Enhancing Students' Engagement Of Library
A Social Learning Theory Perspective
Jurnal Antarabangsa Dunia Melayu
Southern Journal of Oriental Studies
rokokbet rokokbet rokokbet rokokbet rokokbet rokokbet
Hospitals -structured around medical units or services- function as central hubs for essential care, specialized treatments, and emergency services, making them a vital component of healthcare systems. After decades of rising healthcare expenditures (), health remains a strategic priority in developed countries, representing a major budget item and a driver of related industries and employment. Within this complex systems, clinical units are led by managers who face organizational responsibilities that extend far beyond their clinical expertise.
To address this dual role, they have a variety of tools known as performance management systems (PMS) designed for managerial purposes in an attempt to better align individual behaviour and decision-making in ways that help achieve organizational goals (; ; ). However, most PMS were originally designed for private industrial sectors () mostly in Anglo‐Saxon contexts (), and they must be adapted to the specific circumstances of their use, particularly when implemented in public healthcare settings (). Therefore, both profit and non-profit healthcare organizations employ PMS tools (e.g., key performance indicators, cost accounting, balanced scorecards, and reward systems at a service level) whose effectiveness is not only guaranteed by their technical design but also by the perceived legitimacy and utility among those who use them.
Although PMS are widely implemented in hospitals, few empirical studies have assessed their effectiveness when used by physicians. Existing research primarily focuses on CEOs, CFOs, or other administrative roles, despite evidence that the use of PMS varies depending on the professional background of hospital leadership (). This paper aims to fill this gap by surveying clinical unit managers about the tools they use and examining whether their use is associated with better performance in their units. To identify these tools, we conducted an in-depth literature review to compile a diverse selection for consideration by the clinical unit manager. While not exhaustive, this list includes tools that are commonly used in previous research (; ; ) and presumably also used by practitioners (See Appendix I).
This study explores the significance of PMS using waiting time (WT) as a proxy for performance. In health systems, WT is a critical indicator of efficiency, patient satisfaction and timely access to care. WT is defined as the number of days from a patient’s inclusion on the official waiting list to the delivery of the scheduled specialist service. Our first aim is to analyse if PMS can help clinical unit managers reduce patients WT by shaping individual behaviour towards organizational goals (; ; ; ), assuming that the shorter WT indicates better outcomes whereas longer WT suggest poorer performance. The reasons are twofold. First, it provides a valid measure for all the services considered and enable comparison across them. Second, all services follow the same registration rules, ensuring homogeneity in data calculation. In the public health system, individuals must access services based on the priority assigned to their condition, so that patients with the same pathology and severity who have been waiting longer are attended to first ().
From a theoretical perspective, some frameworks support the notion that when clinical managers perceive management tools as essential, the quality and effectiveness of their services tend to improve. According to the theory of planned behaviour (TPB) (), positive attitudes toward a given tool increase the likelihood of its consistent and effective use. Managers who perceive a tool as beneficial are therefore more likely to be committed to implementing organizational or managerial measures. Within the TPB framework, positive perceptions of PMS tools are expected to foster their active integration into managerial routines and decision-making processes. This relationship has been supported by various studies in health-related contexts , ; ).
Similarly, organizational commitment theory (OCT) emphasizes that when managers are engaged and recognize a tool’s importance, they are more likely to foster sustained improvements in care delivery (; ). In this context, managers who perceive PMS tools as valuable may also be more committed to organizational objectives and more inclined to translate performance information into concrete managerial decisions.
Accordingly, the proposed model assumes that PMS use influences managerial engagement with performance information. This engagement may, in turn, support more effective managerial practices, such as the monitoring of waiting lists, the use of process and outcome indicators, the alignment of clinical activity with management agreements, and the comparison of performance across units. These practices are expected to be associated with more favourable waiting-time patterns, although the exploratory design of this study does not allow causal relationships to be established. Building on these theoretical arguments, this study is guided by the following research question:
Rather than providing confirmatory causal evidence, the study offers an exploratory and descriptive assessment of whether services whose managers perceive PMS tools as more relevant tend to show more favourable WT patterns. Prolonged delays can negatively affect patient outcomes and quality of life, whereas shorter WTs are linked to greater satisfaction and trust. Furthermore, international organizations consider WT a standard benchmark for assessing health system performance ().
The study is developed considering the seven public hospitals managed by Servizo Galego de Saúde (SERGAS), the Galician Regional Health Service. These hospitals comprise 196 medical units providing healthcare to a population of 2,699,499 inhabitants, of whom 25.18% are over 64 years old (). The study is situated within the Spanish National Health System (NHS), which is organized into 17 regional healthcare services, while private healthcare plays a complementary role. Accordingly, the contextual setting of the investigation is the medical services with inpatient activity operating in Galician public hospitals during 2015 and 2016.
The contribution of the paper is twofold. First, it contributes to current management accounting and healthcare literature by providing evidence of an association between the use of PMS and lower WT for patients requiring specialized medical care. Second, it shifts the traditional focus on top-level management (CEOs, CFOs, etc.) to mid-level clinical unit managers The results support that PMS improve WT outcomes, benefiting both patient satisfaction and hospital performance.
The remainder of the paper is structured as follows: first, we outline the research methodology and present the empirical findings. Subsequently, we discuss these results in light of the theoretical framework, and finally, we draw conclusions and reflect on their implications for research and practice.
The data sources included, on the one hand, information provided by the clinical unit manager through a survey, and on the other, data from the Galician Regional Healthcare Service (SERGAS). The data provided by SERGAS refers to the WT for the 67 clinical services that completed the questionnaire, thereby reflecting the performance of each clinical unit. Waiting times are calculated as the number of days from referral to the first specialized consultation or procedure and are routinely consolidated at the clinical unit level. SERGAS applies standardized recording and reporting protocols, including uniform criteria for waiting list registration, internal validation, and audit procedures, ensuring comparability and reliability across all seven public hospitals in Galicia. To ensure temporal coherence between the survey responses and the performance indicators, WT data were matched to the corresponding period in which the questionnaire data were collected.
Regarding the questionnaires, once the necessary contact data and the approval of the general director of the hospital had been obtained, we followed the five steps proposed by , starting with sending an e-mail to the clinical unit manager () that included a link to the web survey as well as a brief description of the study's purpose and a guarantee of confidentiality. Although we contacted all 196 services in population, we finally received 70 fully completed valid questionnaires (see Appendix II). However, three of them were excluded due to missing data, resulting in 67 usable responses, representing a response rate of 34.18%. Given the challenging context of surveying busy clinical managers in a public health system, the combination of personalized invitations, multiple reminders, telephone follow-up, and an extended fieldwork period yielded a response rate (34.18%) that is not only acceptable but comparatively strong relative to established benchmarks in this field. For instance, and examine performance management systems in organizational and university settings, and report response rates of 11.04% and 10.3%, respectively. Thus, response rates in comparable studies frequently fall within the 10–25% range and can drop below 15% in online surveys. Against this benchmark, our response rate of 34.18% is comparatively high and exceeds typical expectations.
The questionnaire consisted of 14 items addressing PMSs within each clinical unit. These systems encompass a range of tools, including balanced scorecards, key performance indicators (KPIs), cost accounting systems, benchmarking mechanisms, reporting systems (e.g., employee satisfaction measures), and incentive systems (; ). Due to contextual specificities, additional clinically oriented tools—such as clinical sessions and teaching and research meetings—are also incorporated. Although not strictly a PMS tool, the strategic plan is closely related, as it provides the foundation for PMS design and implementation. Furthermore, within SERGAS’s administrative and clinical governance structures, assessment agreements (acordos de xestión) were included to reflect local specificities and ensure comprehensive coverage of all tools relevant to PMS in this context.
The questionnaire was developed following established guidelines for survey design in management and healthcare research (; ). The process consisted of four stages. First, literature review and item generation: initial items were drawn from validated PMS instruments, primarily the framework proposed by , which has been widely used in management accounting research. Additional items were informed by prior studies on PMS in healthcare settings (; ; ). Second, expert review and content validation: the initial draft was pre-tested with nine experts, including four experienced clinical unit managers, two hospital administrators (non‑clinical expertise), two university academics specializing in management accounting and healthcare management, and the head of the local official medical association. Experts were asked to assess item clarity, relevance, comprehensiveness, and face validity. Revi-sions were made based on their feedback, and some items (e.g., management agreement) were added as a result of this analysis. Third, pilot testing: the revised questionnaire was piloted with a small group of clinical managers to identify any remaining ambiguities, technical issues with the web based format, and to estimate completion time. Fourth, the resulting instrument consisted of 14 items assessing the perceived existence and useful-ness of 14 specific PMS tools (see appendix) measured on a 6 point scale (0 = not availa-ble, 1 = barely used ... 5 = essential use). The estimated completion time was 12–15 minutes.
Given the size and nature of the available dataset, the empirical strategy was designed as an exploratory and descriptive analysis rather than as a confirmatory inferential test. The study contacted the full population of eligible clinical services in the Galician public hospital system, but the final number of usable responses was 67. This limited sample size restricts the statistical power of conventional inferential techniques and makes complex multivariate modelling unsuitable for the objectives of this paper.
Accordingly, the analysis focuses on describing whether favorable WT outcomes are more frequently observed among clinical units whose managers’ report higher perceived relevance or use of PMS tools. Contingency tables are used to present the distribution of units across PMS uses categories and WT groups. These tables provide an intuitive way to identify patterns of association without implying causality.
Exact bivariate tests are reported as a complementary robustness-oriented check because they are more appropriate than asymptotic chi-square tests when cell counts are small. However, these tests are not used as the basis for confirmatory claims. Instead, their results are interpreted together with the observed counts, expected counts, odds ratios, and confidence intervals as descriptive evidence of possible patterns.
The empirical analysis is based primarily on descriptive contingency tables. These tables organise categorical data into a structured format and display the frequency of clinical units classified according to two dimensions: managers uses of PMS tools and WT performance. In this study, cross-tabulation is used to describe patterns in the data and to identify whether certain PMS uses categories tend to appear more frequently among units with shorter or longer WT.
This approach is appropriate for an exploratory study with a limited number of observations, as it allows the data structure to be presented transparently without imposing the assumptions required by more complex statistical models.
Our aim is to examine how the acknowledgment of PMS by clinical unit managers is associated with WT. This approach enables us to explore the relationship between specific PMS tools (independent variable) and WT (dependent variable), thereby contributing to the identification of potential drivers of health care inefficiencies, specifically excessively long WT, reported as the most frequently mentioned problem in the Spanish healthcare system. In our study, frequency is used to describe the number of medical units with a short or long WT whose managers acknowledge the relevance of each PMS tool.
Given the relatively small sample size and the presence of low expected cell counts, Fisher’s exact test was additionally used to assess whether the descriptive patterns observed in the contingency tables were robust to small-sample conditions. However, the purpose of this analysis is not to make confirmatory statistical claims, but to provide additional information on the strength and direction of the observed bivariate patterns.
To apply Fisher test, it was necessary to construct two response groups for each tool based on the distribution of responses. These response groups differ across tools because some are not widely implemented or known, whereas others are compulsory and therefore exhibit high levels of use. For example, in Galician hospitals, several PMS tools —particularly cost accounting, employee satisfaction measures, and incentive and reward systems— are largely absent at the clinical service level or are managed centrally rather than by clinical unit heads. Cost accounting, when available, is typically handled by hospital analytical units, while employee satisfaction is only occasionally measured at the hospital level, and incentive systems are centrally regulated with limited managerial discretion. Consequently, for these tools, the relevant analytical distinction is between perceived existence (any use: 1–5) and non-available or non-use (–1, 0), as most respondents report little or no direct engagement.
By contrast, management agreements, key process indicators, and key outcome indicators are widely present and well established across clinical units, with most respondents reporting some level of use and a substantial proportion considering them indispensable. In these cases, where tools are actively integrated into decision-making, planning, and continuous improvement, responses are categorized as indispensable (5) versus non-indispensable (–1 to 4).
The balanced scorecard (BSC) shows uneven implementation: although widely known and conceptually adopted, its formal application as an integrated management tool varies. Similarly, clinical sessions and teaching/research meetings are universally present but differ in whether they are used as structured management tools or remain routine activities. In both cases, the relevant distinction is between higher-intensity, indispensable use (4–5) and lower or non-managerial use (–1 to 3).
Key structural indicators and benchmarking practices are generally known but only partially institutionalized. Their use differentiates between those who actively monitor and employ them for management purposes (3–5) and those who make little or no use of them (–1 to 2).
Finally, while the strategic plan formally exists in all hospitals, its practical use at the clinical service level is heterogeneous. The key distinction lies between moderate-to-high engagement (2–5), where it informs managerial decisions, and minimal or no engagement (–1 to 1), where it remains largely disconnected from day-to-day practice.
To address concerns regarding the sample size, we complemented the multivariate analysis with a series of exact bivariate tests. Each PMS tool was dichotomised according to the distribution-informed criteria described, and Fisher’s exact test was computed for the resulting 2×2 tables. Table 2 displays the odds ratios (OR), 95% confidence intervals, and exact p-values for all 14 tools.
Note OR = odds ratio for meeting the WT standard (short wait). CI = confidence interval. All p values are two tailed exact probabilities. Because the study is exploratory, p-values are reported only as descriptive indicators and are not used as the sole basis for interpreting the substantive relevance of the findings.
As shown in Table 2, none of the bivariate associations reached statistical significance at the conventional .05 level. Therefore, the results should be interpreted cautiously and should not be considered confirmatory evidence of an association between individual PMS tools and WT. However, the odds ratios provide descriptive information on the direction of the observed association. Several managerial tools, including the balanced scorecard, management agreements, key process indicators, and key outcome indicators, showed odds ratios above 1, indicating a tendency toward shorter WT among units where these tools were perceived as more relevant. In contrast, employee satisfaction measures and incentive and reward systems showed odds ratios below 1. Given the small sample size and wide confidence intervals, these patterns should be interpreted as exploratory and hypothesis-generating rather than definitive.
For the list of PMSs presented to managers, Tables 3 and 4 display the results of contingency analyses, categorized as short (<32 days) and long (≥32 days) WT. The 32-day threshold corresponds to the sample median and was adopted to create two groups of comparable size, thereby improving balance in the analysis and avoiding arbitrary external cut-off points not supported by the data (). For each tool, tables present both the observed and expected counts of services. Because cut-off points were defined according to the empirical distribution of each tool, the resulting classifications should be interpreted as context-specific analytical groupings rather than standardized thresholds.
Table 3 shows that, for most tools, the observed number of clinical services with short WT was higher than expected when managers reported using these tools. Overall, the results suggest better-than-expected performance for 11 of the 14 tools when they were actively used by the clinical unit managers.
Observed frequencies of short WT were equal to or higher than expected for nearly all PMS tools, with only exceptions the strategic plan and key structural indicators. These findings suggest that the use of PMS tools is generally associated with shorter waiting times across clinical units. The strongest positive associations were observed for incentive and reward systems, first-level cost accounting tools, and key outcome indicators, indicating that these mechanisms may be particularly relevant for improving service performance and reducing WT.
Although dichotomization may reduce variability, it was considered appropriate for exploratory purposes and to facilitate interpretation under small-sample conditions.
Table 4 complements these findings by examining the same tools among services with long WT (>32 days). In this analysis, a different pattern emerged. Consistent with findings for short WT (Table 3), for 11 tools, the observed counts were lower than expected, indicating that these tools were used less frequently among services experiencing prolonged waits. Again, the higher difference is for two tools, first-level cost accounting and incentives and rewards systems. These tools were substantially underrepresented among clinical services reporting long WT, reinforcing the positive association with short waiting times observed in Table 3.
Overall, the descriptive results suggest that units whose managers use certain PMS tools more intensively tend to exhibit more favourable WT patterns. However, given the exploratory nature of the study and the absence of statistically significant bivariate associations, these findings should not be interpreted as evidence of a direct causal relationship between PMS use and reduced WT, particularly in organizational contexts that may differ from the one examined in this study.
In the context of New Public Management (NPM), private sector practices were introduced into the public sector to enhance efficiency, accountability, and performance. In hospital management, NPM has promoted decentralization, managerial autonomy, and the use of PMS (). These systems are applied not only at the hospital level but at the level of individual departments or clinical services, such as surgery, radiology, or emergency care. Within this framework, the clinical unit manager plays a key role, acting as the link between clinical practice and organizational objectives. Their engagement in using PMS is critical for implementing performance-based strategies, assessing outcome and motivating staff. However, to the best of our knowledge, this perspective has been insufficiently explored in the literature.
This paper examined how clinical unit managers use PMS tools and how such use is associated with service performance. While previous research typically acknowledges the technical effectiveness of these tools and assumes their positive impact on performance (; ), less attention has been paid to the role of the clinical unit manager in actively incorporating these tools into managerial practice.
Given the exploratory nature of the empirical design, the findings should be interpreted as descriptive patterns rather than as confirmatory evidence. The analysis identifies whether certain PMS uses tend to co-occur with shorter WT, but it does not establish causality or statistically robust predictive relationships. This distinction is particularly important because the limited sample size constrains statistical power and prevents the use of more demanding multivariate empirical strategies. Although the sample size reflects the inherent challenges of gaining access to practitioners, the study nevertheless provides valuable insights into clinical middle management.
Our findings suggest that managerial awareness and acceptance may be relevant for understanding how PMS are used in clinical units. Consistent with theories of planned behaviour () and organizational commitment (; ), a positive managerial attitude and perceived usefulness of PMS are linked to improved service-level outcomes. Importantly, effectiveness depends not only on technical design but also on the engagement of those responsible for their use.
The findings may also reflect the hybrid nature of physician-managers, who operate between professional clinical logics and managerial expectations. In this context, PMS adoption may depend less on formal organizational mandates and more on whether managers perceive these tools as compatible with professional values and clinical priorities.
Empirically, the descriptive results suggest that greater use of PMS tools is associated with more favourable WT patterns across healthcare services. Specifically, there is a positive deviation between observed and expected number of units with better results in WT, a pattern identified for most tools. In contrast, the lack of association between clinical-oriented tools (teaching and research meetings) and reduced WT differs from the positive pattern observed for managerial PMS, suggesting that it is the clinical managers´ integration of PMS that is associated with more favourable WT outcomes.
The results reveal significant heterogeneity across PMS tools, suggesting that their impact on WT reduction depends on their strategic alignment. While comprehensive tools like the balanced scorecard—especially when combined with KPIs and management agreements—show a favourable association with efficiency by aligning clinical activity with organizational goals (; ), other measures such as employee satisfaction and reward systems appear less effective in this context. This discrepancy may stem from the rigid nature of public sector labour frameworks, where stable career tracks and a focus on equity over performance-linked compensation limit the perceived utility of incentive-based tools for clinical managers. Ultimately, these findings suggest that performance improvement in public hospitals is driven by strategic monitoring and managerial commitment rather than by traditional reward-based mechanisms.
The study also highlights the importance of adopting the managerial level of analysis. Prior research has generally examined PMS at the hospital or system level, often focusing on administrative executives. By contrast, this study directs attention to clinical unit managers, who operate at the interface between professional practice and organizational strategy. These physician-managers face unique challenges in balancing clinical and managerial responsibilities, and our evidence suggests that the PMS use can be decisive in achieving service efficiency. This contributes to a more nuanced understanding of PMS effectiveness across hierarchical levels in healthcare organizations.
This work presents a few limitations that also provide ample opportunities for future research. First, the empirical analysis is exploratory and descriptive. Although this approach is appropriate given the limited number of usable responses, it prevents the study from drawing confirmatory statistical or causal conclusions. The results should therefore be interpreted as preliminary evidence of possible patterns that future studies should test using larger samples and more robust empirical designs. Second, future studies should expand the sample to other regional health services, enabling a more comprehensive comparison across diverse geographical contexts and healthcare systems. This could help identify whether the observed patterns are consistent or specific to the region studied. Third, adding different types of uses could enhance the present findings about PMS. The importance lies not only in the implementation and formal recognition of each tool by managers, but also in how it is effectively utilized, i.e., enabling vs. coercive and diagnostic vs. interactive. Fourth, a limitation of this study is that PMS use is based on self-reported uses, which may not fully reflect actual implementation due to the absence of objective usage measures. In addition, the response rate raises the possibility of response bias, as clinical unit managers with stronger perceptions of the value of PMS may have been more likely to participate. Consequently, the results should therefore be interpreted with appropriate caution. Finally, the interest of a comparative study between the public and private sectors is unquestionable and would shed light on how collaboration or competition between them influences efficiency, quality, and access to healthcare, particularly in mixed systems. This is particularly relevant in a country like Spain, where the establishment of agreements between public and private healthcare is a matter of public interest, as it is often understood as a step toward the privatization of the public health system.
Future research should extend these findings in several directions. As the data were collected in 2015–2016, studies using more recent data are needed to assess whether changes in managerial practices and the evolution of PMS have influenced the observed relationships. In particular, comparing traditional activity-based indicators with the outcome measures promoted by the Value-Based Health Care (VBHC) model, while incorporating Patient-Reported Experience Measures (PREMs), could provide valuable insights into how performance measurement has evolved towards a more patient-centred approach. Furthermore, although this study identifies associations between PMS use and waiting-time performance, future research should examine the organizational and managerial mechanisms underlying these relationships using research designs capable of investigating causal pathways.
In terms of practical implications, the findings suggest that fostering clinical unit managers recognition of PMS tools may be relevant for service management, although further research is needed to determine whether such uses translate into measurable improvements in WT outcomes. Clinical managers achieve better unit outcomes when they perceive PMS as supportive of their work, which may only be possible through appropriate management training. Consequently, developing managerial competencies is essential for the effective leadership of clinical units. Because PMS use was measured through self-reported perceptions, responses may also be affected by social desirability bias or differences in managerial interpretation of the tools.
Conceptualization, M.B.G.S. and C.G.L.; Methodology, M.B.G.S. and C.G.L.; Data acquisition, M.B.G.S.; Analysis and interpretation, M.B.G.S., C.G.L. and F.R.S.; Writing- Preparation of the draft, M.B.G.S., C.G.L. and F.R.S.; Writing-Revision & Editing, M.B.G.S., C.G.L. and F.R.S. All authors read and agree with the published version of the manuscript.
1
Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. Journal of Applied Social Psychology, 32(4), 665–683. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x
2
Berbel-Vera, J., Gonzalez-Sanchez, M. B., Barrachina-Palanca, M., & Sánchez-García, J. (2025). The role of management control systems for digital transformation success. Review of Managerial Science, 20(8), 3031-3065. https://doi.org/10.1007/s11846-025-00961-3
3
Berberoğlu, A. (2018). Impact of organizational climate on organizational commitment and perceived organizational performance: Empirical evidence from public hospitals. BMC Health Services Research, 18, Article 399. https://doi.org/10.1186/s12913-018-3149-z
4
Bonomi Savignon, A., Costumato, L., Scalabrini, F., & Sanchietti, M. (2024). Towards performance governance in healthcare: An analysis of Italian local health units. The International Journal of Health Planning and Management, 39(6), 1819–1839. https://doi.org/10.1002/hpm.3844
5
Chenhall, R. H. (2003). Management control systems design within its organizational context: Findings from contingency-based research and directions for the future. Accounting, Organizations and Society, 28(2–3), 127–168. https://doi.org/10.1016/S0361-3682(01)00027-7
6
De Harlez, Y., & Malagueño, R. (2016). Examining the joint effects of strategic priorities, use of management control systems, and personal background on hospital performance. Management Accounting Research, 30, 2–17. https://doi.org/10.1016/j.mar.2015.11.002
7
De Vaus, D. (2013). Surveys in social research (6th ed.). Routledge. https://doi.org/10.4324/9780203519196
8
Decreto 104/2005, de 6 de mayo, de garantía de tiempos máximos de espera en la atención sanitaria. Diario Oficial de Galicia, núm. 90, de 11 de mayo de 2005, p. 7902. https://www.xunta.gal/dog/Publicados/2005/20050511/AnuncioE0E6_es.html
10
Fahy, J. (2002). A resource-based analysis of sustainable competitive advantage in a global environment. International Business Review, 11(1), 57–77. https://doi.org/10.1016/S0969-5931(01)00047-6
11
Fantahun, B., Dellie, E., Worku, N., & Debie, A. (2023). Organizational commitment and associated factors among health professionals working in public hospitals of southwestern Oromia, Ethiopia. BMC Health Services Research, 23(1), Article 180. https://doi.org/10.1186/s12913-023-09167-3
12
Ferreira, A., & Otley, D. (2009). The design and use of performance management systems: An extended framework for analysis. Management Accounting Research, 20(4), 263–282. https://doi.org/10.1016/j.mar.2009.07.003
13
Franco-Santos, M., Lucianetti, L., & Bourne, M. (2012). Contemporary performance measurement systems: A review of their consequences and a framework for research. Management Accounting Research, 23(2), 79–119. https://doi.org/10.1016/j.mar.2012.04.001
14
Franco-Santos, M., & Otley, D. (2018). Reviewing and theorizing the unintended consequences of performance management systems. International Journal of Management Reviews, 20(3), 696–730. https://doi.org/10.1111/ijmr.12183
15
Genrich, M., Angerer, P., Worringer, B., Gündel, H., Kröner, F., & Müller, A. (2022). Managers’ action-guiding mental models towards mental health-related organizational interventions—A systematic review of qualitative studies. International Journal of Environmental Research and Public Health, 19(19), Article 12610. https://doi.org/10.3390/ijerph191912610
16
Genrich, M., Worringer, B., Angerer, P., & Müller, A. (2020). Hospital medical and nursing managers’ perspectives on health-related work design interventions: A qualitative study. Frontiers in Psychology, 11, Article 869. https://doi.org/10.3389/fpsyg.2020.00869
17
Gonzalez-Sanchez, M. B., Broccardo, L., & Martin-Pires, A. (2018). The use and design of the balanced scorecard in the healthcare sector: A systematic literature review for Italy, Spain, and Portugal. The International Journal of Health Planning and Management, 33(1), 6–30. https://doi.org/10.1002/hpm.2415
18
Gutierrez-López, C., Barrachina-Palanca, M., & Gonzalez-Sanchez, M. B. (2025). Determinants of knowledge transfer performance in HEIs: A comparison between disciplines through management control tools. Tertiary Education and Management, 31, 45–63. https://doi.org/10.1007/s11233-025-09152-x
19
Iacobucci, D., Posavac, S. S., Kardes, F. R., Schneider, M. J., & Popovich, D. L. (2015). Toward a more nuanced understanding of the statistical properties of a median split. Journal of Consumer Psychology, 25(4), 652–665. https://doi.org/10.1016/j.jcps.2014.12.002
20
Instituto Galego de Estatística. (2019). Instituto Galego de Estatística. Recuperado el 25 de agosto de 2025 de https://www.ige.gal/web/index.jsp?idioma=es
21
22
23
Kurunmäki, L. (2004). A hybrid profession—The acquisition of management accounting expertise by medical professionals. Accounting, Organizations and Society, 29(3–4), 327–347. https://doi.org/10.1016/S0361-3682(02)00069-7
24
Lachmann, M., Trapp, R., & Wenger, F. (2016). Performance measurement and compensation practices in hospitals: An empirical analysis in consideration of ownership types. European Accounting Review, 25(4), 661–686. https://doi.org/10.1080/09638180.2014.994541
25
Malmi, T., & Brown, D. A. (2008). Management control systems as a package—Opportunities, challenges, and research directions. Management Accounting Research, 19(4), 287–300. https://doi.org/10.1016/j.mar.2008.09.003
26
OECD. (2020). Waiting times for health services: Next in line. OECD Publishing. https://doi.org/10.1787/242e3c8c-en
27
Peris-Ortiz, M., García-Hurtado, D., & Devece, C. (2019). Influence of the balanced scorecard on the science and innovation performance of Latin American universities. Knowledge Management Research & Practice, 17(4), 373–383. https://doi.org/10.1080/14778238.2019.1569488
28
Röttger, S., Maier, J., Krex-Brinkmann, L., Kowalski, J. T., Krick, A., Felfe, J., & Stein, M. (2017). Social cognitive aspects of the participation in workplace health promotion as revealed by the theory of planned behavior. Preventive Medicine, 105, 104–108. https://doi.org/10.1016/j.ypmed.2017.09.004
29
Sardi, A., Sorano, E., Tradori, V., & Ceruzzi, P. (2024). Performance measurement and critical success factors: A case study of a national health service. International Journal of Productivity and Performance Management, 73(11), 270–293. https://doi.org/10.1108/IJPPM-05-2023-0238
30
van Elten, H. J., & van der Kolk, B. (2025). Performance management, metric quality, and trust: Survey evidence from healthcare organizations. The British Accounting Review, 57(6), Article 101511. https://doi.org/10.1016/j.bar.2024.101511
31
van Elten, H. J., van der Kolk, B., & Sülz, S. (2019). Do different uses of performance measurement systems in hospitals yield different outcomes? Health Care Management Review, 46(3), 217–226. https://doi.org/10.1097/HMR.0000000000000261
32
World Bank. (2025). Current health expenditure per capita (current US$). Recuperado el 25 de agosto de 2025 de https://data.worldbank.org/indicator/SH.XPD.CHEX.PC.CD
... Strategic plan implemented and communicated throughout the organization
... Balanced scorecard: monitoring of strategic objectives and indicators related to 1) patients, 2) processes, 3) training, and 4) financials
... Cost accounting: cost per service (e.g., Cardiology)
... Cost accounting: cost per process (e.g., Appendectomy)
... Cost accounting: cost per patient (e.g., Mr./Ms. X’s appendicitis)
... Clinical sessions
... Teaching and research meetings
... Management Agreements
... Key structural indicators (e.g., material and human resources)
... Key process indicators (e.g., waiting list, average length of stay, occupancy rate)
... Key outcome indicators (e.g., discharges, mortality rate)
... Employee satisfaction measures
... Benchmarking: comparison of departmental results with those of other departments or institutions
... Incentive and reward systems based on results achieved
