Conference Agenda

Public and private organizations differ in ownership and control, presumably influencing citizens’ perceptions of service delivery. Public administration research has extensively examined ownership as a contributor to public sector bias, yielding mixed findings (Marvel, 2016; Meier et al., 2022). The role of control, from monopolistic to competitive settings, has largely been overlooked (Xu & Li, 2022). Thus, drawing on the theory of motivated reasoning, we investigate how control and ownership combined influence citizens' perceptions of service delivery. We hypothesize that citizens perceive public services delivered by public organizations as less productive but more aligned with normative performance aspects. We propose that competitive settings reduce public sector bias regarding productivity-related and normative performance aspects, whereas monopolistic settings increase it. We will develop a 3×3 factorial survey experiment and aim for a sample of n = 1,548 German citizens, representative of age, gender, and income. The design will examine how citizens, as service users, perceive these performance aspects for public and private providers under monopolistic and competitive conditions, presenting a heating energy delivery scenario where they receive district heating with no choice options (monopoly) or gas with choice options (competition). Our research expands our understanding of public sector bias by introducing control as a central dimension. The findings offer practical insights for public managers, providing strategies to address biases, enhance service delivery, and justify marketization or de-marketization policies.

In today's complex environment, public organizations face increasing challenges to maintain quality that aligns with the needs of all stakeholders, including customers, employees, and partners. Total Quality Management (TQM) is a management philosophy aimed at improving organizational efficiency and achieving organizational goals, widely recognized in the literature for obtaining excellent results. A specific tool developed for the public sector is the Common Assessment Framework (CAF) (EUPAN, 2019), adapted by public sector organizations from the European Foundation for Quality Management (EFQM) model, which is extensively used in the private sector. While past literature uses the EFQM model as a research framework to explore the relationship between management practices and organizational results in private sector contexts, there is limited research on the effects of total quality management on public sector excellence. Previous studies in the private sector highlight the role of EFQM management practices, i.e., enablers, in fostering organizational performance (Kafetzopoulos and Gotzamani, 2018; Bou-Llusar et al., 2005), emphasizing the need for comprehensive examination within the public sector as well. Excellence models like EFQM and CAF state the causal relationship between enabling management practices and organizational results. As previous cross-case qualitative studies in the public sector supported only correlation (Raharjo et al., 2015), and correlation does not imply causation, there is a need to further investigate the relationship between each enabler and result.

Furthermore, as focusing on isolated management areas is not sufficient to reach excellence (Eskildsen et al., 2000), there is the need to examine the synergies and interdependencies among the five postulated management practices. To guide the implementation of the model in public sector organizations, quality management research should examine the stated core role of leadership in driving the path towards excellence as well as the interdependencies among the three other management factors: people, strategy and planning, partnerships and resources. Empirical research on the EFQM highlights the core mediating role of process management to research excellent results, which needs to be further examined in the public sector context as well.

Therefore, this study aims to fill these three key underlying problems and research gaps, by considering the CAF as a starting research framework to investigate the public sector. Specifically, the research aims to:

(a) Examine the causal relationship between management practices and organizational performance

(b) Analyze the mediating role of process management in this relationship

(c) Determine the connections and interactions between the five core management practices

To answer these questions, the study draws on the TQM principles and the EFQM model. The examination of management practices and organizational results will be informed by the analysis of the relationships within the categories of the EFQM (Heras-Saizarbitoria et al., 2012). The research framework incorporates theories of leadership, human resource management, strategic planning, partnership management, and process management, emphasizing their roles in achieving organizational excellence. All these theories are considered within a unique research framework, developed based on the structure of the CAF.

Based on the above research model, the following hypotheses can be formulated:

H1: Leadership has a positive effect on People.

H2: Leadership has a positive effect on Strategy and Planning.

H3: Leadership has a positive effect on Partnership and Resources.

H4: Strategy and Planning have a positive effect on (a) People and (b) Partnership and Resources.

H5: Processes mediate the positive effect of (a) People on People results, (b) Strategy and Planning on Citizen Results, and (c) Partnerships and Resources on Social Responsibility Results.

H6: People Results have a positive effect on Key Performance Results.

H7: Customer Results have a positive impact on Key Performance Results.

H8: Society Results have a positive impact on Key Performance Results.

To empirically test the following hypotheses, the study employs a quantitative survey design approach. Comparative data are collected from a sample of European public sector organizations across the 27 EU countries, with the assistance of CAF national correspondents, EIPA, and available CAF-users databases. To manage potential biases from respondents familiar with the CAF structure, the survey targets both CAF-users and non-CAF-users, considering any significant deviations in the responses of these two groups. The online quantitative questionnaire, administered via an online tool, focuses on operationalizing and measuring nine one-factorial constructs corresponding to the CAF criteria, and gathering socio-demographic data of respondents. Data will be analyzed using structural equation modelling, with the relevance and novelty of this approach in quality management studies to understand complex causal relationships highlighted in the study by Magno et al. (2024).

Previous research in the private sector has shown a positive relationship between enablers and results, with process management playing a key mediating role (Calvo-Mora et al., 2014). These studies highlighted a causal relationship between management practices and organizational results when considered collectively (Bou-Llusar et al., 2005; Calvo-Mora et al., 2014), suggesting positive impacts on individual outcomes as well. In public organizations, leadership is expected to significantly influence strategy and planning, human resources, partnerships, resources, and processes (Anastasiadou & Zirinoglou, 2015). Furthermore, interactions among enablers indicate that strategy positively impacts human resources, partnerships, and processes. Regarding the potential challenges, the primary task is ensuring the reliability and validity of the questionnaire and the operationalized constructs. This will be accomplished by consulting field experts and utilizing peer validation. Another challenge is that the questionnaire is in English, which could reduce participation rates in some countries, although it facilitates comparable cross-country results. In such cases, translating the questionnaire into languages the author is proficient in and focusing the study on a cross-country perspective may be considered. Lastly, analyzing the interdependencies among constructs requires robust statistical techniques and careful interpretation of results. To address this complexity, the author is attending various statistical courses and relying on expertise from different fields.