessment questionnaire is essential, designed to evaluate individuals’ perceptions of ability, opportunity, and motivation across many behavioral contexts and demographics, including patients, healthcare professionals, and the general populace. This is significant, as prior questionnaires were predominantly crafted for particular behavioral circumstances. This study seeks to overcome these constraints by developing a novel questionnaire that effectively and consistently assesses the six subdomains of the COM-B model, employing a stringent psychometric methodology to guarantee the instrument’s reliability and validity. Materials and methods Research design This study employed a quantitative methodology using a survey approach. A survey is a research method used to gather information about the prevalence, distribution, and interrelationships among variables within a population. Data were collected through questionnaires, assessments, interviews, and similar instruments administered Pharmacia 72: 1–10 3 at specific locations. The study was conducted from June 2025 to August 2025 and involved 258 individuals from three universities in Central Java Province, Indonesia. Research instrument The questionnaire comprised 21 items prepared by the researcher in accordance with the theoretical framework of the study and existing literature (Courtenay et al. 2019; Addo et al. 2022; Ashiru-Oredope et al. 2022; Farrell et al. 2023b, 2023a). Due to the researcher’s modifications to the instrument, the likelihood of measurement error was slightly increased. Therefore, the most suitable data collection method for psychological assessment was through questionnaires. The questionnaire consisted of four sub-aspects: planning, self-motivation, performance, and self-reflection. The research variables were evaluated using these instruments. The questionnaire was carefully developed to ensure it functioned as an effective data collection tool, guaranteeing valid and reliable results. A 5-point Likert scale was employed (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree). Collecting data The redesigned and validated questionnaire was subsequently distributed to 258 students from three selected universities for testing. During data collection, students were informed that participation was voluntary and would not affect their grades. Data were collected using purposive sampling, as response rates among web-based students are typically high. This study was approved by the Faculty of Medicine Ethics Committee of Universitas Islam Sultan Agung (reference number No. 282/VI/2025/ Bioethics Commission). Data analysis The data were analyzed using JASP version 26. The reliability of the questionnaire was evaluated by examining its content and construct validity. Reliability analysis involved calculating Cronbach’s alpha coefficient, with values above 0.45 signifying acceptable internal consistency (Taber 2018). This study employed exploratory factor analysis (EFA) to assess the validity and reliability of the instrument using the JASP software version 0.14.1.0. EFA is a statistical technique that enhances the reliability of a scale by identifying and eliminating unsuitable components. The factorial validity of the questionnaire was assessed using the Kaiser–Meyer–Olkin (KMO) statistic and Bartlett’s test. A KMO value of 0.70 or higher indicated satisfactory factorial validity. To establish construct validity, in addition to factor loadings of 0.4 or higher, the following fit indices were used to evaluate model goodness of fit: χ² values between 2 and 5, Tucker–Lewis Index (TLI) and Comparative Fit Index (CFI) of 0.90 or higher, standardized root mean square residual (SRMR) of 0.08 or lower (Leung et al. 2021), and a root mean square error of approximation (RMSEA) of 0.05 or lower with a 90% confidence interval that included this value, indicating good fit (Xia and Yang 2019). Results Sociodemographic characteristics of respondents Out of the total questionnaires distributed, 258 were returned fully completed, resulting in a response rate of 86%. This high response rate indicates that respondent participation was strong and sufficient for further analysis according to research standards. Based on the characteristics of the respondents (Table 1), the majority were in the 17–25-year age group (72.48%), indicating that most respondents were young adults. Most respondents were female (86.05%) rather than male. The most common education level was a bachelor’s degree (S1) at 52.33%, followed by senior high school graduates at 36.05%, indicating that respondents generally had a secondary to higher education background. By academic year, most respondents were first-year students (69.77%), suggesting significant participation from new students. Regarding place of residence, the distribution was relatively balanced, though slightly higher among those from rural areas (52.71%) compared to urban areas (47.29%). Table 1. Results of respondent characteristics. Characteristics N 258 (%) Age (years old) 17–25 187 (72.48) 26–35 27 (10.47) 36–65 44 ( 17.05) Gender Male 36 (13.95) Female 222 (86.05) Level of education Senior high school 93 (36.05) D3 10 (3.88) S1 135 (52.33) Profesi/S2/S3 20 (7.75) Academic year First year 180 (69.77) Second year 31 (12.02) Third year 28 (10.85) Fourth year 19 (7.36) Residence Rural area 136 (52.71) Urban area 122 (47.29) Monthly expenses < 582.932 47 (18.22) > 582.932–847.398 54 (20.93) > 847.398–2.040.262 83 (32.17) > 2.040.262–9.909.844 63 (24.42) > 9.909.844 11 (4.26) Sulistyaningrum IH et al.: Development and initial validation of the antibiotic4 This distribution suggests that educational opportunities and challenges may vary depending on geographic location. In terms of monthly expenditure, the largest group fell within the range of > 847,398–2,040,262 IDR (32.17%), followed by > 2,040,262–9,909,844 IDR (24.42%), indicating that most respondents had a moderate level of expenditure. Content validity The results of the content validity test indicated that the involvement of three experts made a significant contribution to assessing the quality of the research instrument. The experts critically examined each item of the instrument, considering the suitability of the indicators, the clarity of the language, and the relevance of the content to the research objectives. Through this process, several recommendations were provided, which later formed the basis for improving the instrument – for example, simplifying sentence wording, strengthening substantive aspects, and adjusting indicators to be more representative. The instrument, revised based on expert assessment, can therefore be considered to have adequate content validity and is suitable for use in research (Table 2). The calculation was mainly based on expert evaluation results. Content validity is divided into the item-content validity index (I-CVI) and the scale-content validity index (S-CVI). The I-CVI was calculated by dividing the number of experts who rated items 3 or 4 points by the total number of experts, while the S-CVI was calculated by dividing the number of items that received 3 or 4 points from all experts by the total number of scale items. I-CVI > 0.78 and S-CVI > 0.80 indicate that the scale has good content validity (Zamanzadeh et al. 2015). Exploratory factor analysis In this study, factor analysis was used to test the instrument’s validity by applying EFA with the help of the JASP software version 0.14.1.0. A rule of thumb (Hajji et al. 2016) states that at least 300 samples are required for factor Table 2. COM-B model content validity. Variable Item code Statement CVI Conclusion Subjective and objective SUB1 I feel I have enough knowledge about how to use antibiotics. 0.89 Valid SUB 2 I am aware of the importance of responsible antibiotic use. 1.00 Valid SUB 3 I feel like I’m using antibiotics wisely in my treatment. 1.00 Valid OB1 Antibiotics can kill bacteria. 1.00 Valid OB2 Antibiotics can kill viruses (reverse coded). 0.89 Valid OB3 Excessive use of antibiotics makes them less effective. 0.78 Valid OB4 The antibiotics used on one individual are sometimes the same as those used on another. 0.78 Valid Social pressure SOCPRE1 I feel pressure from doctors or pharmacists to reduce my antibiotic use. 0.78 Valid SOCPRE1 I feel that the doctor or pharmacist encourages me to use antibiotics wisely. 1.00 Valid SOCPRE1 I feel pressure from the government to reduce antibiotics. 0.78 Valid Peer support PS1 I often discuss individual health with my friends. 0.89 Valid PS2 I often exchange advice with friends about reducing antibiotics. 0.67 Valid Identity professional IP1 Good individuals use new approaches and strategies. 0.89 Valid IP2 Good individuals make decisions based on scientific data. 0.89 Valid Emotion EM1 If I stop using antibiotics completely, I will feel satisfied. 1.00 Valid EM2 I would feel wise if I reduced the excessive use of antibiotics. 0.89 Valid EM3 I would feel calm if I only used antibiotics when I was sick. 0.78 Valid Behavior B1 I am taking the antibiotic dosage as directed by the doctor or pharmacist. 1.00 Valid B2 I’m recording the use of antibiotics at home. 0.89 Valid B3 If I look healed, I’ll stop the antibiotics before the time is up (reverse coded). 0.89 Valid B4 I keep a stock of antibiotics to use without consulting a doctor. (reverse coded) 0.56 Valid Pharmacia 72: 1–10 5 analysis. Vautier et al. (2013) suggested that a sample size of 100 or more is adequate. In this study, 258 participants met the EFA requirements. Kaiser–Meyer–Olkin and Bartlett’s test Presents (Table 3) the overall measure of sampling adequacy (MSA) value in this study as 0.785, indicating that the sample was adequate for factor analysis. Bartlett’s test of sphericity was significant (χ² = 1536.648; df = 210; p < 0.001), confirming that the instrument was suitable for analysis (Gülay Ogelman et al. 2015). A probability value < 0.05 indicates that the sample size and normality were sufficient for performing principal component analysis (PCA), the relationships between variables were strong, and the data were suitable for exploratory factor analysis (Onwuegbuzie and Leech 2005; Manzar et al. 2018). Factor loadings The results of the EFA with varimax rotation and a cutoff loading criterion of ≥ 0.40 (DeVellis 2003) revealed six main factors. Seventeen of the 21 total items met the practical criteria for retention, indicating that the measured construct could be considered stable and valid. These six factors represented the dimensions of personal and objective identity (5 items), social pressure (3 items), subjective understanding (3 items), peer support (2 items), behavior (2 items), and emotional aspects (2 items). Thus, this factor structure was considered sufficiently strong to proceed to the confirmatory factor analysis (CFA) stage. The description of the EFA results is presented in Table 4. Additionally, Fig. 1 displays the exploratory components of the antibiotic use behavior instrument in a scree plot. Thereafter, a scree plot was created by mapping the derived eigenvalues, as shown in Fig. 1, which illustrates the development of the six main components. The scree plot suggested that six factors could be considered to explain the factor structure of the assessment tool for antibiotic use behavior among university students. Therefore, only the first six factors had eigenvalues greater than one based on the simulated data (Fig. 1). The results (Table 5) of the reliability test using Cronbach’s alpha show that most constructs had values above 0.70, indicating that they were reliable. Construct 1 (Identity professional and objective) obtained a value of 0.784; Construct 2 (Social pressure) was 0.718; Construct 3 (Subjective) was 0.782; and Construct 4 (Peer support) was 0.755. These four constructs are considered reliable because they are above the minimum threshold of 0.70 recommended for social research (Hair et al. 2019). Construct 5 (Behavior) had a value of 0.625, which, although lower, is still considered acceptable in exploratory research because it exceeds the 0.60 threshold (Sharaf et al. 2018). Construct 6 (Emotion), with a value of 0.496, was deemed acceptable, as literature acknowledges that a Cronbach’s alpha value of ≥ 0.50 is acceptable during the initial phases of instrument development or exploratory research (Thorndike 1995), despite being below 0.60. Overall, these findings indicate that the six factors in this instrument demonstrate satisfactory reliability and are suitable for further analysis. Additional fit indices The CFI, TLI, and RMSEA each include a penalty for model complexity, which penalizes the multiple correlated factors model more than the higher-order model because the correlated factors model is less parsimonious – it requires more parameters to be estimated – than the higher-order model (Table 6). The RMSEA measures the absolute fit error and estimates how well the model fits the data in terms of the average error per degree of freedom (Alexander and Oesterreich 2013). A good model fit is indicated by an RMSEA value of less than 0.06. Researchers can determine whether the suggested model is appropriate for the data by using these Table 3. Kaiser–Meyer–Olkin and Bartlett’s test results. Overall MSA Χ² df p 0.785 1536.648 210.000 <0.001 Figure 1. EFA path diagram and scree plot. a. EFA path diagram; b. Scree plot. a b Sulistyaningrum IH et al.: Development and initial validation of the antibiotic6 criteria to evaluate the model fit quality. By measuring the average standardized difference between the observed and estimated covariances, the SRMR offers a clear indication of absolute fit (Rasoal et al. 2011). A satisfactory fit is defined as an SRMR score less than 0.08. This cut-off point implies that the model fits the data well because there is only a small difference between the observed and projected covariances. Increased values could indicate problems with model fit, and a review and modification of the model might be required to better capture the relationships in the data. By contrasting the suggested model with a null model, the TLI evaluates how well it fits. The TLI’s unique ability to penalize excessively complicated models makes it especially valuable for promoting parsimony in model evaluation (Shi et al. 2019; Jian et al. 2024). After adjusting for model complexity, a TLI score of ≥ 0.90 is considered acceptable, meaning that the suggested model provides an adequate fit compared with the null model. The CFI compares the assessed model with a null model that assumes no relationships between variables. This index measures how much the suggested model improves fit in comparison with the null model after accounting for variations in sample size and model complexity (Marsh et al. 2014). A CFI value of approximately 0.90 indicates a good model fit. Because the Bayesian Information Criterion (BIC) has somewhat lower power than the other four techniques, it can be disregarded in real-world situations involving small sample sizes. Consequently, model selection can be refined by applying only the convergence selection approach. Discussion This study describes the development of the first short and generic measure – a 6-item self-evaluation questionnaire – designed to assess perceived abilities (physical and psyTable 4. Results of extraction factor. Item code Construct 1 Construct 2 Construct 3 Construct 4 Construct 5 Construct 6 Uniqueness IP1 0.705 0.462 IP2 0.668 0.501 B1 0.643 0.463 SOCPRE2 0.558 0.594 OB1 0.490 0.678 SOCPRE1 0.804 0.342 SOCPRE3 0.765 0.371 EM1 0.450 0.649 SUB2 0.928 0.005 SUB3 0.511 0.543 SUB1 0.475 0.569 PS2 0.851 0.181 PS1 0.582 0.491 B3 0.765 0.372 B4 0.516 0.599 EM3 0.691 0.486 EM2 0.436 0.668 OB2 0.859 OB3 0.940 OB4 0.912 B2 0.833 Note. Applied rotation method: varimax. Table 5. Reliability test results. COM-B model Constructs Cronbach’s alpha Interpretation Motivation and capability Construct 1 (identity professional and objective) 0.784 Reliable Opportunity Construct 2 (social pressure) 0.718 Reliable Capability Construct 3 (subjective) 0.782 Reliable Opportunity Construct 4 (peer support) 0.755 Reliable Behavior Construct 5 (behavior) 0.625 Reliable Motivation Construct 6 (emotion) 0.496 Acceptable Table 6. Measurement fit indices results. Parameters Value Cut-off value Interpretation RMSEA 0.045 ≤0.06 Good fit SRMR 0.032 <0.08 Good fit TLI 0.917 ≥0.90 Good fit CFI 0.962 ≥0.90 Good fit Pharmacia 72: 1–10 7 chological), opportunities (physical and social), and motivation (reflective and automatic). It represents the first effort to develop and psychometrically test a concise antibiotic use behavior questionnaire based on the COM-B model, intended for use across various population groups, including patients, healthcare professionals, and the general public. Evidence supports satisfactory questionnaire acceptance, as well as test–retest reliability and discriminant and predictive validity. There are four main findings. First, the test–retest reliability was fair to good for four of the six items included in the questionnaire (physical opportunity, social opportunity, physical ability, and psychological ability) and excellent for two items (reflective motivation and automatic motivation). However, the final sample included only healthcare professionals (Keyworth et al. 2020). The Self-management Capability, Support, and Motivation–Behavior scale for elderly hypertensive patients showed good reliability and validity, but the GFI, CFI, IFL, and TLI results of the scale did not meet the optimal fit criteria of 0.90 (Wu et al. 2023). EFA is essential during the early stages of instrument development to confirm whether the items effectively measure the intended dimensions (Fabrigar et al. 1999). Its main advantage lies in its flexibility as a data-driven approach, which does not require strong theoretical hypotheses at the outset. Instead, it allows the existing data structure to determine how variables should be grouped. This functionality is achieved through the selection of suitable factor retention criteria and rotation methods, as suggested by Costello and Osborne (2005). EFA improves the construct validity of an instrument by showing that the items on a questionnaire are related to each other and grouped together in the right way (Osborne 2014). EFA enhances the construct validity of an instrument by confirming that questionnaire items are empirically correlated and grouped together along intended dimensions (Brown 2015). Furthermore, EFA acts as an effective data reduction tool, transforming a large number of observed variables into a more manageable set of interpretable factors, which facilitates further statistical analysis (Osborne 2014). Farrell et al. (2023b) also used the COM-B framework (Capability, Opportunity, Motivation, Behavior), but the context and stage of questionnaire development differed. The article by Farrell et al. investigated the factors influencing dairy farmers’ use of antibiotics using a pre-developed and empirically tested questionnaire. The question items focused on the technical abilities of farmers, access to veterinarians, regulations, and economic and ethical motivations. Meanwhile, the article on consumer behavior in the use of antibiotics for URTI is still a study protocol. The designed questionnaire places more emphasis on aspects of public understanding, social norms, and beliefs, with planned validation through an expert panel and pilot testing. Thus, the main difference lies in the stage of implementation: research on farmers produced ready-to-use instruments based on field experience, while research on consumers is still in the conceptual stage. This comparison shows that COM-B is flexible and can be used in both professional and general public contexts. The instrument is proven to be reliable because each COM-B construct (Capability, Opportunity, Motivation) has adequate internal consistency, and it is valid because it can represent the real-world conditions of farmers’ practices. The question items focused on technical, regulatory, and economic as well as ethical motivational aspects (Farrell et al. 2023b). Conversely, articles on consumer behavior in the use of antibiotics for URTI are still study protocols, so the instruments have not been statistically tested. The planned validity is still limited to content validity through an expert panel, and its reliability will be tested in the pilot study phase. Thus, Farrell et al.’s (2023b) research has already produced a ready-to-use instrument based on empirical evidence, while research on consumer ARI is still in the conceptual stage. This comparison confirms the flexibility of COM-B in designing questionnaires for both professionals (farmers) and the general public, while also highlighting the differences in instrument maturity in terms of validity and reliability testing (Duan et al. 2021b). These findings can serve as a foundation for healthcare practitioners, educators, and counselors to design more targeted health education and promotion interventions – particularly to encourage rational antibiotic use among students and the general public. The implications of this research indicate that the developed questionnaire can be used to identify antibiotic use behavior based on the Capability, Opportunity, and Motivation components, enabling healthcare professionals and educators to provide more focused interventions or guidance. However, this study has limitations, particularly the absence of EFA testing to verify the instrument’s structure. Therefore, future research is recommended to conduct CFA to ensure the structural validity and reliability of this antibiotic use behavior questionnaire. Conclusion This study successfully developed and initially validated a questionnaire to assess antibiotic use behavior based on the COM-B model. Exploratory analysis results indicated that the questionnaire contains key factors reflecting the Capability, Opportunity, and Motivation aspects. This questionnaire has the potential to serve as a useful tool for identifying factors influencing antibiotic use behavior and for designing more effective interventions. However, this study still has limitations; therefore, further testing using CFA is needed to ensure the instrument’s validity and reliability more comprehensively. Acknowledgments Direktorat Riset Teknologi dan Pengabdian Masyarakat, Direktorat Jenderal Pendidikan Tinggi, Riset dan Teknologi, Kementerian Pendidikan Tinggi, Sains, dan Teknologi Republik Indonesia. Sulistyaningrum IH et al.: Development and initial validation of the antibiotic8 Additional information Conflict of interest The authors have declared that no competing interests exist. Ethical statements The authors declared that no clinical trials were used in the present study. The authors declared that no experiments on humans or human tissues were performed for the present study. The authors declared that no informed consent was obtained from the humans, donors or donors’ representatives participating in the study. The authors declared that no experiments on animals were performed for the present study. The authors declared that no commercially available immortalised human and animal cell lines were used in the present study. Use of AI No use of AI was reported. Funding No funding was reported. Author contributions The authors confirm contributions to the paper as follows: study conception and design, HIS, PP, EA, HS; data collection, HS, EA; analysis and interpretation of results, PP, EA; draft manuscript preparation, HIS, PP, EA, HS. All authors reviewed the results and approved the final version of the manuscript. Data availability All of the data that support the findings of this study are available in the main text. References Addo MK, Aboagye RG, Tarkang EE (2022) Factors influencing adherence to antiretroviral therapy among HIV/AIDS patients in the Ga West Municipality, Ghana. International Journal of Infectious Diseases Regions 3: 218–225. https://doi.org/10.1016/j.ijregi.2022.04.009 Alexander E, Butler C, Darr A, Jenkins MT, Long RD, Shipman CJ, Stratton T (2017) ASHP statement on telepharmacy. 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