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Determinants of Accrual Basis International Public Sector Accounting Standards’ (IPSASs) Implementation in Nigeria

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Contemporary Journal of Economics and Finance

Volume 1, Issue 2, March 2023, pages 27-48

Determinants of Accrual Basis International Public Sector Accounting Standards’ (IPSASs) Implementation in Nigeria

DOI:
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Ahmed Aliyu Abdullahi1, Abdullahi Nasiru2, Ibrahim Yusuf1

1 Department Department of Accounting, ABU Business School, Ahmadu Bello University, Zaria, Nigeria
2 Department of Business Administration, ABU Business School, Ahmadu Bello University, Zaria, Nigeria

Abstract: Studies have documented findings with regard to the factors that affect IPSASs implementation which may not be generalisable to other countries due to jurisdictional, cultural and contextual differences. In Nigeria, few studies have been conducted on the issue of IPSASs implementation and most of the studies are theoretical and focused on the benefits of IPSASs implementation rather than the determinants. This study therefore investigated the determinants of accrual basis IPSASs implementation in the Nigerian Federal Government Ministries (using Abuja as the study area) with the view to further provide empirical evidence on the determinants. To achieve this, a survey of the federal ministries was conducted to collect data on perceptions of accountants, internal auditors, and budget officers on the determinants of accrual basis IPSASs implementation in Nigeria. The population of the study comprised the 656 accounting staff of all the 24 Federal Government Ministries in Nigeria. The sample size was 339 accounting staff selected using proportionate stratified random sampling techniques.  Data collected were obtained from primary sources through the use of Questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).  The study found that political will, Infrastructure, manpower, and Culture Change were all positive and significantly related to accrual basis IPSASs implementation in Nigeria. While, collaboration and legislation are positive but insignificantly related to accrual basis IPSASs implementation in Nigeria. Coordination is the only factor that was negative but significantly related to accrual basis IPSASs implementation in Nigeria. It was therefore concluded that political will, infrastructure, manpower, and culture change have impacted on accrual basis IPSASs implementation in Nigeria. However, collaboration, coordination and legislation have not impacted on the accrual basis IPSASs implementation in Nigeria. The study recommends for an increased involvement of executives and legislatives, active participation, and provision of adequate funds to facilitate the implementation process.  Again, government should provide the additional needed skilled personnel to handle the accrual basis IPSASs implementation and consider the benefits of Collaborating with other governments who are successful in IPSASs implementation. Finally, the study also recommends that government should provide additional efforts or be more proactive in providing the necessary Infrastructure for the implementation of accrual basis IPSASs in the federal government ministries.

Keywords: IPSASs Implementation, Federal Ministries, IPSAS, Determinants, Nigeria

1. Introduction

Nigeria made a pronouncement for the adoption of IPSASs since 2010. The roadmap for its adoption and implementation was one that necessitated a move by public sector entities to first of all migrate to the use of cash-based IPSAS for the preparation of accounts by 2014 and to later on migrate fully to accrual-based IPSASs by 2016. But some ministries are still in the process of adopting the standard and those who have started implementing accrual basis IPSASs, did not implement the full requirements of the standards (Idris, 2016).

The decision to adopt IPSASs in 2010 by government, mark the beginning of a shift in public sector financial reporting in Nigeria. The quest for the adoption of a uniform government accounting standards provides motivation for this study. First, the revelation of non-implementation or partial implementation across ministries in Nigeria provides the need to study the factors that influence/slow down the implementation. Second, few academic research have studied factors influencing the adoption of IPSASs in Nigeria, in particular, and Africa, in general.

The empirical results of the world-wide researches have however looked at or examined varied perceived factors that influence IPSASs implementation (Nobes, 1998; Jaggi & Low, 2000; Chan, 2006; Zeghal & Mhedhbi, 2006). Prior literature argued that the attributes documented as having significant effect on the adoption of IPSASs in one country may not be generalisable to other countries. This is so because a country’s financial reporting system is affected by the local environment and tends to reflect cultural, economic, professional and contextual differences, and institutional pressures (Nobes, 1998; Jaggi & Low, 2000; Zeghal & Mhedhbi, 2006; Kossentini and Othman, 2011; Shima & Yang, 2012; Hamisi, 2012; Phan, 2014) among others.

1.1 Problem Statement

In Nigeria which is the focus of the current study, few studies have been conducted on the issue of IPSASs implementation. Some of the few studies include Obazee (2011),  Yerima (2012), Madawaki (2012), Baba (2013), Bello (2013), Acho (2014), Bello (2014), Otunla (2014), Nongo (2014), Ijeoma and Oghoghomeh (2014), Isa (2014), Ofoegbu (2014), Francis and Samuel (2015), Nkwagu, Okoye and Nkwagu (2016), Felix (2016). Most of these studies however, were literature based, analytical with the aid of content analysis or conceptual studies with little or no empirical evidence, focused on the relationship between IPSASs implementation and the anticipated benefits on one hand, and IPSASs adoption and familiarity and credibility on the other hand. Given the paucity of consistent research efforts on the topic in Nigerian literature as well as the assertions made by some scholars (Nobes, 1998; Jaggi & Low, 2000; Zeghal & Mhedhbi, 2006; Aggestam, 2010; Kossentini and Othman, 2011; Shima & Yang, 2012; Hamisi, 2012; Phan, 2014) that determinants documented for other countries may not be generalizable to other countries, the present study strives to provide further empirical evidence on the determinants in Nigeria and to bring ongoing IPSASs implementation in Nigeria into the international arena. Hence, perception was sought with regards to political will, infrastructure, manpower, collaboration, coordination, legislation and culture change and the extent to which each of them affect the successful implementation of IPSASs in the Nigerian federal government ministries.

1.2 Aim of the Study

The main objective of this study is to examine the determinants of Accrual basis IPSASs implementation in Nigerian federal government ministries. Specifically, the study investigates the effect of political will, infrastructure, manpower, collaboration, coordination, legislation and culture change on IPSAS implementation in Nigeria. Based on the review and in line with the underpinning theories, the study hypothesized after the review of each independent and dependent variables.

A part from the introduction, the remainder of the paper is organized as follows. Section two reviews relevant literature relating to accrual basis IPSASs implementation and development of hypotheses of the study, Section three deals with methodological issues of the paper, Section four analyzed the data, interprete and discusses findings of the study, Section five concludes the study and proffers recommendations.

2. Literature Review

This section focuses on the review of relevant and related literatures on the subject matter. Thus, relevant empirical literatures on the determinants of IPSAS implementation were reviewed in order to facilitate the readers’ thorough understanding of previous study in this field and the theories underpinning the study.

2.1 IPSAS Implementation Determinants Literature and Hypotheses Development

The implementation of accrual basis IPSASs in both developed and developing countries has attracted great attention from accounting researchers and is influenced by many factors. Prior studies such as Zeghal and Mhedhbi (2006), Abd-Elsalam and Weetman (2007), Ouda (2008), Caba-Perez, Lopez-Hernandez and Ortiz-Rodriguez (2009), Ouda (2010), Aggestam, (2010), Masoud (2010), Eriotis, Stamatiadis and Vasitious (2011), Lande and Rocher (2011), Shima and Yang (2012), Hamisi (2012), Brusca, Montesinos and Chow (2013), Guerra de Sousa, Fernandes de Vasconcelos, Caneca and Niyama (2013), Aidoo-Buameh (2014), Al-zubi (2015), Brusca and Martinez (2015), Jones and Caruana (2015), Tanjeh (2016), Agyemang and Yensu (2017a), Agyemang and Yensu (2017b) have identified Seven (7) key factors that affect accrual basis IPSASs implementation (IPSASIMPL) namely: Political will (POLWILL), Infrastructure (INFRAST), Manpower (MANPOW), Collaboration (COLLABO), Coordination (COORD), Legislation (LEGISL) and Culture changes (CULCHAN). In terms of this study, the attributes that are important in the IPSASs implementation in Nigerian environment may differ from those of other countries. Hence, the key factors were discussed below:

2.1.1 Political Will                                                                                                                

Political will and support are considered to be instrumental to gain acceptance of the possible benefits of accrual accounting by all levels of government (Ouda, 2004). Again, political support in terms of active participation of both the executives and legislatures as well as provision of adequate infrastructure is critical to the successful acceptance of IPSASs (Tanjeh, 2016). Rakoto (2008) also argued that political commitment is essential for accounting reforms to become a national priority and gain funding from the international community. Similarly, prior studies argued that the success of government accounting standards adoption mainly depends on political and management support (Ball, 2012; Oulasvirta, 2012; Chan, 2006; Aidoo-Buameh, 2014). Hamisi (2012) reported a positively correlated and significant relationship between political will and IPSAS implementation.

H01: There is no significant positive relationship between political will and accrual basis IPSASs implementation in Nigeria.

2.1.2 Infrastructural Facility

Infrastructural facility is also one of the important factors which can influence the successful implementation of IPSASs in most of the developing countries. Prior literatures argued that introduction of a new standard require expertise, new technology or amending the existing information technology, cultural changes, co-operation, incentives and penalties and standard software (Hepworth, 2003; Lande & Rocher, 2011). Joshi, Bremser and Al-Ajimi (2008) argued that the development of infrastructure impacted positively and significantly on the development and implementation of single set of global accounting standards. Hamisi (2012), Tanjeh (2016) reported also reported positive and significant relationship between Infrastructure and IPSAS implementation.

H02: There is no significant positive relationship between Infrastructure and accrual basis IPSASs implementation in Nigeria.

2.1.3 Manpower

Manpower is considered key to successful implementation of accrual basis IPSASs. As reported by Chan (2006:35), lack of technical personnel imposes a severe constraint to IPSASs implementation, thus human resources are obstacle to overcome in government accounting reform.  Again, Ilie and Miose (2012) established that, policy change is effected by human beings and its success depends largely upon their active involvement in the change process. Eriotis, Stamatiadis, and Vasiliou (2011) argued that the level of accrual accounting adoption was positively related to Information Technology (IT) quality, reform related to training, education level of accounting staff, and professional consultants’ support.

H03: There is no significant positive relationship between manpower and accrual basis IPSASs implementation in Nigeria.

2.1.4 Collaboration

Collaboration between government entities and between the ministries, departments and agencies is an important factor to be considered during the implementation and even at post-implementation stages. Collaboration with donor agencies on implementation costs is equally important. Prior literatures argued that collaboration between the staff who will implement the IPSASs will also be of significance because that can provide information on the implementation which was not previously available to a particular ministry, it is therefore expected that effective collaboration will have positive impact on IPSASs implementation (Brusca, Montesinos & Chow 2013; Al-zubi, 2015; Cosimato, Torres & Troisi 2015; Adhikari & Garseth-Nesbakk 2016).

H04: There is no significant positive relationship between collaboration and accrual basis IPSASs implementation in Nigeria.

2.1.5 Coordination

Prior literatures such as Ouda (2008),  Rakoto (2008), Ouda (2010), Aggestam, (2010) have argued that, effective coordination is an important factor in determining IPSASs implementation and lack of effective coordination and communication between the civil servants and the accountants, led to the development of a standard which was not really understood, even by those responsible for its development and as a result, it was difficult to convince other stakeholders to support the process of accounting reform. However, Hamisi (2012) in his study of the factors affecting IPSASs implementation in Kenya documented a contrary view. The result of the study indicates a negative and significant relationship between IPSASs adoption and consultation and coordination.

H05: There is no significant positive relationship between coordination and accrual basis IPSASs implementation in Nigeria.

2.1.6 Legislation

Prior studies revealed the need for enactment of new laws or amendments of existing ones to manage conflicts between existing legislation and international standards provisions (Pina, Torres & Yetano, 2009; Ouda, 2010; Isa, 2014). Ouda (2008) reported a positively correlated and significant relationship between legal barriers/environment and transition to accrual accounting. Similarly, Cam-Van (2016), Agyemang and Yensu (2017a) documented a significant positive relationship between existing laws and accrual basis IPSAS implementation.

H06: There is no significant positive relationship between legislation and accrual basis IPSASs implementation in Nigeria.

2.1.7 Culture Change

In addition to adequate legal provision, culture has also been identified as an important factor for successful implementation of IPSASs. United Nations (1995) believed that a change in management culture towards output instead of input and the use of discretion by manager in allocation of resources could motivate manager to adopt new accounting system. Additionally, the use of efficiency and effectiveness as a measure of performance could also lead to demanding reliable financial information which will require new accounting system that satisfies managers need (United nation, 1995). Ouda (2008) revealed a positively correlated and significant relationship management culture and transition to accrual accounting. Furthermore, Zeghal and Mhedhbi (2006) investigated the factors affecting the adoption of international accounting standards (IASs) by developing countries. They documented that cultural membership is positively and significantly associated with the adoption of IASs. Similarly, Agyemang and Yensu (2017b) documented a significant positive relationship between cultural practices and accrual basis IPSASs implementation.

H07: There is no significant positive relationship between culture change and accrual basis IPSASs implementation in Nigeria.

2.2 Theoretical Framework of the Study

The study is premised on Institutional and diffusion of innovation theories. Institutional theory was first developed by Meyer & Rowan, (1977) and later expanded by DiMaggio & Powell (1983). According to this institutional theory, the process of adapting institutionally acceptable practices where organizations resemble each other both culturally and structurally is recognized as institutional isomorphism (Meyer & Rowan, 1977; DiMaggio & Powell, 1983). The theory explains that adoption of a new system in this case accounting practice by organization or country can be influenced by institutional isomorphism namely: coercive, normative or mimetic isomorphism. According to Zeghal and Mhedhbi, (2006); Hassan (2013) the choice of a particular accounting practice by an organization or country will to a large extent be affected by at least one of these three isomorphism. A country adopts a new system in order to meet up with the world best practice, pressure from other countries/from international organisations or as a result of a new legislation which made it mandatory for all public entities to comply. This is a clear example of coercive isomorphism. On the other hand, normative isomorphism explained the adoption of a new system through level of professionalism acquired by the implementers. The third and the last isomorphism that is mimetic isomorphism explain that countries within the same region are most likely to adopt the same accounting standards. This means that countries adopt new system from their counterparts who are successful in the adoption by way of imitation. Diffusion of Innovation theory is a theory that seeks to explain how, why and at what rate new ideas and technology spread through cultures. The theory was developed by French sociologist Gabriel Tarde in 1903 and Everett Rogers, a professor of rural sociology, popularized the theory in his 1962 book Diffusion of Innovations (Kaminski, 2011).

Rogers (1995) defines innovation as an idea, practice, or object that is perceived as new by individual or other unit of adoption and diffusion as “the process by which an innovation is communicated through certain channels over time among the members of a social system”. Overall the diffusion of innovation is defined as “the process by which an innovation is communicated through certain channels over time among members of the social system” (Rogers, 1995:5).

The theory specified and explained the five attributes of innovations that are perceived by the members of the social system to highly determine its rate of adoption, and defined the relationship between these attributes to rate of adoption. The attributes are: relative advantage, compatibility, complexity/simplicity, trialability and observability. According to Rogers (1995:212) relative advantage is the degree to which an innovation is perceived as being better than the idea it supersedes. He further observed that, the degree of relative advantage can be expressed in economic terms, social prestige, or other benefits.  Compatibility is the degree to which an innovation is perceived to be consistent with the existing values, past experiences, and needs of potential adopters (Rogers, 1995:224). It is noteworthy that an idea that is more compatible would be less uncertain to the potential adopter and fits more closely with the individual’s life situation. The degree to which an innovation is perceived as relatively difficult to understand and use is termed as complexity (Rogers, 1995:242). Any new idea may be classified on the complexity-simplicity continuum. While trialability is the degree to which “an innovation may be experimented with on a limited basis. It is argued that before deciding on whether to adopt or not to adopt a new system, a country should be permitted to use the system on a trial basis in order to test its suitability (Tanakinjal, 2012). And observability is the degree to which the results of an innovation are visible to others (Rogers, 1995:244). He also noted that the result of some ideas are easily observed and communicated to others, whereas some innovations are difficult to observe or describe to others.

Rogers concluded with a generalization (hypothesis) of the construct to the rate of adoption as follows:

“The relative advantage, compatibility, trialability and observability of an innovation, as perceived by members of a social system, are positively related to its rate of adoption. The only construct that is perceived by the members of the social system to be negatively related to its rate of adoption is complexity.”

Davis (1989) in his Technology Acceptance Model (TAM) argued that, if the members of the social system see the innovation as uncomplex (simple or ease of use), then the above hypothesis on complexity can be restated to become as follows to reflect positivity rather than negativity:

“The uncomplex (simple or ease of use) of an innovation as perceived by members of a social system is positively related to its rate of adoption”.

3. Research Methodology

This section focuses on the methods adopted in conducting the study. In order to achieve the objectives stated earlier, this section discusses and justify research designs, methods of data collection and instrumentation, techniques for data analysis, research population studied, sampling techniques and sample size of the study and justification for adopting them.

3.1 Research Design, Population, Sampling, instrumentation and Analysis Technique

The study employed cross sectional survey and correlation research designs and the period of the study is 2017. Data used in the present study were collected from primary source through the use of a Seven point Likert scale structured questionnaire. The Population is 656 accounting staff drawn from the 24 federal government ministries located in the federal capital territory Abuja, Nigeria while the sample size is 242 determined using a sample size table of Krejcie and Morgan (1970). However, Gregg (2008) suggested that where for example stratified or multistage sampling methods are employed, adjustments to certain sample size formulae are necessary especially for more complex designs or for more complex analysis rather than estimating proportions and means. Salkind (2012) further suggested that as rule of thumb researchers can increase the calculated sample size by 40% to 50% for a stratified random sample. He further stated that the increased sample size will account for lost questionnaires and non-respondents, and reduce sampling error which is the bias that resulted from mistakes in either the selection process for prospective sampling units or in determining the sample size. Hence, for this study the calculated sample size of 242 is increased by 40% to 339 sample size. In addition, out of 339 questionnaires that were distributed, 298 were returned out of which 272 are the valid questionnaires which the study used for its analysis.

Questionnaires were distributed proportionately to the 24 ministries, with participating staff (that is accountants, budget officers and internal auditors) being selected randomly. The questionnaires themselves were personally distributed by the researcher and research assistants. The participants completed the survey and returned their responses through the same route. The questionnaire contained 58 items representing the theoretical constructs along with the personal/demographic data (job of the respondents, job designation, educational and professional qualifications, ministry and experience). However, the questionnaire items were reduced to 51 items due to content validity and reliability conducted.

In order to test the sets of hypotheses and to examine the relationships between the variables, Partial Least Square (PLS) path modeling is employed (Henseler, Ringle & Sinkovics, 2009). The PLS, developed by Wold (1985), is a method for estimating path models that involves latent constructs that are indirectly measured by multiple indicators. Thus, PLS approach is one of the structural equation models that estimate the relationships via regression among latent variables, as well as between the latent variables and their indicators.  The PLS-SEM is considered as the most suitable technique in this study even though it is similar to conventional regression technique, because it has the advantage of estimating the relationships between constructs (structural model) and relationships between indicators and their corresponding latent constructs (measurement model) simultaneously (Chin, Marcolin, & Newsted, 2003; Duarte & Raposo, 2010), its ability to handle and predicts single-item constructs (Smart, 2012), its ability to use and handle small sample size (Ringle, Sarstedt & Straub, 2012),  its ability to handle both formative and reflective measure (Hair, Black, Babin & Anderson, 2014a) and its ability to handle data that is not normal (Smart, 2012). This study, therefore, employed the use of Smart PLS Version 2.0 (3M) Software (Ringle, Wende & Will, 2005) to conduct its analyses.

3.2 Research Variables and their Measurements

Based on the hypotheses stated in section two of this paper, the attributes used in developing the hypotheses are to be measured using the proxies in appendix A (questionnaire).

Table 1: Variables and Measurements

S/N Variables Type &Expected sign Measurement Source
1 Relative Advantage Dependent Variable (+) Seven point Likert scale structured questionnaire Items Rogers, (1995); Altholaya, (2013)
2 Compatibility Dependent Variable (+) Seven point Likert scale structured questionnaire Items Rogers, (1995); Altholaya, (2013)
3 Simplicity Dependent Variable (+) Seven point Likert scale structured questionnaire Items Rogers, (1995); Altholaya, 2013
4 Political Will Independent Variable (+) Seven point Likert scale structured questionnaire Items Aidoo-Buameh, 2014; Ouda, 2008
5 Infrastructure Independent Variable (+) Seven point Likert scale structured questionnaire Items Eriotis, Stamatiadis & Vasitious, 2011
6 Manpower Independent Variable (+) Seven point Likert scale structured questionnaire Items Shima & Yung, 2012
7 Collaboration Independent Variable (+) Seven point Likert scale structured questionnaire Items Brusca et al., 2015
8 Coordination Independent Variable (+) Seven point Likert scale structured questionnaire Items Hamisi, 2012
9 Legislation Independent Variable (+) Seven point Likert scale structured questionnaire Items Shima & Yung, 2012
10 Culture Change Independent Variable (+) Seven point Likert scale structured questionnaire Items Zeghal & Mhedhbi, 2006

Source: Author’s compilation from the empirical literature

Table displayed the dependent and independent variables, the a priori expected sign and literature sources. The table further shows that the expected sign for all the variables is positive (+) and questionnaire items serve as a basis for measuring the variables. The dependent variable is measured using three proxies namely:- relative advantage, compatibility and simplicity all from Rogers diffusion theory of innovation.

4. Data Analysis and Interpretation

In this section, the results are presented and discussed. The presentation starts with descriptive statistics, followed by correlation matrix and lastly logistic regression.

4.1 Descriptive Statistics

Table 1 of the paper present the total observation, means and standard deviations of the latent variables. The table indicates that the mean for all the variables had been slightly above 5.0, with the highest (manpower) mean of 5.856. This suggested that on average, the scores to the questions on the variables of the study were considerable higher on the scale, agreeing mostly with the questions.

Table 2: Descriptive Statistics

Variable N Mean Std. Deviation
COLLABORATION 272 5.470 1.319
COMPATIBILITY 272 5.472 1.232
COORDINATION 272 5.277 1.390
CULTURE CHANGE 272 5.460 .999
INFRASTRUCTURE 272 5.159 1.529
LEGISLATION 272 5.515 1.353
MANPOWER 272 5.856 1.224
POLITICAL WILL 272 5.620 1.124
RELATIVE ADVANTAGE 272 5.675 .862
SIMPLICITY 272 5.344 1.086

Source: SPSS output 2017

Again the result from the table implies that responses regarding the importance of these variables in explaining accrual basis IPSAS implementation differs significantly across the respondents as well as the federal government ministries studied.

In order to examine/determine the relationship between the independent and dependent variables, a correlation was run and the result depicted in table 2. Table 2 reveals that all pairs of independent variables correlate positively and therefore, multicollinearity is not an issue in the study model. The result further indicates that both the endogenous and exogenous latent variables have positive correlation between themselves. This implies that these variables move in the same direction. The highest correlation is between POLWILL and MANPOW, which accounted for 0.741 significant at p˂ 0.01 level of significance while the correlation between COORD and COMPAT is the lowest accounting for 0.249 also significant at p˂ 0.01 level of significance.

Table 3: Correlations

Variables Collabo Compat Coord Culchan Infrast Legisl manpow polwill Reladv simplic
Collabo 1
Compat .282** 1
Coord .527** .249** 1
Culchan .449** .472** .675** 1
Infrast .459** .360** .573** .591** 1
Legisl .563** .326** .709** .616** .576** 1
Manpow .690** .389** .723** .645** .603** .715** 1
Polwill .510** .403** .517** .578** .523** .688** .741** 1
Reladv .405** .330** .472** .613** .409** .635** .597** .623** 1
Simplic .485** .536** .328** .516** .509** .397** .594** .509** .488** 1

** Correlation is significant at the 0.01 level (2-tailed).

4.2 Reliability and Validity

The result of the reliability and validity using the Smart PLS 2.0 software package (Ringle et al., 2005) are presented in this section.

The results of the reliability and the validity using the SmartPLS 2.0 software package (Ringle et al., 2005) are presented in the following section. The composite reliability values for all the latent variables examined showed that they are all above the suggested threshold of 0.70 (Ringle et al., 2012; Henseler et al., 2009; Hair, Sarstedt, Hopkins & Kuppelwieser, 2014b). Specifically, as shown in Table 3.0, the values for the reflective multiple-items latent variables ranged from 0.813 to 0.949, thus, indicating higher levels of reliability (Hair et al., 2014b).

Following the composite reliability, the outer loadings were also examined for the indicators’ reliability. The results showed that majority of the loading values exceeded the suggested threshold value of 0.70 (Ringle et al., 2012; Henseler et al., 2009; Hair et al., 2014b), while some are below the threshold of 0.7 signifying that are not significant. The loadings ranged from 0.542 to 0.938. This means that each construct in the model has captured indicators that have much in common and they are statistically significant (Hair et al., 2014b). Again, when the standardized outer loadings were squared, as suggested by Hair et al. (2014b) the values were 0.5 and above. The square of the standardized indicator’s outer loading showed how much variation in an item is explained by its construct and this variance in an item is explained, as a rule of thumb, should be at least 0.50 (Hair et al., 2014b). Hence, in this study, the reliability of the indicators had been assumed (Wong, 2013; Hair et al., 2014b). Table 3 indicates the loadings of the items in the study model.

After assessing composite reliability, is the assessment of convergent validity, whereby the Average Variance Extracted (AVE) values were examined. All the AVE values in the results exceeded the threshold value of 0.50 (Ringle et al., 2012; Henseler et al., 2009; Hair et al., 2014b). The least value was 0.502, and hence, convergent validity was established. The AVE values are also shown in Table 3below. 

Table 4: Item loading, internal consistency, and average variance extracted (AVE) for the first-order constructs
Item Loadings AVE Composite Reliability Cronbach’s Alpha
COLLABO1 .843 .762 .905 .842
COLLABO2 .938
COLLABO3 .833
COMPAT2 .542 .602 .813 .679
COMPAT3 .877
COMPAT4 .862
COORD1 .755 .754 .939 .918
COORD2 .912
COORD3 .926
COORD4 .902
COORD5 .836
CULCHAN10 .703 .502 .875 .835
CULCHAN2 .784
CULCHAN3 .714
CULCHAN4 .731
CULCHAN5 .712
CULCHAN6 .646
CULCHAN9 .661
INFRAST1 .909 .788 .949 .933
INFRAST2 .903
INFRAST3 .897
INFRAST4 .885
INFRAST5 .844
LEGISL1 .723 .682 .927 .905
LEGISL2 .894
LEGISL3 .900
LEGISL4 .879
LEGISL5 .790
LEGISL6 .749
MANPOW1 .832 .771 .910 .851
MANPOW2 .925
MANPOW3 .874
POLWILL1 .825 .607 .902 .869
POLWILL2 .757
POLWILL3 .842
POLWILL4 .777
POLWILL5 .809
POLWILL6 .648
RELADV1 .741 .506 .859 .804
RELADV2 .676
RELADV3 .719
RELADV5 .649
RELADV6 .670
RELADV8 .802
SIMPLIC1 .802 .552 .895 .861
SIMPLIC2 .812
SIMPLIC3 .708
SIMPLIC4 .802
SIMPLIC5 .668
SIMPLIC6 .819
SIMPLIC7 .550

Following the convergent validity establishment is the discriminant validity. The discriminant validity was assessed based on Fornell and Larcker’s (1981) criterion. The results of this study showed that the square root of AVE values (values in bold face in table 4) for all constructs exceeded other construct values as they correlated with a latent variable correlation. Therefore, the discriminant validity construct wise had been established (Henseler et al., 2009; Hair et al., 2014b). Table 4 shows the results of the Fornell and Larcker’s (1981) criterion for assessing discriminant validity.

Table 5: Latent Variable Correlations and square roots of Average Variance Extracted
VARIABLES 1 2 3 4 5 6 7 8 9 10
 COLLABO .873
 COMPAT .280 .776
 COORD .520 .272 .869
 CULCHAN .452 .497 .668 .709
 INFRAST .462 .392 .576 .596 .888
 LEGISL .565 .341 .704 .625 .575 .826
 MANPOW .690 .409 .718 .650 .608 .713 .878
 POLWILL .509 .435 .514 .590 .526 .682 .739 .779
 RELADV .406 .359 .474 .636 .417 .634 .602 .622 .711
 SIMPLIC .488 .555 .333 .525 .512 .404 .602 .527 .506 .743
Note: Diagonal elements (figures in bold) are the square root of the variance (AVE) shared between the constructs and their measures. Off diagonal elements are the correlations among constructs

4.3 Measurement and Structural Models

Figure 1: Measurement model

After establishing the reliability and the validity of the constructs, the structural model estimates were evaluated. The structural model (inner model) evaluation determined the predictive ability of the model. Hen

ce, the evaluation criteria involving PLS-SEM had been the significance level of path coefficients, the coefficient of determination (R2 values), f2 effect sizes and predictive relevance (Q2) (Henseler et al., 2009; Hair, Ringle, & Sarstedt, 2011; Ringle et al., 2012; Wong, 2013; Hair et al., 2014a).

Firstly, the path coefficients were estimated through bootstrapping procedure in SmartPLS 2.0 (Ringle et al., 2005). As suggested by Hair, Ringle & Sarstedt, (2011), Hair et al., (2014a), the number of bootstrapping subsamples was set at 5,000 with 272 bootstrap cases in the data set and a no sign change. The parameters were also estimated based on a path-weighting scheme (Vinzi, Trinchera, & Amato, 2010). The bootstrapping procedure was carried out to obtain standard errors to determine the significance of the coefficients and for the test of hypotheses (Hair et al., 2011; Hair et al., 2014a).

On a significance level of p < 0.05, the results showed that all path coefficients from the predictors to the criterion variables of POLWILL to IPSASIMPL, INFRAST to IPSASIMPL, MANPOW to IPSASIMPL, and CULCHAN to IPSASIMPL were all positively significant. While the path coefficients from COLLABO to IPSASIMPL and LEGISL to IPSASIMPL are positive but not significant. The path coefficient from COORD to IPSASIMPL, is the only path which was negative but significant relationship (β = -0.305).

Table 5 presents the path coefficients, standard errors, t-values, and p-values. The validated structural model is also presented in Figures 2. The t-values are shown on the structural model.

Figure 2: Structural Model

Table 6: Path Coefficients result

Hypotheses Hypotheses Path Path Coefficient Standard Error t Value P Value
H1 POLWILL -> IPSASIMPL .195 .069 2.819 .003
H2 INFRAST -> IPSASIMPL .097 .051 1.927 .028
H3 MANPOW -> IPSASIMPL .317 .080 3.959 .000
H4 COLLABO -> IPSASIMPL .043 .041 1.049 .148
H5 COORD -> IPSASIMPL -.305 .076 -4.007 .000
H6 LEGISL -> IPSASIMPL .080 .068 1.173 .121
H7 CULCHAN -> IPSASIMPL .446 .068 6.512 .000

Source: Smart PLS report, 2017

After determining the significance of path coefficients, next is the examination of the coefficient of determination (R2) of the endogenous latent variables (Henseler et al., 2009). Based on the rule of thumb of acceptable values of R2, as proposed by Chin (1998), 0.19, 0.33, and 0.67 indicated weak, moderate, and strong respectively. The results obtained showed that the R2 for the endogenous latent variable were 0.635 for IPSASIMPL. This indicated that according to Chin (1998), the coefficient of determination (R2) in this study is strong. Overall, the R2 values obtained showed good predictive power of the exogenous latent variables on the endogenous latent variables. In other words, the amount of variance in the endogenous constructs, explained by the exogenous constructs, had been adequate. The following table (Table 6) shows the coefficient of determination (R2 value). Again, the overall coefficient of determination (R2) of 63.5% implies that the exogenous constructs, provides a good explanation about the changes in the accrual basis IPSAS implementation decisions. The remaining balance of 36.5% of the changes is explained by the other factors outside the model.

Table 7: Coefficients of Determination (R2)

Table 6: Coefficients of Determination (R2)
Construct R Square (R2)
IPSASIMPL .635

Source: Smart PLS report, 2017

In addition to determining the R2 values of all endogenous constructs, is the f2 effect size. The effect size of a construct that is exogenous is determined when the construct is omitted from a model to determine its impact on the endogenous construct by means of the change in the R2 value (Hair et al., 2014a). The effect size values represent different levels of impact, which were 0.02, 0.15, and 0.35 that represented small, medium, and large effects of the exogenous latent variables respectively (Cohen, 1988, 1992). Hence, in this study, the exogenous construct INFRAST, COORD, CULCHAN, COLLABO, LEGISL, MANPOW, and POLWILL explained the endogenous latent variables IPSASIMPL with the effect sizes of 0.008, 0.082, 0.230, 0.000, 0.008, 0.060, and 0.038 respectively (see Table 7). These showed that the effect sizes, according to Cohen (1988, 1992), had been none, small, medium, none, none, small, and small for (infrastructure, coordination, culture change, collaboration, legislation, manpower and political will) respectively (see also Table 7). Therefore, the effect sizes of all these constructs on the endogenous construct IPSASIMPL had been small.  Thus the effect size could be expressed using the following formula (Cohen, 1988, 1992):

Effect size: f2   =   R2 Included – R2 Excluded

1 – R2 Included

Table 8: f2 Effect Sizes of the latent variables
Endogenous Exogenous R-squared Included R-squared Excluded f-squared Effect size
IPSAS IMPLEMENTATION INFRAST .635 .632 .008 None
COORD .635 .605 .082 Small
CULCHAN .635 .551 .230 Medium
COLLABO .635 .635 .000 None
LEGISL .635 .632 .008 None
MANPOW .635 .613 .060 Small
POLWILL .635 .621 .038 Small

Source: Smart PLS report, 2017

Lastly, predictive relevance was also examined as an assessment of the structural model, in addition to evaluating the magnitude of the R2 values. The predictive relevance was measured by the Stone-Geisser criterion Q2 value, obtained using the blindfolding procedures (Geisser, 1974; Stone, 1974; Henseler et al., 2009; Hair et al., 2014a). Blindfolding is an iterative process where each data point is omitted based on a certain omission distance and this process is continued until completed and the model has been re-estimated (Hair et al., 2014a). Hair et al., (2014a), however, suggested that the omission distance chosen (between 5 and 10) divided by the number of cases should not be an integer.  In PLS-SEM, when predictive relevance is determined, it shows that the data points of indicators in reflective measurement models of endogenous constructs and endogenous single-item constructs are accurately predicted (Hair et al., 2014a). This procedure, as indicated by Hair et al., (2014a), does not apply to formative endogenous constructs. If Q2 value is greater than zero in a structural model for a certain reflective endogenous latent variable, the path models is considered to have predictive relevance for the particular construct whereas a Q2 measure of less than zero represents lack of predictive relevance (Chin, 2010; Hair et al., 2014a). Hence, Q2 serves as a sign of quality of the structural model (Chin, 2010; Hair et al., 2014a).

Table 8 shows the measure of the predictive relevance of the reflective endogenous latent variables in the study model.  This is represented by the Q2 values obtained by running a blindfolding procedure with an omission distance of 7 based on 272 cases. Using the cross-validated redundancy approach, as recommended by Chin, (2010); Hair et al., (2014a), the endogenous construct had proven a predictive relevance as it value of Q2 had been above zero. Specifically, the Q2 value was 0.394 for IPSASIMPL.

Table 9: Redundancy Q2 Value

Total       SSO       SSE 1-SSE/SSO
IPSASIMPL 816 494.18 .394

Source: Smart PLS report, 2017

4.4 Hypotheses Testing

Based on the results of the test of hypotheses in Table 9, the following are presented. The result of Hypothesis 1 (H1) showed that positive significant relationship existed between Political Will (POLWILL) and IPSASIMPL in the overall model as the path coefficient was positive (β = 0.195; t = 2.819; p < 0.05). With regard to H2, there was a significant positive relationship between Infrastructure (INFRAST) and IPSASIMPL (β=0.097; t = 1.927; p < 0.05). As for H3, the results showed a significant positive relationship between Manpower (MANPOW) and IPSASIMPL (β=0.317; t = 3.959; p < 0.01). Results regarding H4 showed a insignificant positive relationship collaboration (COLLABO) and IPSASIMPL (β=0.043; t = 1,049; p < 0.1). Meanwhile, for H5, there was a significant negative relationship between Coordination (COORD) and IPSASIMPL (β= -0.305; t = -4.007; p < 0.01). Likewise, results regarding H6 showed an insignificant positive relationship between legislation (LEGISL) and IPSASIMPL (β=0.080; t = 1.173; p < 0.1). With regard to H7, the results showed that there was a significant positive relationship between culture change (CULCHAN) and IPSASIMPL (β=0.446; t = 6.512; p < 0.01). Consequently, except for H4 and H6, all other hypotheses were significant. Although H5 is significant but has a negative relationship, hence the alternate hypotheses for H1, H2, H3 and H7 were accepted.

Table 10: Hypotheses Testing
Hypotheses Hypotheses Path Beta value Standard Error T Value p Value Decision
H1 POLWILL -> IPSASIMPL .195 .069 2.819 .003 Rejected
H2 INFRAST -> IPSASIMPL .097 .051 1.927 .028 Rejected
H3 MANPOW -> IPSASIMPL .317 .080 3.959 .000 Rejected
H4 COLLABO -> IPSASIMPL .043 .041 1.049 .148 Fail to reject
H5 COORD -> IPSASIMPL -.305 .076 4.007 .000 Fail to reject
H6 LEGISL -> IPSASIMPL .080 .068 1.173 .121 Fail to reject
H7 CULCHAN -> IPSASIMPL .446 .068 6.512 .000 Rejected
*: Significant at P<0.05      Source: Smart PLS report, 2017

4.5 Discussions of Findings

Having analyzed the primary data collected, the researcher has been able to observe some key issues. The findings of the study among other things include the following: This study reveals that political will is positively and significantly related with accrual basis IPSAS implementation. This finding supports the findings of Aidoo-Buameh (2014); Ouda (2008), and Hamisi (2012) who collectively reported a positive and significant correlation between political will and IPSAS implementation. But contradict the findings of Masoud (2014) who documented a negative and insignificant relationship. Again, the result of Tanjeh (2016) disagrees with the finding of the present study as well as findings of other scholars reported in this study because it revealed a negative and significant relationship between political will and acceptance of IPSAS in Cameroon. Statistically, infrastructure was found to be positively and significantly related with accrual basis IPSAS implementation decisions. This finding confirms the findings of Eriotis, Stamatiadis and Vasitiuos, (2011) which documented that the level of accrual accounting adoption was positively related to Information Technology (IT).  Tanjeh (2016) support the findings of this study as well as the finding reported by Eriotis, Stamatiadis and Vasitiuos, (2011), and Joshi, Bremser and Al-Ajmi (2008). Again, manpower has a positive and significant relationship with accrual basis IPSAS implementation. This means that IPSAS implementation in Nigeria is associated with the importance of having qualified manpower. This finding concurred with the findings of Zeghal and Mhedhbi (2006), Joshi, Bremser and Al-Ajmi (2008), Masoud (2014) and Tanjeh (2016) who documented that manpower as proxied by education level was positively and significantly associated with the adoption of international accounting standards (IASs). However, the finding of this study contradicts the finding documented by Shima and Yang (2012) in a study on the factors affecting the adoption of IFRS using the same proxy (education) for the manpower. The study reveals that education was significantly and negatively related to IFRS adoption. However, Kossentini and Othman (2011) reported a negative and insignificant relationship between education and IFRS adoption in emerging economies. This result totally disagrees with the findings of current study and also disputed the findings of Zeghal and Mhedhbi (2006), Shima and Yang (2012), Masoud (2014) and Tanjeh (2016) who collectively concurred with the finding of the present study.

With regard to the finding on collaboration, prior literature had recognized collaboration as an important factor in the accrual basis IPSAS implementation in other jurisdictions of the world and lack of it can influence public sector to adhere to the international trends in accounting (Brusca, et al., 2013; Al-zubi, 2015; Adhikari & Garseth-Nesbakk, 2016), the result of this study disputed this assertion because this study reveals that collaboration is not significant despite its positive relationship with accrual basis IPSAS implementation in Nigeria. This result can further be explained that, though collaboration has a positive relationship with accrual basis implementation, but the level of collaboration does not reach the extent to which it can have impact on accrual basis IPSAS implementation in Nigeria.

The study also found that coordination between stakeholders in the implementation of accrual basis IPSAS is negatively significant. The result indicated that coordination is inversely related to accrual basis IPSAS implementation. This means that decrease in coordination will influence the IPSAS implementation process. Even though, the result of this study contradict majority of the prior literatures, but the same findings was documented by Hamisi (2012), who equally tested this variable empirically in kenya.  To my own understanding, the decrease or low level of coordination reported in this study can best be explained by lack of functional coordinating office dedicated for IPSAS implementation. Additionally, lack of adequate and regular consultations as well as lack of effective coordination before and during the implementation impedes IPSAS implementation in Nigeria.

Similar to the finding on collaboration, legislation was found to be positive and but insignificantly related to accrual basis IPSAS implementation in Nigeria. This finding is similar to the finding of Shima and Yang (2012), who studied the relationship between IFRS adoption and legal system and found a positive and insignificant relationship. Contrary to the finding of this study, Tanjeh (2016), Cam-Van (2016), Kossentini and Othman (2011), and Masoud (2014) documented positive and significant relationship. The result of this study indicated that the legislation is not strong enough to influence IPSAS implementation. Additionally, there is a need to amend the current legislation to capture the requirements for IPSAS implementation.

Finally, culture change proxied by active participation or involvement of the management in the implementation of IPSAS, readiness for change to the new reporting system (IPSAS), willingness of staff to change to new way of reporting (IPSAS) and focus on the output instead of input is positive and significantly related to accrual basis IPSAS implementation in Nigeria. This finding is supported by Zeghal and Mhedhbi (2006) who documented that cultural membership was positively and significantly associated with the adoption of IAS and also Hamisi (2012), Cam-Van (2016) reported a positive and significant relationship between IPSAS implementation and management culture. On contrary, Masoud (2014) reported a negative and insignificant relationship between Culture and IFRS adoption. The result may not be surprising because the study was conducted in a different setting having different culture.

5. Conclusion

The study examined the determinants of accrual basis IPSASs implementation in Nigeria, 2017 being the year of study. The study reveals that political will and infrastructure have positive and significant impact on Accrual Basis IPSASs implementation in Nigeria. Similarly, manpower and cultural change are also found to have positive and significant impact on Accrual Basis IPSASs implementation in Nigeria. The findings highlight the importance of active participation and increased involvement of key stakeholders, importance of relevant and appropriate software and hardware, importance of skilled and qualified personnel and importance of change process from the traditional way of doing things to the modern way.

Based on the result, the study concludes that Accrual Basis IPSAS implementation in Nigeria could be facilitated when there is Political will, infrastructure, manpower and culture change affect the Accrual IPSAS implementation in Nigeria. The study also concludes that Lack of proper coordination could hinder the implementation of Accrual Basis IPSAS in Nigeria. Similarly, the study concludes that, collaboration and legislation does not affect Accrual IPSAS implementation in Nigeria, this is because government does not consider and exploit all the benefits of Collaborating with other governments who are successful in IPSAS implementation and the current legislation does not cover the provision of IPSAS.

The study recommends for an increased involvement of executives and legislatives, active participation, and provision of adequate funds to facilitate the implementation process.  The study also recommends that government should provide the additional needed skilled personnel to handle the accrual basis IPSAS implementation and consider the benefits of Collaborating with other governments who are successful in IPSAS implementation. Similarly, the study also recommends that government should provide additional efforts or be more proactive in providing the necessary and updated Infrastructure for the implementation of accrual basis IPSAS in the federal government ministries.

Acknowledgements

This is to show our sincere appreciation and gratitude to the Editors and reviewers of the Journal for the many valuable comments on the previous version of the paper which has improved its quality and content to the present state.

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