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. 2023 Apr 17;10:100286. doi: 10.1016/j.chbr.2023.100286

The influence of accounting information system adoption on business performance amid COVID-19

Mohamed Saad 1
PMCID: PMC10110282  PMID: 37122822

Abstract

The previous decade witnessed the dynamic progress that information systems (ISs) brought about in business performances. In this regard, an effective and efficient organization reflects heightened performance through the use of financial systems like the Accounting Information System (AIS) as the system automates the processes and improves efficiencies. In the current times, AIS has been the reason behind the optimum performance of businesses, with past studies evidencing its successful role dependent on critical success factors. Hence, the primary aim of this study is to evaluate AIS through the use of De Lone and Mc Lean's information sys-tem model (DM ISM) among Sudanese banks. The system focuses on critical factors including information quality, system quality, service quality, system usage and user satisfaction and their effects on the performance of banks in Sudan. Accordingly, this study made use of self-administered survey questionnaire to collect data from 103 AIS user, after which PLS-SEM was employed for data validation. The findings supported the significant effects of system and information quality on system usage but not services quality. Also, AIS use was found to significantly affect the performance of business. The study contributed to literature concerning IS in light of AIS benefits determinants, and it validated the proposed model among firms in Sudan. In effect, the study has both theoretical and practical significance, and it provided limitations, implications and future studies recommendations.

Keywords: Accounting information system, User satisfaction, Adoption, COVID-19, Sudan

1. Introduction

In the business model of today, signs of globalization are clearly manifested along with competitive information, dynamic digitalization and dissemination (Alsyouf et al., 2023; Lutfi, 2022a; Lutfi et al., 2023). Additionally, the current technological age has brought about data processing computrization investments in various industris, sectors and activities essentially linking technological development to methods and applications, and thereby changing the business processes (Lutfi et al., 2022a). It is evident that IT and IS use is what paved the way for business opportunities and advantages (Lutfi, 2021; Rabbani et al., 2023) and this is particularly essential in financial firms. Such firms have to deal with logistical determinants and issues more than larger firms because of their limited number of employees as well as lower appropriated budget (Ghobakhloo et al., 2019; Lutfi, 2022b). However, similar demands arise but more so in conditions which are pervasive in computer environments rather than major firms and thus, financial firms need to improve their levels of service to satisfy their regulation, oversight, cost reduction, materials procurement, inventory control and resources use objectives (Alshira'h et al., 2020). Such activities need to be carried out hand in hand with the IT department personnel, which lays credence to the role of AIS for enhancement and thereby its relationship to enhanced delivery of service, reduction of expenses, management, competitiveness in the market, functionality management and errors minimization (Lutfi et al., 2022b).

Added to the above, businesses generally adopt computerized ISs to achieve their goals and objectives and to for effective and efficient management (Alrawad et al., 2022; Lutfi, 2020; Syahidi & Arifin, 2018), leveraging such systems in a way that issues are resolved (Lutfi et al., 2020; Bokhari, 2005). Suffice it to say, enterprises would stop functioning without the help of technologies as they are the techniques used for business issues resolution (Al-Frijat & Saleh, 2014; Alshirah et al., 2021a). Aligned with this statement, the role of ISs in successful businesses (Saarinen, 1996) and the IS level (Thong & Yap, 1996) greatly contributes to achieving goals and enhancing businesses.

More specifically, majority of the current enterprises are run specifically through a core AIS, the lack of which would render the activities integration, coordination and control impossible (Alshirah et al., 2021b). AIS is basically a management information system (MIS) used for the collection, analysis, categorization, addressing and provision of different financial information to those who require it (i.e., users, beneficiaries and managers) for make decisions (Saad et al., 2022). Hurt (2013) definition of the term is a testament to this, in that it is a group of documents, activities, and technologies working together in coordination, to gathering, processing and reporting information to the entities and units that need it (stakeholders) for decision-making. AIS makes it possible to document events and transactions and to produce information for performance evaluation as well as the picture of the firm's financial transactions (Alshirah et al., 2021c; Lutfi et al., 2022d). The system integrated both computer- and information-based technology resources to document and provides the activities and transactions of the organization when it comes to its accounts.

Furthermore, in the context of Sudanese banking sector, efforts have been adopted towards AIS use in the past years to achieve enhanced efficiencies and capabilities of operations and processes, as a result of which, AIS firm adopters have risen in number, boosting the government of Sudan's inclination towards establishing initiatives and grants for rectifying low resources levels (Idris, Kamil, & Mohamad, 2016). However, similar to their counterparts in developing nations, firms in Sudan have not been successful in using AIS and in reaping benefits particularly in light of business analytics and support for decision-making (Abubkr et al., 2018; Khalid, 2020). This has been related to the complexities of decisional modules and system implementation high cost. AIS use aims to exploit real-time information, evaluation, and analysis of functional data in order so that it would achieve the best outcome in decision-making and optimum business performance. Such use contributes to information flow efficiency and effectiveness, decision-making effectiveness, and objectives achievement (Khassawneh & Lutfi, 2014).

On the above basis, this study's main objective is to carry out an identification and evaluation of the AIS influence over the performance of Sudanese banks owing to such influence inconclusive results from the numeraus research have been conducted on the topic (Lutfi et al., 2016, 2017). The study conducts an extensive review of literature as the basis of examination, particularly of studies on the direct relationship between AIS and business performance (EidMustafa & Abbas, 2017; Fitrios, 2016; Ghobakhloo et al., 2019; Nguyen & Anh, 2020; Okon et al., 2021). Following Fadelelmoula (2018), this study adopts the DM ISM (2003) in examining business performance in terms of the dimensions of quality, use of AIS and user satisfaction. The variables relationships are analyzed, with the expectation that they would lead to enhanced business performance. Business performance de-pends on the presence of critical success factors underlining the need to evaluate such factors in the context of AIS use among businesses. Factors that heighten the role and out-come of decision-making quality are the ones that need the most focus.

Therefore, the present study widened the scope of AIS research streams to cover this research objective for banks in country alike Sudan, to improve the technological footprint of banks. Thus, the purpose of the current research was to examine the quality constructs influence on AIS use, which ultimately affect both the user's satisfaction and banks performance. User's satisfaction of AIS is expected to increase performance in banks of Sudan. Due to the opportunities mentioned above, this paper seeks to address the following research questions:

RQ1

Is there any significant relationship between quality constructs (information, system and services) and AIS use in banks of Sudan.

RQ2

Is there any significant relationship between AIS use and satisfaction of users.

RQ3

Does AIS use and user's satisfaction influencing the banks performance in Sudan.

The present research contributes to the ISs and AIS literature in numerous ways. Firstly, it extends the previous literature and interested researcher in AISs context by validating the IS success model (D&M model) in banking sectors, in a country like Sudan. Notably, while earlier works tends to disregard the investigation the benefit and impact through measuring of business performance (Lutfi et al. 2021, 2022e), this research considers this factor. This in turn would enrich and improve the theoretical understanding of such relationships and impact in the context of AISs. Secondly, the current research examines the AISs user's satisfaction, which is needed for the successful and effective usage of AIS among banks. Thus, to achieve the aims and objectives of this study, earlier available knowledge is assessed in literature review for developing the hypothesis of the current research. After that, the rigorous methodology procedures are developed for conclusion the findings of this study. Findings were drawn later via applying advanced statistical technique, which is mentioned in the analysis section.

The examination of such factors in this study are organized accordingly; the topic introduction is dealt with in Section 1, followed by the review of literature regarding the topic in Section 2, based on which the empirical model is developed. Section 3 presents the theoretical background and framework, while Section 4 explains the methods for the achievement of objectives, data collection and sample frame, and data analysis. The discussions of findings and their implications are contained in Section 5, and finally, Section 6 lists the limitations of the study and recommendations for further research.

2. Literature review and hypotheses development

2.1. AIS use influence on user satisfaction and business performance

Business Performance is defined as the key outputs of any strategic endeavor initiated by business. Efficient use of technologies resource can improve the businesses competencies to enhance their performance. Previous literature documented the significant effect of innovative technologies adoption and utilization (e.g., ERP, MIS, AIS, cloud systems) on business performance. For example, Lutfi et al. (2022j) found that E-accounting systems use positively influence firm performance, such as improving cost-saving, flexibility, production lead time, forecasting, costing accuracy, and resource planning. In the cloud systems domain, business performance indicates the cloud services potentials that can significantly influence the cloud based processes and operations (Alharasis et al., 2022; Saad et al., 2022). The effective use of cloud system positively affects business performance. In the context of ERP, Lutfi, Alshira'h et al. (2022b) conducted a study in the context of Jordanian SMEs and found significant impact of ERP adoption on performances. However, AIS as an important resource for business, enables to concentrate on core capabilities, enhanced productivity, competitiveness and eventually performance. In this regard, Lutfi (2022a) stated that the successful usage of AIS positively influences the businesses performance for cloud-supported operations.

In the field of IS, studies presented system use as the effort level in using the system, equating to the system's output degree in light of a time unit (Almaiah et al., 2022a; Trice & Treacy, 1988). Using IS is primarily dependent on its assessment by the user and if the user believes that task performance is enhanced through such use, satisfaction and frequency of use will eventually follow suit (Almaiah et al., 2022b; Bokhari, 2005; Lutfi et al., 2022f). User satisfaction refers to the user's degree of perception of information that the system provides meeting of his/her needs, and thus, it relates to the experience of the user in using the system, his satisfaction and the result of his use of the information in his reaching decisions (Al-Khasawneh & Lutfi, 2013; Almaiah et al., 2022c; Chou et al., 2014).

In addition, AIS user satisfaction is linked to the AIS use (Chou & Hong, 2013; Lutfi et al., 2022g), with such use contributing to performance, efficiency as well as productivity (Lin, 2010; Lutfi et al., 2022g). Past studies gauged users satisfaction and use through three major measurements, which, are, time, in, hours, use, frequency and use level (e.g., Lutfi et al., 2022g; Chou et al., 2014; Lin et al., 2006; Ramli, 2013). Moreover, other, studies including Hsu et al. (2015) and Wixom and Todd (2005) presented four evaluation measures of use satisfaction, namely services satisfaction, information satisfaction, SQ satisfaction, and the overall satisfaction of using AIS. Studies have primarily supported a significant positive link between using AIS and system satisfaction, and as such, this study proposes the following hypotheses:

H1

AIS usage has a significant effect on user satisfaction.

H2

AIS usage has a significant effect on bank performance.

2.2. Users satisfaction

User satisfaction is another factor proposed in D&M IS model, comprising of repeat purchase and visits – with the former referring to the difference between information required and information obtained (Almaiah et al., 2022e; Alsyouf, Lutfi, et al., 2022; Alsyouf & Ku Ishak, 2018). Also, satisfaction of information stems from comparing IS needs and its performance. Contrastingly, repeat purchase refers to the system's global satisfaction examined using the IS level, satisfaction and benefits reaped from the process of input and output (Alam et al., 2023; Almaiah et al., 2022f; Alsyouf et al., 2021).

With regards to performance, it is described as user system interaction, that leads to a certain result (Petter & McLean, 2009) and in the light of AIS performance, it is described as system, ability to present information, that is accuerate, reliable and precise (Al-Mugheed et al., 2022; Alalwan et al., 2014; Almaiah et al., 2022g). ON the basis of the result reported by Bhattacherjee (2001), the effectiveness of system use partially depends on the user's satisfaction; in other words, the AIS use level and frequency and user satisfaction leads to enhanced performance and as such, the present study, proposes, that;

H3

User's satisfaction has a significant effect on bank performance

2.3. Information quality

Studies dedicated to IS, specifically information quality (IQ) and its role in IT use among businesses are plentiful, describing the system's ability to supply accurate, complete, relevant and timely information to serve the end-users decision making (Almaiah et al., 2022d). Information quality is used to gauge the output quality of the information that IT produces (DeLone & Ephraim, 1992; 2003), mitigating erroneous transactions, and enabling the generation of information that is accurate and valuable. Studies along this line looked into the relationship between IQ and IT use and came up with mixed outcomes, with hard to reach conclusions. To begin with, Anggadini (2015) revealed evidence for the significant impact of IQ on AIS among firms in Indonesia, and similarly, Alzoubi (2011) found IQ to have a significant impact on AIS in the context of financial accountants and managers of firms in Jordan. However, other studies found an insignificant relationship between the two variables (IQ and AIS) (e.g., Alkhazaleh, Mansour Khalaf & Ahmad, 2021; Almaiah et al., 2022h; Daoud & Mohamed, 2013), reporting such insignificant effect between IQ dimensions and use of IS. Hence, this study proposes the following hypotheses for testing;

H4

IQ has a significant effect on AIS use.

2.4. System quality

System quality is another, construct, of D&M IS model, which is described as the degree of technical, efficiency. In light of using system, reliability, response time, security as well as flexibility of the system (DeLone & Ephraim, 1992; 2003). High quality systems enable the provision of rated adopters based on their AIS perceptions and simple use, which makes system quality to be one of the major AIS use of firms, as suggested by De Lone and Ephraim (2003). The authors indicated that use and design effectiveness of IS can enhance effectiveness although theories that support the direct effect remain unexplored with most of the examinations reporting a combination of findings.

This study examines system quality as one of the DM ISM factor based on its effect of its use among firms to promote improved decision-making and overall performance. Past studies support a general significant relationship between system quality and use of IS including Almaiah et al. (2022i) and Quintero et al. (2009). Also, Lutfi et al. (2022g) looked into the post use of IS and found SQ to have a significant influence over it. This went the same for Xu et al. (2013), using an a3Q model, proposed, by Nelson et al. (2005), whereby the focus is placed on the, effect of SQ on the adoption of IS and was validated by findings.

Moreover, Negash et al. (2003) also supported system quality relationship with SQ web-based customer use in the organization. Notwithstanding the past related studies, a need exists to further look into system quality in different contexts and hence, the current work propos that:

H5

SQ has a significant effect on AIS use.

2.5. Service quality (SerQ)

Another DM ISM variable that has a potential effect on its use is SerQ and it comprises of indicators (assurance and empathy). Generally speaking, IS provides required information and knowledge but these need to be hazard and risk-free, and the system needs to facilitate easy communication. In this regard, service quality is a measure of the IS quality of service and it is employed by the marketers to investigate the system, being that it reflects its effectiveness and supports the IS department, measuring system reliability, empathy and response (Al-Khasawneh, Lutfi, & Jaber Barakat, 2016; Alrawad, Lutfi, Alyatama, et al., 2023; Almaiah et al., 2022j).

In fact, the analysis of IT has garnered increasing importance throughout time, with services quality considered as one of the IS top dimension, particularly in terms of competitiveness. According to Arshah et al. (2012), SerQ facilitates system enhancement throughout business departments, supports users and improves the performance of the organization, while Chang et al. (2012) found a significant positive SerQ-IS usage relationships.

Nevertheless, other research along this line (e.g., Al-Khasawneh, 2013; Negash et al., 2013; Petter, Stacie & McLean, 2009) reported insignificant association between the SerQ and IS use, leading to mixed findings. In relation to this, SerQ dimensions in DM ISM may have various weights based on the context of the past findings and their analysis, and thus, the present work proposes the following hypothesis;

H6

SQ has a significant effect on AIS use.

3. Methodology

3.1. Measurement development and data collection

The study adopted a questionnaire initially developed in English and later translated into Arabic, within which the items were used to test the hypotheses (Al-Khasawneh et al., 2022). The items were selected from prior literature in AIS and IS and with regards to the metrics of the questionnaire, modification were not made for retaining their importance within the context of the study. Four experts in the field of AIS and IS were consulted for the questionnaire development, after which it was exposed to pre-testing to determine comprehensibility, clarity, relevance and lack of ambiguity of the questions as recommended by past studies (e.g., Al-Khasawneh & Lutfi, 2013; Almaiah et al., 2022h: Alsyouf, Ku Ishak, et al., 2022; Almaiah et al., 2022i, Sekaran & Roger, 2013). Accordingly, a thorough review and assessment of the questionnaire was conduct-ed by three senior managers and four directors working in Sudanese banks using AIS. Following the review, the some items were tweaked to meet readability of the questionnaire for post-pretest completion. The items were gauged on a 5-point Likert scale that ranged from strongly disagree depicted by 1, to strongly agree depicted by 5.

Moreover, users of AIS holding positions as decision-makers were the survey targets, the process of which lasted three months (15 June 2022–17th September 2022). The survey copies were distributed online to various banking sectors and from the copies distributed, 103 were returned. The suitable number of samples were calculated using Hwang et al.’s (2016) suggestion of the, least, sample, size, to, be, 10 times higher than the, number, of, paths, leading, to the endogenous constructs (N = 60). In a similar line of recommendation, Hair et al. (2019) stated that the respondents number, should, be, 8 times, that, of the number of the study, constructs (Almaiah et al., 2022j; Alrawad, Lutfi, Almaiah, et al., 2023; Shatnawi et al., 2022), based on which, the least size of the sample required was (N = 48).

The study carried out a statistical, power analysis for the estimation of the sample size according to Cohen (2013), the result of which was obtained through the calculation using a prior power analysis (G*Power Software). Consequently, the alpha value was found to be 0.05, effect, size was 0.15 (moderate), with power, of, 0.80, indicating that the size of the sample should, be, 96. Therefore, from the aforementioned two methods, at least 96 valid questionnaires are required for sample size estimation. Accordingly, 103 was deemed appropriate for the employment of PLS analysis. PLS was specifically used to test the formulated hypotheses being that it is a multivariate statistical analysis method, which enable the simultaneous estimation of different relationships of multiple exogenous variables and endogenous variables within one model. The professional working of this approach allows the analysis of complex models that have mediating/moderating relation-ships, even with small-sized samples, allowing its use in some circumstances, whereby CB-SEM and other techniques remain useless.

4. Data analysis

4.1. Internal reliabelity

This type of reliabelity reflects the level to which the subscale indicators measure the equivalent concept (Hair et al., 2019), of which composite reliabelity value need to meet the criterion (at least 0.70), with AVE of at least 0.50 (see Table 1 ). The composite reliability and AVE values were met in this study (over 0.50) establishing the reliabelity of the measurement model. Furthermore, the internal consistency of the data was confirmed through Cronbach's alpha values, following Sekaran and Roger’s (2010) rule of thumb that states alpha value of >0.9 to be excellent, >0.80 to be good and >0.70 to be acceptable. The values of Cronbach's alpha in this study satisfied the criterion along with the values of AVE and composite reliabelity values (see Table 1).

Table 1.

Convergent validity results.

Variables Cronbach Alpha Composite Reliability AVEs
Information Quality 0.907 0.927 0.736
System Quality 0.817 0.876 0.647
Service Quality 0.904 0.925 0.729
AIS Use 0.908 0.939 0.836
User Satisfaction 0.937 0.955 0.848
Bank Performance 0.798 0.856 0.614
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The Cronbach's alpha values displayed in the above table exceed 0.70 and thus, establishing consistency. AVE values and high reliabilities also supported the measurement model's, reliability.

4.2. Discriminant validity

The level to which a variables differ from other variable of its kind is known as discrimenant validity (Alsaad et al., 2023; Hair et al., 2019), making this type of validity a condition for evaluation. According to Duarte and Raposo (2010), it is the degree to which a specific element differs from other element, and the greater the level of validity the, greater, will, be the variable's difference in shedding light on the, phenomenon, compared, to, other, variables. Discriminant validity was thus obtained in this, study by squared root AVEs (Hair et al., 2019) based on the criteria that its value should be higher than the value of the correlations between latent constructs.

In Table 2 , the values of the latente constructs correlations and the squared roots of AVEs values are presented specifically of bank performance (0.786), IQ (0.859), SerQ (0.852), SQ (0.807), AIS usage (0.898) and lastly, user satisfaction (0.921).

Table 2.

Discriminant validity (fornell larcker scale).

Constructs 1 2 3 4 5 6
1 Bank Performance 0.786
2 Information Quality 0.454 0.859
3 Services Quality 0.276 0.582 0.852
4 Systems Quality 0.442 0.657 0.556 0.807
5 AIS Use 0.484 0.493 0.429 0.517 0.898
6 Users Satisfaction 0.547 0.613 0.546 0.577 0.552 0.921
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Based on the values in the above table, the squared root of AVEs exceeded the correlations between latente variables, and thus, discrimenant validity is proven. The past sections shed light on the study framework and the relationships of the variables proposed within it based on past literature, which required confirmation on the basis of CFA. The findings of the analysis needed no variable to be deleted, although some of the items were aligned with Hair (2019), ensuring that each, variable, at, least, had two remaining items.

In Table 3 , the values support those in Table 2, whereby the hypothesized relation-ships with p-values exceeding 0.05 were rejected and those lower than 0.05, were sup-ported. Five direct relationships were supported while 1 was rejected.

Table 3.

Hypothesis testing.

Hypo Relationships Sta. Beta t-Value p-Value Result
H1 IQ–AIS 0.235 1.963 0.051 Accepted
H2 SQ–AIS 0.2875 2.090 0.038 Accepted
H3 SerQ–AIS 0.144 1.283 0.202 Not Accepted
H4 AIS–Users Satisfaction 0.253 2.650 0.008 Accepted
H5 AIS Usage–Bank Performance 0.217 2.114 0.037 Accepted
H6 Users Satisfaction–Bank Performance 0.443 5.058 0.001 Accepted
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4.3. Assessing R-squared (R2) and effect sizes (f2)

By Cohen's (2013) estimation, R2 values of 0.26, 0.13, and 0.02 for endogenous constructs are, respectively, substantial, moderate, and weak. Therefore, in this research, the moderate correlations for the constructs of AIS Adoption and Users Satisfaction are shown in Table 4 . However, AIS Adoption, and Users Satisfaction are a significant predictors of Bank Performance.

Table 4.

Coefficient determination.

R Square Effect Size
AIS Adoption 0.157 Moderate
Users Satisfaction 0.232 Moderate
Bank Performance 0.273 Substantial
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Additionally, using the f2 test, it can determine the magnitude of the effect (Cohen, 2013). When referring to f2, Cohen (2013) classifies values of 0.35, 0.15, and 0.02 as large, medium, and small, respectively. The effect size is shown in Table 5 ; it suggests that the estimated model provides a good fit to the data.

Table 5.

The effect size of a model.

f2
 Effect
 F2
 Effect
 F2
 Effect
AIS Adoption User Satisfaction Performance
Information Quality 0.169 Medium
System Quality 0.262 Medium
Service Quality 0.027 Small
AIS Adoption 0.263 Medium 0.374 Large
User Satisfaction 0.265 Medium
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5. Discussion and implications

The present study and the obtained findings from its analysis of data has some implications to both theory and practice concerning the DM model's quality constructs and their effects over the AIS use, which were supported with the exception of service quality. Studies of this caliber in literature by Yakubu and Dasuki (2018), Jaafreh (2017), and Tajuddin (2015) supported and confirmed the significant effects of information quality and system quality on use and user satisfaction but not that of service quality. As a con-sequence, the findings were consistent with those of past studies; for instance, Ghobakhloo and Tang (2015) and Marble (2003) reported the same findings. However, DM model contentions concerning SerQ, as a significant construct in systems usage and systems effectiveness assessment is questionable based on the findings, and this may be attributed to the respondents' lack of satisfaction with the AIS services stemming from lack of communication, broken promises and delayed technical support. Added to this, the context of the study is a developing nation, whose firms are mainly characterized with employees lacking in technical IS training, precluding the system features efficient and overall usage. This needs the provision of training sessions that exceeds the possession of mere computer skills.

In contrast to the proposed hypothesis, the findings rejected the effect of service quality on AIS use - a finding that is not aligned with DMIS prediction that the higher the service quality, the more enhanced will be the intention towards AIS use. Such prediction has been garnering increasing support from past related studies. Despite such contradiction, some of the findings of this study is consistent with past studies that also rejected the role of SerQ in promoting use of innovations (e.g., Ghobakhloo & Tang, 2015; Lutfi et al., 2022h) and this may be owing to the pressure exerted on businesses towards AIS use brought on by the Covid-19 pandemic. In effect, banks use of AIS depended on the uncertainty and lockdown circumstances, driving all businesses to do so. AIS was accepted and employed to enhance reputations and images, pursue workflow and keep abreast of the industry, notwithstanding the benefits or lack thereof. Thus, quality of services of the system use remained negligible.

Moving on to other findings, a significant AIS use and users satisfaction association was supported, along with significant relationships between AIS use and IQ and system quality. The findings revealed that SQ is the top predictor, of, AIS, use from the other predictors examined and that maybe related to the varying importance level of the ISs success models constructs based on the attributes of the organization (Almaiah et al., 2022k; Heo & Han., 2003). Evidently, centralized computing in firms stress more on system quality rather than information quality as reported by Petter and McLean (2009) and this is aligned with other studies as well (e.g., Almaiah et al., 2022l; Hsu et al., 2015; Lin et al., 2006; Li & Wang, 2021). In these studies, system quality covers flexibility of the system and its reliability, urging the user towards system use, which ultimately leads to their satisfaction.

According to other studies in the same caliber, AIS use frequently leads to enhanced business performance (Abdo et al., 2021; Alalwan et al., 2014; Hou, 2013), supporting the important role of using AIS in enhanced decision-making, information quality, reliable and accurate information use outcome and user's satisfaction (Ramli, 2013). Added to this, Ouiddad et al. (2018) empirically found that ISs adoption to result in enhanced performance of businesses.

This study conducted an empirical examination of the factors effects on business performance and the findings validated the use of AIS-based decision process, involving evaluation and processing. This highlights the firms need to adopt novel processes to, shed, light, on the context and for AIS use effectiveness and meaningfulness. It is important for the stakeholders and experts to come together and collaborate on the domain systems to ensure clarity of processing and implications in AIS as this would lead to the thorough evaluation of the required sources and resources.

Based on the findings, the, theoretical, framework, is effective in, evaluating, the use of AIS in enhanced performance of businesses paving the way for more in-depth investigations into the topic. Constructs relationships in DM ISM showed the AIS capabilities to improve quality of decision-making in the midst of complex issues through its efficient use and thus, AIS role in enhancing performance and competitiveness among businesses has been validated.

Finally, business performance can be motivated through AIS use, facilitating management control and oversight of different complex problems and transactions on the basis of different viewpoints in order to reach informed, precise and fact-based decisions. The D&M (2003) extension with the addition of decision making quality variables is useful in studying technologies and innovations, in various contexts and nations having similar level of backgrounds, both economic and social, (Almaiah et al., 2022k; Khassawneh & Lutfi, 2014). Nevertheless, the limited size of the sample employed in this study calls for caution when generalizing the results to other nations as suggested by past literature (Almaiah et al., 2022l; Lutfi et al., 2022c, 2022h, 2022i, Lutfi et al., 2022j).

In an uncertain, information-requiring environment, the firm's information processing and sourcing play a key role and in this regard, using AIS can extend such capabilities, making the firm adept at the collection, analysis and relay of information, and enhancing the possibility of achieving optimum performance (Lutfi et al., 2022k).

6. Conclusion, limitations, and recommendations for further works

Applying the D&M IS Model, this study formulates a theoretical framework to analyze the impact of system IQ, SQ and SerQ on AIS use and user's satisfaction, and eventually bank performance. By using 103 data units culled from bank’ employees in Sudan, this study finds significant and robust empirical evidence to support the hypotheses. It shows that system and information quality significantly affect system usage. User satisfaction positively associated with business performance. We further find that AIS use was found to significantly affect user satisfaction and the performance of business. Finally, service quality is not predicted by AIS use.

Notably, this study has several contributions to theory, the first being that it clarifies the relationship between AIS adoption and the factors affecting it in Sudanian banks, being a developing nation. To the best of the authors’ knowledge, this is a pioneering empirical/theoretical study to examine the influencing factors of AIS use and their eventual impact on the performance of businesses. According to the reviewed literature, several technologies came under the radar of most studies, but little is known about AIS and its contribution to businesses and thus, this study adds to literature as it presents a comprehensive conceptual model based on DM ISSM that is effective in explain quality factors influence over AIS use and user satisfaction. In so doing, the DM ISSM explanatory and predictive strength increases and generates outcomes serving the academic and practical circles.

In this line of argument, the findings also contribute to practice, primarily of government agencies and decision-makers, consultants and vendors of AIS as well as the firms. The advantages of AIS are supported through the validation of the significant relationship between its use and business performance, indicating that AIS use in firms can enhance their operations, competitiveness, and productivity, and accurate information, timely decision-making and overall improved and optimum performance. This study is complementary to past studies that evidenced the relationships and thus, it is important for the relevant entities to acknowledge AIS in the performance, growth and sustainability of firms.

With regards to the dealings with Covid-19 pandemic, the pressure on technologies use (e.g., AIS) has been compounded specifically among businesses that are intent on maintaining stability and performance. AIS use enhancement is possible through the collaboration of relevant agencies and entities in promoting AIS and its benefits during disruptive incidents like the pandemic. This realization would lead to enhanced use of AIS among such businesses.

This study is unique as it stresses on the AIS benefits in terms of operations, transactions and performance and the results validate the, assumptions, of, the adopted model, DM IS Model (2003), and the additional factors.

Despite its numerous contributions, this study has several limitations considering that no single study can completely answer all questions relating to any investigated topic. Among its limitations is the size of the sample used which is limited and thus, future studies can rise the size of sample to encapsulate other groups and demographics. One more limitation, is, the, context, of, study that is banks in Sudan, which could lead to the limited generalization to other sectors, countries and contexts. In this regard, future studies may examine developing African and Asian nations. In addition, this study is limited, as it adopted a cross-sectional study design; requiring data culled from one period – for more accurate measurement, the time of the study needs to be extended in a longitudinal style study. This limitation was also, repeatedly, mentioned in past DM studies and thus, future studies may adopt a strategy to examine pre- and post-adoption AIS usage.

Furthermore, this study is limited to examining certain factors in the proposed model (i.e., AIS use net benefit) and as such, further research may look into the system's net bene-fits and the like. Additionally, AIS use is important for competitiveness and sustainability among businesses, and thus, this study may be replicated by future studies to enhance the results external validity. Also, this study is limited pertaining to the response rate, regard-less of the follow-up efforts exerted by the authors, resulting in 103 responses. This number is satisfactory for model-fit testing and statistical inferences through PLS-SEM but future studies can confirm the results by employing a large sample size so that CB-SEM tool may also be used to ascertain accurate and precise results.

Lastly, this study examined limited variables, as other variables also contribute to enhanced business performance via AIS use, like training, user experience, maturity of AIS, internal control quality – these factors may also count in reaping the benefits and advantages brought onto the table by AIS, primarily business performance. The determination of such important factors would pave the way for a better insight into the examined topic.

Funding

This research was funded by the Deanship of Scientific Research at King Faisal University, grant no. [GRANT3286].

Declaration of competing interest

The authors declare no conflict of interest.

Data availability

Data will be made available on request.

References

  1. Abdo K., Ahmed A., Elsayed Ibrahim N.M. Effect of adopting the criterion of revenue from contracts with clients on accounting conservatism. Proceedings of ICGER. 2021;2021:55. [Google Scholar]
  2. Abubkr A.E.A., Mohammed E.M.K., Badreldin E.A.S., Musa E.M. 2018. The role of internal audit in evaluating the financial performance at Sudanese banks. [Google Scholar]
  3. Al-Frijat, Saleh Y. The impact of accounting information systems used in the income tax department on the effectiveness of tax audit and collection in Jordan. Journal of Emerging Trends in Economics and Management Sciences. 2014;5:19–25. [Google Scholar]
  4. Al-Khasawneh, Lutfi A. The relation between human resource management (HRM) strategies and job loyalty as practiced at the public relations (PR's) units in the government ministries of Jordan. Journal Of Management Research. 2013;5(3):146. [Google Scholar]
  5. Al-Khasawneh, Lutfi A., Al-Zoubi M.R., Alnajjar F.J. Quality between the contemporary management & islamic thought perspectives: Comparative study. Journal Of Emerging Trends In Economics And Management Sciences. 2013;4(2):281–290. [Google Scholar]
  6. Al-Khasawneh, Lutfi A., Eyadat H.M., Alrawad M., Almaiah M., Lutfi A.A. The impact of organizational training operations management on job performance: An empirical study on Jordanian government institutions. Global Business Review. 2022 [Google Scholar]
  7. Al-Khasawneh, Lutfi A., Jaber Barakat H. The role of the Hashemite leadership in the development of human resources in Jordan: An analytical study. International Review Of Management And Marketing. 2016;6(4):654–667. [Google Scholar]
  8. Al-Mugheed Khaild, Bayraktar Nurhan, Al-Bsheish Mohammad, AlSyouf Adi, Aldhmadi Badr K., Jarrar Mu’taman, Alkhazali Moath. Effectiveness of game-based virtual reality phone application and online education on knowledge, attitude and compliance of standard precautions among nursing students. Plos one. 2022;17(11) doi: 10.1371/journal.pone.0275130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Alalwan J., Thomas M.A., Roland Weistroffer H. Decision support capabilities of enterprise content management systems: An empirical investigation. Decision Support Systems. 2014;68:39–48. [Google Scholar]
  10. Alam M.M., Lutfi A., Abdallah A. Antecedents and consequences of customers' engagement with pro-environmental consumption-related content on social media. Sustainability. 2023;15(5):3974. [Google Scholar]
  11. Alharasis, Esam E., Tarawneh A.S., Shehadeh M., Haddad H., Ahmad M., Hasan E.F. Reimbursement costs of auditing financial assets measured by fair value model in Jordanian financial firms' annual reports. Sustainability. 2022;14(17) [Google Scholar]
  12. Alkhazaleh, Mansour Khalaf A., Ahmad M. Would irregular auditing implements impact the quality of financial reports: Case study in Jordan practice? Journal of Management Information and Decision Sciences. 2021;24(6):1–14. [Google Scholar]
  13. Almaiah, Amin M., Al-Otaibi S., Lutfi A., Omar A., Awajan A., Alsaaidah A., Alrawad M., Ali B.A. Employing the TAM model to investigate the readiness of M-learning system usage using SEM technique. Electronics. 2022;11:1259. [Google Scholar]
  14. Almaiah, Amin M., Al-Rahmi A.M., Alturise F., Alrawad M., Alkhalaf S., Lutfi A., Al-Rahmi W.M., Ali B.A. Factors influencing the adoption of internet banking: An integration of ISSM and UTAUT with price value and perceived risk. Frontiers in Psychology. 2022;13 doi: 10.3389/fpsyg.2022.919198. [DOI] [Google Scholar]
  15. Almaiah, Amin M., Al-Rahmi A., Alturise F., Hassan L., Lutfi A., Alrawad M., Alkhalaf S., Al-Rahmi W.M., Al-sharaieh S., Theyazn H.H.A. Investigating the effect of perceived security, perceived trust, and information quality on mobile payment usage through near-field communication (NFC) in Saudi arabia. Electronics. 2022;11:3926. doi: 10.3390/electronics11233926. [DOI] [Google Scholar]
  16. Almaiah, Amin M., Alfaisal R., Said A., Salloum, Hajjej F., Shishakly R., Lutfi A., Alrawad M., Mulhem A.Al, Alkhdour T., Al-Maroof R.S. Measuring institutions' adoption of artificial intelligence applications in online learning environments: Integrating the innovation diffusion theory with technology adoption rate. Electronics. 2022;11:3291. [Google Scholar]
  17. Almaiah, Amin M., Alfaisal R., Salloum S.A., Al-Otaibi S., Al Sawafi O.S., Al-Maroof R.S., Lutfi A., Alrawad M., Mulhem A.Al, Awad A.B. Determinants influencing the continuous intention to use digital technologies in Higher Education. Electronics. 2022;11:2827. [Google Scholar]
  18. Almaiah, Amin M., Alfaisal R., Salloum S., Al-Otaibi S., Shishakly R., Lutfi A., Alrawad M., Mulhem A.Al, Ali B.A., Al-Maroof R.S. Integrating teachers' TPACK levels and students' learning motivation, technology innovativeness, and optimism in an IoT acceptance model. Electronics. 2022;11:3197. [Google Scholar]
  19. Almaiah, Amin M., Alfaisal R., Salloum S.A., Hajjej F., Thabit S., Ali El-Qirem F., Lutfi A., et al. Examining the impact of artificial intelligence and social and computer anxiety in E-learning settings: Students' perceptions at the university level. Electronics. 2022;11:3662. [Google Scholar]
  20. Almaiah, Amin M., Alhumaid K., Aldhuhoori A., Alnazzawi N., Ahmad A., Alfaisal R., Said A., Salloum, et al. Factors affecting the adoption of digital information technologies in higher education: An empirical study. Electronics. 2022;11:3572. [Google Scholar]
  21. Almaiah, Amin M., Ayouni S., Hajjej F., Lutfi A., Omar A., Ali B.A. Smart mobile learning success model for higher educational institutions in the context of the COVID-19 pandemic. Electronics. 2022;11:1278. [Google Scholar]
  22. Almaiah, Amin M., Hajjej F., Lutfi A., Al-Khasawneh A., Alkhdour T., Omar A., Shehab R. A conceptual framework for determining quality requirements for mobile learning applications using delphi method. Electronics. 2022;11:788. [Google Scholar]
  23. Almaiah, Amin M., Hajjej F., Lutfi A., Al-Khasawneh A., Shehab R., Al-Otaibi S., Alrawad M. Explaining the factors affecting students' attitudes to using online learning (madrasati platform) during COVID-19. Electronics. 2022;11:973. [Google Scholar]
  24. Almaiah, Amin M., Hajjej F., Shishakly R., Lutfi A., Amin A., Ali B.A. The role of quality measurements in enhancing the usability of mobile learning applications during COVID-19. Electronics. 2022;11:8070. doi: 10.3390/1748070. [DOI] [Google Scholar]
  25. Alrawad M., Lutfi A., Almaiah M.A., Alsyouf A., Al-Khasawneh A.L., Arafa H.M., Ahmed N.A., AboAlkhair A.M., Tork M. Managers' perception and attitude toward financial risks associated with SMEs: Analytic hierarchy process approach. Journal of Risk and Financial Management. 2023;16(2):86. [Google Scholar]
  26. Alrawad M., Lutfi A., Alyatama S., Al Khattab A., Alsoboa S.S., Almaiah M.A., Hashim Ramadan M., et al. Assessing customers perception of online shopping risks: A structural equation modeling–based multigroup analysis. Journal of Retailing and Consumer Services. 2023;71 [Google Scholar]
  27. Alrawad M., Lutfi A., Alyatama S., Elshaer I., Almaiah M.A. Perception of occupational and environmental risks and hazards among mineworkers: A psychometric paradigm approach. International Journal of Environmental Research and Public Health. 2022;19:3371. doi: 10.3390/ijerph19063371. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Alsaad A., Alam M.M., Lutfi A. A sensemaking perspective on the association between social media engagement and pro-environment behavioural intention. Technology In Society. 2023;72 [Google Scholar]
  29. Alshira’h, Ahmad F., Alsqour M., Lutfi A., Alsyouf A., Malek A. A socio-economic model of sales tax compliance. Economies. 2020;8:88. doi: 10.3390/economies8040088. [DOI] [Google Scholar]
  30. Alshirah, Hamed M., Alshira’h Ahmad F., Abdalwali L. Political connection, family ownership and corporate risk disclosure: Empirical evidence from Jordan. Meditari Accountancy Research. 2021 doi: 10.1108/MEDAR-04-2020-0868. [DOI] [Google Scholar]
  31. Alshirah, Hamed M., Alshira’h Ahmad F., Abdalwali L. Audit committee's attributes, overlapping memberships on the audit committee and corporate risk disclosure: Evidence from Jordan. Accounting. 2021;7:423–440. [Google Scholar]
  32. Alshirah M., Lutfi A., Ahmad A., Saad M., Ibrahim N.M.E.S., Mohammed F. Influences of the environmental factors on the intention to adopt cloud based accounting information system among SMEs in Jordan. Accounting. 2021;7:645–654. doi: 10.5267/j.ac.2020.12.013. [DOI] [Google Scholar]
  33. Alsyouf A., Ku Ishak A. Understanding EHRs continuance intention to use from the perspectives of UTAUT: Practice environment moderating effect and top management support as predictor variables. International Journal of Electronic Healthcare. 2018;10:24–59. [Google Scholar]
  34. Alsyouf A., Ku Ishak A., Lutfi A., Nasser Alhazmi F., Al-Okaily M. The role of personality and top management support in continuance intention to use electronic health record systems among nurses. International Journal of Environmental Research and Public Health. 2022;19 doi: 10.3390/ijerph191711125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Alsyouf A., Lutfi A., Al-Bsheish M., Jarrar Mu’taman, Al-Mugheed K., Almaiah M.A., Nasser Alhazmi F., Masa’deh Ra’ed, Anshasi R.J., Abdallah A. Exposure detection applications acceptance: The case of COVID-19. International Journal of Environmental Research and Public Health. 2022;19:7307. doi: 10.3390/ijerph19127307. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Alsyouf A., Lutfi A., Alsubahi N., Nasser Alhazmi F., Al-Mugheed K., Rami J. Anshasi, nora ibrahim alharbi, and moteb albugami. "The use of a technology acceptance model (TAM) to predict patients' usage of a personal health record system: The role of security, privacy, and usability. International Journal Of Environmental Research And Public Health. 2023;20(2):1347. doi: 10.3390/ijerph20021347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Alsyouf A., Masa’deh Ra’ed, Albugami M., Al-Bsheish M., Lutfi A., Alsubahi N. Risk of fear and anxiety in utilising health app surveillance due to COVID-19: Gender differences analysis. Risks. 2021;9:179. [Google Scholar]
  38. Alzoubi A. The effectiveness of the accounting information system under the enterprise resources planning (ERP) Research Journal of Finance and Accounting. 2011;2:10–19. [Google Scholar]
  39. Anggadini S.D. The effect of top management support and internal control of the accounting information systems quality and its implications on the accounting information quality. Information Management and Business Review. 2015;7:93–102. [Google Scholar]
  40. Arshah R.A., Ishak Desa M., Che Hussin A.R. Establishing important criteria and factors for successful integrated information system. Global Journal on Technology. 2012;2:311–317. [Google Scholar]
  41. Bhattacherjee A. Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly. 2001;25:351–370. [Google Scholar]
  42. Bokhari R. The relationship between system usage and user satisfaction: A meta-analysis. Journal of Enterprise Information Management. 2005;18:211–234. [Google Scholar]
  43. Chang Y.-ke, Zhang X., Mokhtar I.A., Schubert F., Majid S., Luyt B., Yin-leng T. Assessing students' information literacy skills in two secondary schools in Singapore. Journal of Information Literacy. 2012;6:19–34. [Google Scholar]
  44. Chou H.-W., Chang H.-H., Lin Y.-H., Chou S.-B. Drivers and effects of post-implementation learning on ERP usage. Computers in Human Behavior. 2014;35:267–277. [Google Scholar]
  45. Chou J.-S., Hong J.-H. Assessing the impact of quality determinants and user characteristics on successful enterprise resource planning project implementation. Journal of Manufacturing Systems. 2013;32:792–800. [Google Scholar]
  46. Cohen J. Statistical power analysis. Current Directions in Psychological Science. 2013;1:98–101. [Google Scholar]
  47. Daoud H., Mohamed T. Accounting information systems in an ERP environment and tunisian firm performance. International Journal of Digital Accounting Research. 2013;13:1–35. [Google Scholar]
  48. De Lone W., Ephraim M.L. The DeLone and McLean model of information system success: A ten-year update. Journal of Management Information Systems. 2003;19:3–9. [Google Scholar]
  49. DeLone W., Ephraim M.L. Information systems success: The quest for the dependent variable. Information Systems Research. 1992;3:60–95. [Google Scholar]
  50. Duarte P.A., Raposo M.L. Vols. 449–484. Springer; Berlin, Heidelberg: 2010. A PLS model to study brand preference: An application to the mobile phone market. (Handbook of partial least squares: Springer handbooks of computational statistics). [Google Scholar]
  51. Eid, Mustafa M.I., Abbas H.I. User adaptation and ERP benefits: Moderation analysis of user experience with ERP. Kybernetes. 2017;46(3):530–549. doi: 10.1108/K-08-2015-0212. [DOI] [Google Scholar]
  52. Fadelelmoula A.A. The effects of the critical success factors for ERP implementation on the comprehensive achievement of the crucial roles of information systems in the higher education sector. Interdisciplinary Journal of Information, Knowledge, and Management. 2018;13:21–44. [Google Scholar]
  53. Fitrios R. Factors that influence accounting information system implementation and accounting information quality. International Journal of Scientific & Technology Research. 2016;5:192–198. [Google Scholar]
  54. Ghobakhloo M., Adel A., Tang S.H. Business value of enterprise resource planning spending and scope: A post-implementation perspective. Kybernetes. 2019;48(5):967–989. doi: 10.1108/K-01-2018-0025. [DOI] [Google Scholar]
  55. Ghobakhloo M., Tang S.H.T. Information system success among manufacturing SMEs: Case of developing countries. Information Technology for Development. 2015;21:573–600. [Google Scholar]
  56. Hair J., Jeffrey R., Sarstedt M., Christian R. When to use and how to report the results of PLS-SEM. European Business Review. 2019;31(1):2–24. [Google Scholar]
  57. Heo J., Han I. Performance measure of information systems (IS) in evolving computing environments: An empirical investigation. Information & Management. 2003;40:243–256. [Google Scholar]
  58. Hou C.-K. Measuring the impacts of the integrating information systems on decision-making performance and organisational performance: An empirical study of the Taiwan semiconductor industry. International Journal of Technology, Policy and Management. 2013;13:34–66. [Google Scholar]
  59. Hsu P.-F., Yen H.J.R., Chung J.-C. Assessing ERP post-implementation success at the individual level: Revisiting the role of service quality. Information & Management. 2015;52:925–942. [Google Scholar]
  60. Hurt R. 3rd ed. McGraw-Hill; New York: 2013. Accounting information systems: Basic concepts and current issues. [Google Scholar]
  61. Hwang Y., Al-Arabiat M., Shin D.-H., Lee Y. Understanding information proactiveness and the content management system adoption in pre-implementation stage. Computers in Human Behavior. 2016;64:515–523. [Google Scholar]
  62. Idris, Kamil M., Mohamad R. The influence of technological, organizational and environmental factors on accounting information system usage among Jordanian small and medium-sized enterprises. International Journal of Economics and Financial Issues. 2016;6 7S:240–248. [Google Scholar]
  63. Jaafreh A.B. Evaluation information system success: Applied DeLone and McLean information system success model in context banking system in KSA. International Review of Management and Business Research. 2017;6:829–845. [Google Scholar]
  64. Khalid W.E.O. Role of actuarial accounting in enhancing banks performance. Global Journal of Economics and Business. 2020;9(2):452–457. [Google Scholar]
  65. Khassawneh, Lutfi A.A. The influence of organizational factors on accounting information systems (AIS) effectiveness: A study of Jordanian SMEs. International Journal of Marketing and Technology. 2014;4:36. [Google Scholar]
  66. Lin H.-F. An investigation into the effects of IS quality and top management support on ERP system usage. Total Quality Management. 2010;21:335–349. [Google Scholar]
  67. Lin H.-Y., Hsu P.-Y., Ting P.-H. ERP systems success: An integration of IS success model and balanced scorecard. Journal of Research and Practice in Information Technology. 2006;38:215–228. [Google Scholar]
  68. Li Y., Wang J. Evaluating the impact of information system quality on continuance intention toward cloud financial information system. Frontiers in Psychology. 2021;12 doi: 10.3389/fpsyg.2021.713353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Lutfi A. Investigating the moderating effect of Environment Uncertainty on the relationship between institutional factors and ERP adoption among Jordanian SMEs. Journal of Open Innovation: Technology, Market, and Complexity. 2020;6(3):91. [Google Scholar]
  70. Lutfi A. Understanding cloud based enterprise resource planning adoption among SMEs in Jordan. Journal of Theoretical and Applied Information Technology. 2021;99:5944–5953. 10.24473031560656. [Google Scholar]
  71. Lutfi A. Factors influencing the continuance intention to use accounting information system in Jordanian SMEs from the perspectives of UTAUT: Top management support and self-efficacy as predictor factors. Economies. 2022;10:75. [Google Scholar]
  72. Lutfi A. Understanding the intention to adopt cloud-based accounting information system in Jordanian SMEs. International Journal of Digital Accounting Research. 2022;22:47–70. [Google Scholar]
  73. Lutfi, Abd A., Kamil M.I., Mohamad R. AIS usage factors and impact among Jordanian SMEs: The moderating effect of environmental uncertainty. Journal of Advanced Research in Business and Management Studies. 2017;6:24–38. [Google Scholar]
  74. Lutfi A., Alshira’h Ahmad F., Alshirah M.H., Al-Okaily M., Alqudah H., Saad M., Ibrahim N., Abdelmaksoud O. Antecedents and impacts of enterprise resource planning system adoption among Jordanian SMEs. Sustainability. 2022;14:3508. doi: 10.3390/su14063508. [DOI] [Google Scholar]
  75. Lutfi A., Al-Khasawneh A.L., Almaiah M.A., Alshira’h Ahmad F., Alshirah M.H., Alsyouf A., Alrawad M., Al-Khasawneh A., Saad M., Ali R.Al. Antecedents of big data analytic adoption and impacts on performance: Contingent effect. Sustainability. 2022;14 [Google Scholar]
  76. Lutfi A., Al-Khasawneh A.L., Almaiah M.A., Alsyouf A., Alrawad M. Business sustainability of small and medium enterprises during the COVID-19 pandemic: The role of AIS implementation. Sustainability. 2022;14:5362. doi: 10.3390/su14095362. [DOI] [Google Scholar]
  77. Lutfi A., Al-Okaily M., Alshirah M.H., Alshira’h A.F., Abutaber T.A., Almarashdah M.A. Digital financial inclusion sustainability in Jordanian context. Sustainability. 2021;13(11):6312. [Google Scholar]
  78. Lutfi A., Al-Okaily M., Alsyouf A., Abdallah A., Abdallah T. The impact of AIS usage on AIS effectiveness among Jordanian SMEs: A group analysis of the role of firm size. Global Business Review. 2020;21:1–19. doi: 10.1177/0972150920965079. [DOI] [Google Scholar]
  79. Lutfi A., Al-Okaily M., Alsyouf A., Alrawad M. Evaluating the D&M IS success model in the context of accounting information system and sustainable decision making. Sustainability. 2022;14:8120. doi: 10.3390/su14138120. [DOI] [Google Scholar]
  80. Lutfi A.S., Alkelani N., Al-Khasawneh M.A., Alshira’h Ahmad F., Alshirah M.H., Almaiah M.A., Alrawad M., Alsyouf A., Saad M., Ibrahim N. Influence of antec accounting system usage on SMEs performance: The moderating effect of COVID-19. Sustainability. 2022;14 doi: 10.3390/su142215048. [DOI] [Google Scholar]
  81. Lutfi A., Alkelani S.N., Al-Khasawneh M.A., Alshira’h A.F., Alshirah M.H., Almaiah M.A.…Ibrahim N. Influence of digital accounting system usage on SMEs performance: The moderating effect of COVID-19. Sustainability. 2022;14(22) [Google Scholar]
  82. Lutfi A., Alkelani S.N., Alqudah H., Alshira’h Ahmad F., Alshirah M.H., Almaiah M.A., Alsyouf A., Alrawad M., Montash A., Abdelmaksoud O. The role of E-accounting adoption on business performance: The moderating role of COVID-19. Journal of Risk and Financial Management. 2022;15(12):617. [Google Scholar]
  83. Lutfi A., Alkilani S.Z., Saad M., Alshirah M.H., Alshirah A.F., Alrawad M., Al-Khasawneh M.A., Ibrahim N., Abdelhalim A., Hashim Ramadan M. The influence of audit committee chair characteristics on financial reporting quality. Journal of Risk and Financial Management. 2022;15:563. doi: 10.3390/jrfm15120563. [DOI] [Google Scholar]
  84. Lutfi A., Alrawad M., Alsyouf A., Almaiah M.A., Al-Khasawneh A., Al-Khasawneh A.L., Alshira'h A.F., Alshirah M.H., Saad M., Ibrahim N. Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling. Journal of Retailing and Consumer Services. 2023;70 [Google Scholar]
  85. Lutfi A., Alsyouf A., Almaiah M.A., Alrawad M., Khalil Abdo A.A., Al-Khasawneh A.L., Ibrahim N., Saad M. Factors influencing the adoption of big data analytics in the digital transformation era: Case study of Jordanian SMEs. Sustainability. 2022;14:1802. doi: 10.3390/su14031802. [DOI] [Google Scholar]
  86. Lutfi A., Ashraf M., Ahmad Watto W., Alrawad M. Do uncertainty and financial development influence the FDI inflow of a developing nation? A time series ARDL approach. Sustainability. 2022;14 [Google Scholar]
  87. Lutfi A.A., Kamil Md I., Mohamad R. The influence of technological, organizational and environmental factors on accounting information system usage among Jordanian small and medium-sized enterprises. International Journal of Economics and Financial Issues. 2016;6 7S:240–248. [Google Scholar]
  88. Lutfi A., Saad M., Almaiah M.A., Abdallah A., Al-Khasawneh A., Alrawad M., Alsyouf A., Al-Khasawneh A.L. Actual use of mobile learning technologies during social distancing circumstances: Case study of king faisal university students. Sustainability. 2022;14:7323. doi: 10.3390/su14127323. [DOI] [Google Scholar]
  89. Marble R.P. A system implementation study: management commitment to project management. Information & Management. 2003;41(1):111–123. [Google Scholar]
  90. Negash S., Ryan T., Igbaria M. Quality and effectiveness in web-based customer support systems. Information & Management. 2003;40:757–768. [Google Scholar]
  91. Nelson R., Todd P., Wixom B. Antecedents of information and system quality: An empirical examination within the context of data warehousing. Journal of Management Information Systems. 2005;21:199–235. [Google Scholar]
  92. Nguyen H.T., Anh N. Determinants of accounting information systems quality: Empirical evidence from Vietnam. Accounting. 2020;6:185–198. [Google Scholar]
  93. Okon E.E., Evans O., Samuel D. Effect of human resource in accounting information system on management decision-making in seventh—day adventist institutions in eastern Nigeria. Advances in Social Sciences Research Journal. 2021;8:1–11. [Google Scholar]
  94. Ouiddad, Ahmed, Chafik, Chroqui R., Hassani I.B. Paper presented at 2018 IEEE international conference on technology management, operations and decisions (ICTMOD), marrakech, Morocco, november 21–23. IEEE; Piscataway: 2018. Does the adoption of ERP systems help improving decision-making? A systematic literature review; pp. 61–66. [Google Scholar]
  95. Petter S., DeLone W., McLean E. Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems. 2008;17:236–263. doi: 10.1057/ejis.2008.15. [DOI] [Google Scholar]
  96. Petter S., McLean E. A meta-analytic assessment of the DeLone and McLean IS success model: An examination of IS success at the individual level. Information & Management. 2009;46:159–166. [Google Scholar]
  97. Quintero J.M.M., García Pedroche E., María I.G.R. Influence of the implementation factors in the information systems quality for the user satisfaction. Journal of Information Systems and Technology Management. 2009;6:25–44. [Google Scholar]
  98. Rabbani M.R., Lutfi A., Ashraf M.A., Nawaz N., Ahmad Watto W. Role of artificial intelligence in moderating the innovative financial process of the banking sector: a research based on structural equation modeling. Frontiers in Environmental Science. 2023;10:2083. [Google Scholar]
  99. Ramli A. The impact of external factors on accounting information system (AIS) usage. Journal of Entrepreneurship and Business (JEB) 2013;1:1–14. [Google Scholar]
  100. Saad M., Lutfi A., Almaiah M.A., Alshira’h A.F., Alshirah M.H., Alqudah H., Alkhassawneh A.L., Alsyouf A., Alrawad M., Abdelmaksoud O. Assessing the intention to adopt cloud accounting during COVID-19. Electronics. 2022;11:4092. doi: 10.3390/electronics11244092. [DOI] [Google Scholar]
  101. Saarinen T. An expanded instrument for evaluating information system success. Information Management. 1996;31(2):103–118. [Google Scholar]
  102. Sekaran U., Roger B. 5th ed. John Wiley & Sons; West Sussex: 2010. Research methods for business. [Google Scholar]
  103. Sekaran U., Roger B. John Willey & Sons Ltd; West Sussex: 2013. Research methods for business: A skill-building approach. [Google Scholar]
  104. Shatnawi S., Marei A., Daoud L., Alkhodary D., Shehadeh M. Effectiveness of the board of directors' performance in Jordan: The moderating effect of enterprise risk management. International Journal of Data and Network Science. 2022;6(3):823–836. [Google Scholar]
  105. Syahidi A.A., Arifin N.A. Vol. 407. IOP Publishing; 2018. Strategic planning and implementation of academic information system (AIS) based on website with D&M model approach. (IOP conference series: Materials science and engineering. Bristol). August. [Google Scholar]
  106. Tajuddin M. Modification of DeLon and mclean model in the success of information system for good university governance. Turkish Online Journal of Educational Technology-TOJET. 2015;14:113–123. [Google Scholar]
  107. Thong J., Yap C.-S. Information systems effectiveness: A user satisfaction approach. Information Processing & Management. 1996;32(5):601–610. [Google Scholar]
  108. Trice A., Treacy M. Utilization as a dependent variable in MIS research. ACM SIGMIS - Data Base: The Database for Advances in Information Systems. 1988;19:33–41. [Google Scholar]
  109. Wixom B., Todd P. A theoretical integration of user satisfaction and technology acceptance. Information Systems Research. 2005;16:85–102. [Google Scholar]
  110. Xu J., Benbasat I., Cenfetelli R. Integrating service quality with system and information quality: An empirical test in the e-service context. MIS Quarterly. 2013;37:777–794. [Google Scholar]
  111. Yakubu N., Dasuki S. Assessing eLearning systems success in Nigeria: An application of the DeLone and McLean information systems success model. Journal of Information Technology Education: Research. 2018;17:183–203. [Google Scholar]

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