A significant factor associated with the performance of local authorities is their financial management (Pawi et al., 2011). Since property tax collection contributes around 60% of the local authorities’ financial resources, it is essential to emphasize property tax performance (Daud et al., 2013). According to data.worldbank.org (2020), Malaysia’s property tax (part of the other taxes category) contributes about 2.3% of total country revenue, increasing at about 0.3% from the preceding year. Property tax performance is measured by various elements and numerous administrative practices, including property tax base, collection, compliance, assessment, exemption, billing, and enforcement (Nyabwengi and K’Akumu, 2019).

Previous research in Malaysia primarily discussed the issues of property tax arrears, property tax non-compliance, property tax appeal procedure, and general property tax performance (Atilola et al., 2017, 2019; Mohd et al., 2018; Sahari et al., 2020; Abdullah et al., 2022). This trend is similar in other regions, especially developing countries, which set fewer observable priorities for property tax assessment (Piracha and Moore, 2016; Jashari, 2020; Carrillo et al., 2021). Besides, research in property tax assessment is immature and limited to exploring the factors related to property tax reassessment. Prior property tax reassessment research used secondary data and was conducted qualitatively, leading to theoretical and methodological gaps (Ross and Mughan, 2018; Kim et al., 2020; Abd Rahman et al., 2021). In addition, the implementation of reassessment is still inferior in practice and should not be passed over where no revaluation has been undertaken for almost 35 years by the majority of Malaysian local authorities (Abd Rahman et al., 2021). Given these points, this study focused on the performance of property tax in the reassessment context.

This research examines the local authorities’ performance in the context of property tax reassessment. Reassessment is “the relisting and revaluing of all property within an assessment district after finding that the original assessment is too faulty for correction through the usual procedures of review and equalization” (International Association of Assessing Officers, 2013). Malaysian property tax reassessment must be conducted once every 5 years (Section 137[3], Act 171), and the new valuation list must be prepared upon reassessment work.

Whereas the existing research has rarely discussed property tax reassessment determinants, several authors have highlighted such factors in research on different contexts of property tax issues, as shown in Figure 1. The previous works can be divided into two main sub-fields: internal and external factors. The latter, which include socioeconomic conditions and political interference, are not easily controlled and lead to property tax uncertainty. Internal factors, however, are manageable and can be improved to maintain high-quality property tax reassessment performance.

To date, very few studies have examined the factors contributing to property tax reassessment activity (Senawi et al., 2022). In the literature, analyses of the determinants of property tax reassessment are primarily associated with the intangible resources of local authorities. Several reviews suggest that sufficient human resources, adequate property systems and software, good public relations and a competent assessor may contribute to the successful implementation of property tax reassessment. The current study emphasizes the association between the intangible assets of local authorities and their reassessment practices (see Figure 1). In addition, compared to the socioeconomic and political factors, intangible resources are easier to control and manage with regard to maintaining property tax performance. The latest evidence also indicates that internal determinants are more significant than external ones (Senawi et al., 2022). In addition, evidence found that one of the impediments to property tax reassessment is a lack of knowledge in preparing the paperwork for revaluation to be submitted to the state government (Abd Rahman et al., 2021).

Based on the finding on the significant relationship between intangible assets and property tax reassessment, this research is conducted to integrate IC as it is first needed to cater for this issue compared to other determinants. The knowledge asset is essential in property tax reassessment activity as a fundamental that can create value for this performance. Therefore, IC and its dimensions (human capital, structural capital and relational capital) are adopted to be the first to test their relationship with the property tax reassessment performance. The study expands the works by Senawi, Osmadi and Abd Rahman et al. (2022) to identify the actual impacts of IC on property tax reassessment performance. Using multi-scales for this study will ensure that the constructs are comprehensively measured.

IC has been extensively investigated in the literature, and different perspectives on its components have been adopted. The vast majority of the studies included in this review found three main elements of IC: human capital (HC), structural/organizational capital (SC) and relational capital (RC; Ferreira and Franco, 2017; Obeidat et al., 2017; Kamaluddin and Bakar, 2019; Xu et al., 2019; Liu et al., 2020; Masoomzadeh et al., 2020; Secundo et al., 2020). The public-sector dimensions of HC refer to aptitudes for pursuing target performances, a sense of ownership and motivations that look almost identical from a private-sector perspective (Manes Rossi et al., 2016). On the other hand, the author defined SC as the procedures and routines that support decision-making, achieving objectives and handling changes. Structural capital is also known as organizational capital or internal capital since the concept covers the internal features of an organization (Kamaruddin and Abeysekera, 2013; Wang et al., 2015). In addition, RC (also known as external capital) was defined as a combination of values, relationships and acts (Manes Rossi et al., 2016).

Furthermore, a closer look at the existing literature shows that staff levels of expertise, skills and education; the use of computer software; and the relationship with the public have become the key determinant factors of property tax reassessment performance (Cohen et al., 2020; Massawe, 2020; Abd Rahman et al., 2021), and these are closely linked to the concept of IC. Previous studies have also revealed that human resources is one of the main internal factors contributing to a successful property tax revaluation (Senawi et al., 2022). The human resources of an organization is a component of IC, specifically the HC dimension. Since HC has become dominant over the other elements of IC (Bontis et al., 2015), it has been suggested that IC has a positive association with the determinant factors of property tax reassessment.

On the other hand, property tax systems and software components are linked with SC characteristics. This interconnection is supported by evidence in the literature that SC comprises hardware, software, organizational structures, patents, trademarks and other factors that support or increase employee productivity (Bontis et al., 2015; Mahmood and Mubarik, 2020). The hardware and software highlighted here refer to computers, valuation systems, land records and any related items that valuation and property management staff may have and use to support property tax reassessment activities. Recent data indicate that property land records, inventories and computer software can lead to a successful reassessment process (Senawi et al., 2022). Therefore, a close positive relationship between SC and property tax reassessment has been demonstrated.

Besides, public relations and education factors are closely related to another IC dimension, RC. Close interaction between employees of an organization and its partners has become an indicator of RC (Liu and Jiang, 2020). In the context of property tax reassessment, a closer relationship between stakeholders, governments and taxpayers is needed to make the revaluation process successful. The stakeholders and high-level government will help in the decision to conduct property tax reassessment, whereas the taxpayer’s concern is to reduce the number of objection and appeals cases, which motivates the reassessment work. Massawe (2020) recommended that continuous focus is required to ensure a high degree of public awareness of the property tax reform objectives and procedures, where the main agenda is to reassess property tax value. Other findings in the literature suggest that one property tax reform strategy would be to introduce more and better taxpayer education to increase public understanding and acceptance of rising property tax bills (Slack and Bird, 2014). Hence, the existing situation proposed that RC positively impacts property tax reassessment performance.

In conclusion, all the property tax reassessment determinants are related to the IC components, which are classified into three main dimensions: HC, SC, and RC. Although the elements of IC in property tax reassessment performance have not been directly discussed, local authorities have embraced the concept. Hence, adopting IC and its components as substitutes for certain property tax reassessment determinants is undoubtedly realistic and logical. Therefore, the IC components were adopted as independent variables for this study setting.

The theoretical foundation of the RBV (Wernerfelt, 1984; Barney, 1991) confirms the impact of resources and capabilities on organizational performance. Therefore, all the IC dimensions (HC, SC, and RC) were considered in this study in order to determine whether they positively impacted property tax reassessment performance. Despite the limited number of studies that have attempted to ascertain the impacts of HC, SC, and RC on property tax reassessment, sufficient empirical evidence supports the hypothesis that HC, SC, and RC have positive and significant effects on organizational performance.

Researchers have discovered that HC is an essential form of IC, and it is argued that the other two dimensions (SC and RC) originate from it (Wang et al., 2015; Farah and Abouzeid, 2017). The findings of a recent report describe how HC is positively related to organizational performance in the public sector (Kamaruddin and Abeysekera, 2014; Farah and Abouzeid, 2017; Busenan et al., 2018). Furthermore, HC also positively impacts performance in other sectors and companies, such as small and medium-sized enterprises (SMEs), manufacturing, pharmaceuticals and publicly listed companies (McDowell et al., 2018; Kweh et al., 2019; Ahmed et al., 2020; Shahwan and Fathalla, 2020). In the property tax reassessment context, research indicates that adequate and experienced human capital contributes to successful reassessment implementation (Daud et al., 2013; Kuusaana, 2015; Propheter, 2016; Eom et al., 2017; Atilola et al., 2019; Senawi et al., 2022). An organization can benefit from human capital resources through the human knowledge and experience available to it during its operations, eventually contributing to property tax reassessment performance success and enhancement.

In addition, research indicates that SC positively impacts organizational performance. Researchers have implied that SC positively impacts organizational performance in the public and private sectors (Kamaruddin and Abeysekera, 2014; McDowell et al., 2018; Ahmed et al., 2020). Adequate local authorities resources such as high-quality land records, property mapping and inventories, as well as computer software and hardware contribute to higher performance in property tax revaluation practices (Dornfest, 2008; Babawale, 2013; Dimopoulos and Moulas, 2016; Nyabwengi and K’Akumu, 2019; Awasthi et al., 2020; Massawe, 2020). The resources available in the organizations, such as up-to-date systems, and stable procedures and policies, can help them work successfully, leading to better performance in reassessment work.

Meanwhile, past research indicates that RC significantly affects organizational performance (Kamaluddin and Bakar, 2019; Yusoff et al., 2019; Masoomzadeh et al., 2020). These findings suggest that RC is also an essential contributor to property tax reassessment since it is linked to creating relationships with the public to educate them on the importance of activities that return benefits to them (Dornfest, 2008; Kuusaana, 2015; Prabhakar, 2016; Mishra et al., 2020). The relationship building with the public, government, stakeholders and other institutions will motivate them in reassessment works. After all, the findings demonstrate that the multiple dimensions of IC significantly affect an organization’s overall performance. Therefore, the authors suggested three hypotheses concerning the positive relationships between the components of IC and the property tax reassessment performance of local authorities. Hence, the connections between the IC components and the performance of property tax reassessment were hypothesized as follows;

The interrelationship between the dimensions of IC has few interpretations. Recent findings indicate that the interaction between these components leads to several outcomes for an organization, like improved financial performance in the hotel industry (Sardo et al., 2018) and enhanced entrepreneurial opportunity recognition among SMEs (Rahman et al., 2022). Several studies suggest that HC needs a support system to leverage its effect on performance, and such a system is provided through SC and RC (Singla et al., 2022). Prior literature suggests that HC endorses the organizational component, an essential aspect of SC (Sardo et al., 2018). Bontis (1998) argued that without SC, IC would just be HC, and HC strongly influences SC. The employee’s competency, experience, skills and commitment will shape the organization’s systems, procedures, operations and policies, affecting property tax reassessment performance.

In addition, Singla et al. (2022) argued that RC resulted from better HC in a project performance context. Similarly, Rahman et al. (2022) examined the relationships that both HC and SC had with RC, finding these to be positive and significant in the context of employees in Omani SMEs. Rahman et al. (2022) demonstrated that HC strongly influences SC and that SC shapes the RC of SMEs in the Sultanate of Oman. Therefore, it is proposed that, with high competency and experience level, the local authorities officers influence the organizational relationship with others and actively change knowledge, leading to high property tax reassessment performance. Likewise, the stable local authorities’ procedures, operations and policies affect their relationship development with other parties such as the public, stakeholders, government, universities and private institutions. The strong relational building will enhance the performance of property tax reassessment.

In conclusion, the knowledge, expertise and skills gained by valuation staff help them to invest in information technology, good property databases and organizational development. RC is a source of successful networking, requiring knowledge, skills, expertise, a stable organization structure and state-of-the-art databases. Hence, RC was expected to enhance property tax reassessment performance by combining human and structural capital. Thus, the following hypotheses were formulated:

Self-administered and electronic questionnaires were used to collect data to suit the research situation and limitations. Back translation of the initial questionnaires between English and Malay was performed to ensure the accuracy and clarity of the items. Subsequently, the authors pre-tested the questionnaire using the expert judgement of academicians and practitioners in real estate and social science research. The process continued with cognitive interviews with the target respondents to establish the validity and reliability of the questionnaire. The authors arrived at the final version of the questionnaire using the pre-testing feedback to add and subtract items.

Based on the purposive sampling method, data were collected from West Malaysian local authorities, specifically from valuation officers of grades W29 to W54 in the valuation and property management departments. The W29 to W54 officers combine support, management, and professional staff who acknowledge having a high judgement on property tax matters. W29 officers are the lowest grade positioned as the head of the valuation and property management department in West Malaysian Local Authorities, whereas W54 officers are the highest. Due to different legislation and time constraints, the compiled data only reflected Peninsular Malaysia, excluding Sabah, Sarawak and the Federal Territories of Labuan. The exclusion of East Malaysia will slightly limit the research in geographical aspects.

For the final data collection, 216 questionnaires were distributed among all the local authorities in West Malaysia, and 157 questionnaires were returned for analysis. However, only 155 questionnaires were usable due to duplicate responses, as respondents answered in hard and electronic copies. The samples comprised 79 female (51%) and 76 male (49%) respondents, thus representing the genders almost equally. The statistics based on their years of service were obtained using five categories: <5 years (n = 30, 19%), 5–10 years (n = 38, 25%), 10–15 years (n = 39, 25%), 15–20 years (n = 18, 12%) and > 20 years (n = 30, 19%). Most respondents were of grades W29 to W36 (n = 118, 76%). Meanwhile, 37 (24%) respondents were of grades W41 to W54. These statistics also indicated that most of the respondents come from support staff.

The IC dimensions (human capital, structural capital and relational capital) were examined as independent variables of property tax reassessment performance (the dependent variable). The item measurements were developed in three steps, as in Table 1: literature review, expert judgement, and cognitive interview. The rationale for these stages is to establish content validity and reliability. During the first step, the measurements of IC were adapted from those previously used in validated research in a public organization: human capital had 25 items, structural capital had 17 items, and relational capital had 18 items (Cohen and Vlismas, 2013;Fazlagic and Szczepankiewicz, 2018; Ramírez et al., 2020). The measurement for property tax reassessment performance was developed based on previous property tax research, with five items used. The questionnaire also involves eight items for the demographic profile section.

After the expert judgement stage by 10 experts (7 academicians and 3 practitioners) in real estate and social science research, 25 items were subtracted, and four items were added. The last step during pre-testing using a cognitive interview with six actual respondents increases the total number of items to 53. Hence, the final items for the survey used 12 items for human capital (HC), 11 for structural capital (SC), 12 for relational capital (RC), 10 for property tax reassessment performance (PTRP) and eight for the demographic profile. All independent constructs use reflective indicators, as most recent literature suggests (Barrutia and Echebarria, 2021; Rahman et al., 2022; Singla et al., 2022). On the other hand, the dependent variable, PTRP, used the reflective-formative measurement model type. In this model, the lower-order constructs are reflectively measured constructs that do not share a common cause but rather form a general concept that fully mediates the influence on subsequent endogenous variables (Chin, 1998). The rationale for doing this is that the PTRP construct has two dimensions – implementation and effectiveness.

The reflective indicators of HC consist of qualifications, experience, training, skills, loyalty, creativity, motivation, and satisfaction (Cohen and Vlismas, 2013; Fazlagic and Szczepankiewicz, 2018). Besides, the items for SC subsist technical equipment, an up-to-date database, organizational structure and internal process adaptation, organizational culture, and promotional tools (Fazlagic and Szczepankiewicz, 2018; Ramírez et al., 2020). In addition, RC indicators are expressed by relations with the public, with other clients, with other public institutions, with suppliers, and with universities and images (Fazlagic and Szczepankiewicz, 2018). Lastly, PTRP indicators involve implementation and effectiveness aspects, where implementation elements include the occurrence and frequency of reassessment and the preparation to conduct a reassessment (Local Government Act, 1976; Eom et al., 2017). The effectiveness aspect incorporates the equity and uniformity of assessment, act compliance, tax collection impact, cost and tax burden (Local Government Act, 1976; Mehta and Giertz, 1996; Cornia and Walters, 2005; Eom et al., 2017; Asongu et al., 2021). In the questionnaire, the respondents were told to indicate their level of agreement with certain given statements to obtain feedback on the constructs of the research framework. Five-point and seven-point Likert-type scales from 1 (strongly disagree) to either 5 or 7 (strongly agree) were used to measure the constructs of the variables employed. Different Likert scales can be used to minimize bias in research.

Common method variance (CMV) might have been a concern in this study because the independent and dependent variables were collected simultaneously from the same respondents. Following the approaches used in previous studies, both procedural and statistical remedies were performed to assess the existence of CMV. For the procedural remedies, the recommendations of Podsakoff et al. (2012) were followed, so the authors adopted a single common-method-factor approach to controlling for CMV. First, we selected four items developed by Fugate et al. (2009) that were collected in the same survey but not included in the tested model. These were used as marker indicators. Second, a method factor was created using the marker indicators as an exogenous variable to predict each endogenous construct in the model. Finally, we compared the method factor model with the baseline model and found that the significant paths in the baseline model remained significant in the method factor model.

Next, we tested the CMV using statistical remedies, as suggested by Kock and Lynn (2012) and Kock (2015), to test full collinearity. Using this method, all the variables are regressed against a common variable, and if the VIF ≤ 3.3, no bias arises from the single-source data. The analysis yielded a variance inflation factor (VIF) of less than 3.3, as shown in Table 2 below. Thus, single-source bias was not a severe issue with our data. Along this line, it was concluded that the data had no CMV issues.

We followed the suggestions of Anderson and Gerbing (1988) to test the developed model using a two-step approach. First, we tested the measurement model to assess the validity and reliability of the instruments used, following the guidelines of Hair et al. (2019) and Ramayah et al. (2018). Then, we ran the structural model to test our hypotheses.

For the measurement model, we assessed the loadings, average variance extracted (AVE) and composite reliability (CR). Loading values should be ≥0.5, AVE values should be ≥0.5 and CR values should be ≥0.7. As shown in Table 3, the AVE values were all higher than 0.5 and the CR values were all higher than 0.7. The loadings were also acceptable, with only 12 loadings less than 0.708 (Hair et al., 2019). For the formative second-order construct, Table 4 shows that the VIF values for implementation and effectiveness were all below the 3.33 threshold (Diamantopoulos and Siguaw, 2006). The results, therefore, did not indicate a multicollinearity problem. The two measurement models were recorded for the PTRP construct since it is a higher-order model, and the analysis used the disjoint two-stage approach. The selection for this approach is that this method shows a better parameter recovery of paths compared to the (extended) repeated indicator approach (Sarstedt et al., 2019).

In step 2, we assessed the discriminant validity using the HTMT criterion suggested by Henseler et al. (2015) and updated by Franke and Sarstedt (2019). The HTMT values should be ≤0.85, and the stricter criterion and the mode lenient criterion should be ≤0.90. As shown in Table 5, the HTMT values were all lower than the stricter criterion threshold of ≤0.85. We could therefore conclude that the respondents understood that the five constructs were distinct. These validity tests showed that the measurement items were valid and reliable.

As Hair et al. (2017) and Cain et al. (2017) suggested, we assessed the multivariate skewness and kurtosis. The results showed that the collected data were multivariate normal for only Mardia’s multivariate skewness (β = 1.844, p < 0.001) but not for Mardia’s multivariate kurtosis (β = 26.509, p < 0.05). Thus, following the suggestion of Hair et al. (2019), we reported the path coefficients, standard errors, t-values, and p-values for the structural model using a 5,000-sample re-sample bootstrapping procedure developed by Ramayah et al. (2018). Moreover, based on the criticism of Hahn and Ang (2017) that p-values are not a good criterion for testing the significance of the hypothesis, the use of a combination of criteria—such as p-values, confidence intervals and effect sizes—was suggested. Table 6 summarizes the criteria used to test the research hypotheses.

First, we tested the effect of the three predictors on PTRP, and the R² was 0.182 (Q² = 0.052), which showed that all three predictors explained 18.2% of the variance in PTRP. HC (β = 0.016, p = 0.444), SC (β = 0.095, p = 0.182) and RC (β = 0.353, p < 0.001) were all positively related to PTRP, with only RC significant, so only H3 was supported, and H1 and H2 were rejected. Next, we tested the effect of HC on SC, and the R² was 0.379 (Q² = 0.364), which indicates that HC explained 37.9% of the variance in SC. The relationship of HC with SC is significant with β = 0.616, p < 0.001, thus supporting H4. Lastly, we tested the effects of HC and SC on RC, with the R² of 0.462 (Q² = 0.379) indicating that HC and SC explained 46.2% of the RC variance. The findings indicate that HC and SC influence RC significantly with β = 0.423, p < 0.001 and β = 0.331, p < 0.001, respectively, thus supporting H5 and H6.

Besides, rather than we reported the statistical significance (p-values), substantive significance (effect size) was also disclosed. Referring to Hair et al. (2019), f² explain how the removal of a certain predictor construct affects an endogenous construct’s R² value. As a rule of thumb, values higher than 0.02, 0.15 and 0.35 depict small, medium and large f² effect sizes (Cohen, 1988). From Table 6, RC indicates a small effect in producing the R² for PTRP. However, HC and SC do not predict the property tax reassessment performance. In addition, the results indicate that HC has a substantial effect in producing the R² for SC. Moreover, the results show that HC has a moderate effect compared to SC, with a small effect in producing the R² for RC.

Finally, we assessed the predictive relevance of the model through the blindfolding procedure. The predictive sample reuse technique, popularly known as Stone–Geisser’s Q², can be applied as a criterion for predictive relevance. Henseler et al. (2009) accentuated this measure to assess the research model’s predictability. Based on the blindfolding procedure, the results indicate that the Q² values for PTRP (Q² = 0.052), SC (Q² = 0.364), and RC (Q² = 0.379) are more than 0, suggesting that the model has sufficient predictive relevance. For reference, values of 0.02, 0.15, and 0.35 indicate that an exogenous construct has a small, medium or large predictive relevance for a particular endogenous construct (Hair et al., 2017).

The research model adds theoretical value to the discourse of organizational psychology, knowledge management, and property tax reassessment management. Firstly, the theory of organizational psychology was added by highlighting local authorities’ attitude to succeed in property tax reassessment from a knowledge assets perspective. The study addressed the vital part of the literature gap by considering the aspect of organizational behavior in conducting reassessment work which reveals that various stakeholders (managers, staff, public, government, universities, and suppliers) are significantly involved in this performance. This aspect will add value to the psychology theory among local authority managers in implementing property tax policies. Alongside all the stakeholders, managers need to focus on other resources in local authorities: their human resources and organizational capital.

Second, this research significantly contributes to the body of knowledge and enriches understanding of the role of human, relational and structural capital in improving property tax reassessment performance. The study contributes to the theory of RBV by identifying the intangible resources needed for a property tax reassessment and how it could work. Even though IC research is not entirely new, it is diverse when it relates to IC in the context of local authorities’ property tax reassessment performance. To the best of our knowledge, this study is the first to reveal the relationships between the components of IC and organizational performance in the property tax reassessment context. The findings also confirm the interlinkages between these intangible assets and their direct and indirect impacts on property tax reassessment performance. The authors conclude that undertaking revaluation activities may be useless without the integrated support of HC, SC, and RC. The study also confirms the indirect effects of HC and SC on property tax reassessment performance.

Lastly, this study focused on the success factors related to property tax assessment, which are rarely studied in the literature. The study contributes to the theory by providing a conceptual framework of property tax management that examines the performance of property tax reassessment through the lenses of knowledge assets. Despite the other factors, intangible assets (HC, SC, and RC) confirmed having a significant impact and became one of the determinants in this performance. The local authorities need to consider these aspects to be successful in property tax reassessment. Therefore, the study provides valuable information for researchers, academics and local authorities in IC and property tax research.

The research framework provides a roadmap for managers and stakeholders in local government to understand the workable links between IC and its dimensions in the context of property tax reassessment. Local authorities may add value to the property tax assessment aspect of their property tax policies, which may also help to reflect their knowledge of assets, actual property tax assessment quality, and overall property tax performance. The performance of a property tax reassessment greatly depends on building relationships such as a good affair with the public, government, universities, other agencies and suppliers, as well as actively exchanging knowledge. Therefore, it is in the best interests of external parties and local authorities to maintain healthy work relations.

A property tax reassessment activity involves the public and a higher level of government. The public and the local authorities must have a smooth relationship. Maintaining healthy relations with the public and with government agencies, understanding their requirements, and building trust are essential for implementing a property tax reassessment. At the same time, the local authorities must ensure a healthy working relationship among their staff and a stable organizational structure. As suggested, the benefits of relationship building cannot be achieved without developing HC and SC. Local authorities must establish people and organizations that focus on relationship building so that the knowledge and skills of the valuation staff, as well as the institution’s systems and procedures, can be used, which would ideally result in successful reassessment activities. The strategy must revolve around developing RC and using these resources to leverage the effects of HC and SC on property tax reassessment performance.

The authors developed a model explaining the interrelationships of IC and its impact on property tax reassessment performance. This probe appears to be limited in its scope to design a conceptual framework based on the literature gaps and theoretical backdrops. The other limitations of this study are question order and acquiescence bias, as there is no randomization of questions, and all items are positive. Future research into the proposed IC and property tax reassessment model could empirically explore, examine and validate the model presented in this study in different industry sectors and economies. Different social settings are required to establish and increase the generalisability of the findings and confirm or refute the theoretical relationships between the concepts.