The effect of business intelligence on the quality of decision making in construction industry firms in Jordan

Business Intelligence (BI) 20

Business Intelligence (BI) can lead to increased organizational performance. A corporation may stay competitive in a rapidly changing market by optimizing its performance and adapting to new difficulties (Jamaludin & Masor, 2011). When a firm is strapped for cash or has limited resources, business information may be a business saver A business intelligence tool may help supply information, such as reporting and consolidation, that can be targeted toward high-impact or high-value prospects (Bassellieret et. al, 2003). Organizations may benefit from the usage of business intelligence and analytics to get a competitive edge over their rivals by making better, more informed choices inside the firm (Bassellieret et. al, 2003).

Governments and businesses may employ BI and data analytics to gain a significant competitive edge in unpredictable economic times with constrained resources. BI solutions enable a business to concentrate on the most important outcomes and deliver on time and on budget. As a result, quality, accuracy, and a better understanding will be achieved to enhance competitive advantage. In several ways, business intelligence technologies may aid in maintaining a competitive edge.

In an era of intense competition and with the future being more reliant than ever on data, teams must adopt BI or risk losing lucrative contracts to competitors. Until recently, construction companies relied on either paper filing or the increasingly prevalent use of personal computers. Although these old methods were widely accessible, they were inefficient, restricted in the amount of data they could simultaneously represent, prone to human mistake or loss, and space intensive. The advent of BI will be the key to addressing these issues.

Various groups engage in decision-making nowadays (stakeholders, customers, suppliers, etc.). In many instances, the scope of a specific choice is global. Regional and International interdependencies need a greater interchange of information and sharing of expertise, as well as better coordination of actions, as opposed to everything that has occurred in the past (Viehland, 2005). Organizations that want to improve the quality of their decisions, their public image, or the service they give to their partners should focus on building an IT infrastructure that will take a holistic view of business operations, customers, suppliers, etc (Wells & Hess, 2004). This study will focus on the impact of business intelligence on the quality of decision-making in construction industry firms in Jordan.

The interest in civilian rehabilitation initiatives has risen dramatically in recent decades. This is particularly true in developing countries, where aging buildings and obsolete infrastructures, such as electricity, water and sewage, telecommunications, and transportation, are causing social and economic concerns (Kumaraswamy & Zhang, 2001). The construction sector is a very competitive one, with high levels of risk and low-profit margins (Enshassi et al. 2013). In such a competitive climate, the success or failure of a single project may have a significant impact on the company's long-term viability. As a result, improving performance is critical to the construction company's long-term viability and competitive advantage.

In response to the improving performance especially for managers and their businesses, companies have invested heavily in BI systems (Hou, 2012). BI is aimed at displaying an organization's information assets for understanding business dynamics and making better decisions by integrating information from numerous sources (Aruldoss et al., 2014; Li et al., 2008). BI is a method used by businesses and organizations to enhance their decision-making processes. It starts with identifying information requirements, then gathers data on that information, applies analytical methods to turn the data and information into intelligence, and then makes those intelligence products accessible to individuals who must make choices.

There has been a lot of literature written on the decision-making model to explore the practice of thinking frameworks in making strategic decisions in businesses (Negulescu & Doval, 2014)Some of them use quantitative and qualitative approaches to help decision-makers determine and clarify different criteria (Azemi et al., 2018; Negulescu & Doval, 2014). The importance of time and knowledge in influencing the result of choices is highlighted by the quality of decision-making, particularly in the setting of a family company where the fundamental stakeholders of the Company are the core family.

In Jordan, a rising nation afflicted by a growing population and political instability in the area, the construction industry is a crucial sector for addressing these difficulties (Sweis et al. 2016). In addition, this industry provides around 4.5 percent of the national gross domestic product (Albalkhy et al., 2021). However, the Jordanian construction industry is plagued by several issues that restrict its contribution to the country's sustainable growth. These include but are not limited to, safety-related issues, delays, poor quality, performance, and productivity, unskilled labor, cost overruns, and rework (Alkilani et al., 2013; Al-Momani, 2000; El-Mashaleh et al., 2010; Sweis, 2013).

Consequently, there is a need for a shift in Jordan's approach to construction project management. As a result, BI, as a concept that has shown success in several projects and locations throughout the world, could be one of the essential answers to these difficulties. In reality, there is relatively little research and study on BI adoption in Jordan (Albalkhy et al., 2021; Sweis et al., 2016). More recently, the role of BI has come to be more understood in helping the construction sector to start an industrial revolution, which has the potential to enhance construction methods and results. This research examines the Kingdom of Jordan as a case study to explore the impact of BI on the quality of decision-making in construction industry firms. The impact of integrating business intelligence on decision-making quality was addressed in different studies (Herschel & Jones, 2005; Marshall & De la Harpe, 2009; Tvrdíková, 2007). Little of these studies addressed the impact of business intelligence on decision-making in construction industry firms, and none addressed this in Jordan.

Jordan is in the heart of the Middle East. Recent evidence demonstrates that the ongoing -Arab-Israeli conflict and the Gulf War have caused a wide range of economic problems in the Middle East and had a significant impact on the economy. As a result, Jordan is vulnerable to external negative influences , and regional unrest, a situation that is exacerbated by its limited natural resources. This means that organizations must make decisions quickly and correctly in response to changes that were not expected or planned for. So, in this case, the most important tool to think about is BI.

This study will benefit both academics and the industry. It benefits the industry by providing fresh insights for managers on the impact of business intelligence on high-quality decision-making. These insights might be used as a guideline for businesses to understand the main variables that should be addressed when opting to enhance decision-making quality in the construction industry in Jordan. Also, it benefits academics by gaining expertise and staying updated on up-to-date information to be used for future research.

A survey will be used as the study's data collection. Different demographic variables like Age; Gender; Educational level; and Experience will be collected. The primary source of data in the current study is a questionnaire that is intended to reflect the themes of the study. A random sampling of the upper and middle management of construction firms and the inclusion of competent adults in the sample will be used to ensure the research's quality and validity and breadth. The findings may provide investors and business owners with effective and beneficial insights into BI technologies and functions to achieve more idealistic organizational advantages. It also allows managers to gain a better understanding of how BI functions are used in the process of obtaining the desired managerial support advantages.

The main tenant of this section of the research study is to determine the theory, which will be used as the ground framework to develop the research questions, objectives, and hypotheses of the study. Since this study is based on determining the role of technological innovation, such as Business intelligence (BI) in improving the quality of the decision-making process in the construction industry of Jordan, the "Technological Acceptance Model” (TAM), which is an adaption of “Theory of Reasoned Action,” in the field of Information System (IS) will be employed. In the Technological Acceptance Model, according to Davis (1989), a person's perceptions of a system's usability, utility, and ease of use, play a significant role in determining the level of interest that individuals have in employing that system, and (the behavioral intention to use) factor plays as a mediator to reach the actual usage (as in the case of Business Intelligence) (Fang et al., 2018).

It is generally accepted in Jordan's construction industry that the perceived ease of use of a system has a direct link with how helpful that system is considered. This is the case for a variety of reasons, including The Technological Acceptance Model has been simplified since the attitude component was removed from the definition of the Theory of Reasoned Action. In addition, initiatives to expand TAM is permitted and have often taken one of these three routes: adding components from related models, introducing new or alternative belief elements, or investigating the variables that influence how people see the usefulness and accessibility of business intelligence (Ul-Ain, Vaia,& DeLone, 2019). For example, a study that was conducted by Verma, Bhattacharyya, and Kumar (2018) proved that the construct connections of the Technological Acceptance Model are mostly based on semantic correlations between the questionnaire items. In other words, it can be stated that this specified theoretical model mainly suggests that any technology, which has the potential to get easily accepted and acknowledged is required to be implicated in the practical setting. In this way, this specified theoretical model helped the researcher to provide a ground framework to assess the application of Business Intelligence (BI) in construction firms functioning in Jordan. since Business Intelligence is a highly commendable technology, this technological advancement dares to get accepted by construction firm's workers and stakeholders in Jordan.

According to Saunders et al. (2007), the quantitative method was widely used as a synonym for any data-gathering tool (such as a questionnaire) or data analysis strategy (such as graphs or statistics) that created or used numerical data. This study is considered a quantitative cross-sectional study. In general, quantitative methodologies study conditions or events that influence individuals. Quantitative research generates objective facts that can be expressed using statistics and figures, which is suitable for this kind of study (Bloomfield & Fisher, 2019).

A survey will be used to collect data. Questionnaires can be an effective means of measuring preferences and opinions. The questionnaire data will be analyzed, then statistically translated into useful conclusions and graphed. Quantitative analysis tools, such as graphs, charts, and statistics, allow closed-ended questionnaire data to be turned into relevant information, according to Saunders et al. (2007). To increase credibility, it is important to choose a sample that will represent the population under investigation. The planned sample size for this study is about 120 participants. This will give enough to determine a result on a local or regional level. It will be allowed for managers aged 25–70 with experience ranging from 1 to 35 years, which will increase the variability, thus lowering internal and external threats (Galaitsi et. al, 2021).

Managerial decision-makers will initially be asked to fill out a survey requesting them to rate the functionality commonly used by them during data analysis and decision-making processes. Also, the survey will include some background questions. The collected data will be used to draw a comparative analysis of the effective use of BI in the decision-making process. The results will be analyzed with the Statistical Package for Social Sciences (SPSS) using statistics, chi-square, t-tests, and Pearson’s product-moment correlations. Cronbach’s alpha coefficients will also be used to examine instrument reliability. A multiple regression analysis will be conducted using the SPSS software. This analysis will help test relationships between more than one independent variable and the dependent variable at the same time (Field, 2013).

Business intelligence (BI) tools can manage vast volumes of data, both organized and unstructured, to discover, develop, and create new possibilities for strategic business. They make an effort to make the understanding of large data sets simpler. Businesses have the potential to achieve a competitive edge, long-term sustainability, and strategic alternatives by recognizing new possibilities and putting into action an efficient plan that is founded on insights.

Based on earlier information, the study’s problem may be demonstrated by considering the questions below:

RQ1

Is there a statistically significant positive direct impact of BI on decision-making quality in construction industry firms in Jordan?

Is there a statistically significant positive direct impact of BI on information quality in construction industry firms in Jordan?

RQ3

Is there a statistically significant positive direct impact of information quality on decision-making quality in construction industry firms in Jordan?

H10

H1a

There is a relationship between BI and decision-making quality in construction industry firms in Jordan (α ≤ 0.05). Comment by The Iammartino Family: This comment is for all hypotheses. You do not need to assume a positive or negative direction in hypotheses. Focus should be.."There is a relationship, There is not a relationship"

H2a

There is a relationship between BI and information quality in construction industry firms in Jordan (α ≤ 0.05).

H30

There is no relationship between information quality and decision-making quality in construction industry firms in Jordan (α ≤ 0.05).

H3a

There is a relationship between information quality and decision-making quality in construction industry firms in Jordan (α ≤ 0.05).

The significance of the current study arises from the important role of the construction sector in improving the economy in Jordan. The research adds to both academics and business by demonstrating for the first time the direct influence of managerial decision quality enhancements associated with the active management of BI. This research fills a gap in the literature by investigating a sector of the economy that has not been well examined in Jordan: the application of business intelligence in the construction sector. The research also will examine what special characteristics the BI has and how these features affect the correct decision that can be taken with the available data at the appropriate time according to the environment.

BI is still not widely used in developing countries, like Jordan. Business intelligence is one of the most important things that can boost the construction industry's drive for innovation and creativity. Many studies have shown that using business intelligence in companies improves individual business activities like marketing (Xu et al., 2017) and customer relationship management (Nam et al., 2019), which in turn improves the overall performance of the company (Bronzo et al., 2013; Vuki et al., 2013). If Business intelligence is used properly in Jordan, it will have the potential to speed up and improve basic business leadership and decision-making. This increases operational productivity, brings in new income, and gets the upper hand over business competitors. BI systems can also help businesses figure out what's going on in the market and what business problems need to be solved.

Business Intelligence

BI is a collection of procedures, systems, and technology that transform unstructured data into information that drives lucrative business activities. It is a collection of software and services that converts data into insight and knowledge that can be put into action (Gurjar & Rathore, 2013).

Decision Making

Decision-making is the process of determining the best course of action by recognizing the need for a choice, collecting relevant data, and assessing potential answers (Nakagawa et al., 2011).

Stopped Here May 21, 2023

It refers to the state of a choice at the time it is made, not the consequence. When assessing a choice issue, decision quality principles allow for the guarantee of both efficacy and efficiency in a changing and uncertain setting (Wen et al., 2014).

Information Quality

Information quality is the level of intelligence possessed by a system which is directly proportional to the caliber of the information it stores. It is defined by the things people want from an information system and the kind of information they want. It is the completeness, accuracy, format, and timeliness of the information that is made by the system (DeLone & McLean, 2003).

Construction Industry

The construction industry is the part of manufacturing and trade that builds, fixes, renovates, and takes care of infrastructures. It affects the country's technological and technical progress and often controls the growth of the country's infrastructure development, which in turn often leads to the country's progress in terms of ensuring sustainability (Osmani, 2011).

Indicators of the well-being of a country's economy can be found in the construction industry, which encompasses a wide range of activities from the design process to building construction. However, this sector faces many risks, and these risks can affect the implementation of the project and a reduction in the quality of decision-making. Business Intelligence (BI) is becoming an integral aspect of many business sectors, especially the information technology (IT) industry. Business Intelligence is a framework of ideas, practices, tools, and techniques that help in understanding the potential of the company's core and footage from the situation. So, the purpose of this research is to study the impact of BI on the quality of decision-making in construction industry firms in Jordan.

The quantitative research technique is used. The independent variable will be BI, and the dependent variable will be the quality of decision-making in construction industry firms in Jordan. A survey will be administered to the managerial decision-makers in Jordan. SPSS will be used to examine the assumptions. It's important for businesses to know the main benefits of each BI function so they can use the ones that are best for their business needs and follow the pattern of both strategy and experience.

The findings may provide light on how investors and project managers might better use suitable BI tools and functions to achieve desired organizational benefits. This specified research has proposed the paradigm of proposing the concept of Business Intelligence (BI), which uses digital technology to enhance the planning and forecasting process to help the construction firms in improving the overall management of the construction projects. Hence, this project is intended to assess the direct impact of information quality on decision making quality in construction industry firms in Jordan.

Peer-reviewed, scientific publications published within the last five years were among the research criteria utilized to find articles. SPRINGER, Google Scholar, and NCU library will be the main search engines used in collecting information from scholarly journals and peer-reviewed articles for the literature search strategy. To fulfill the literature review of such an exploratory study, relevant books, and websites regarding BI and the technology acceptance model theory will be used. Most of the studies included in the literature review were written or published over the past five years, which means that they are recent. To retrieve articles, the following phrases and keywords were utilized: business intelligence, decision-making, quality of decision-making, technological acceptance model, the impact of Business Intelligence on the quality of decision-making, Business Intelligence in construction industry firms, the construction industry in Jordan, and information technology.

All of the references used in this study will examine the influence of intelligence on decision-making or demonstrate how a particular sub-competence within each intelligence will be critical to a successful construction industry. Over the last two decades, both the academic and industrial sectors have endeavored to comprehend and cultivate the abilities necessary for efficient decision-making in the ever-changing global business environment (VanderPal, 2014).

Theoretical Frameworks

Technology Acceptance Model

The technology acceptance model (TAM) model will be used as the ground-grounded framework to develop the research questions, objectives, and hypotheses of this study. So, it is important to discuss the main components of such a model. The TAM model had been utilized as the theoretical base as recommended by Bach, Eljo, and Zoroja (2016), who called for assessing and evaluating the relationship between BI and perceived ease of implementation and perceived usefulness. The TAM model, which had been developed by Davis (1989), was originally derived from Fischbein and Ajzen's (1977) TRA (reasoned action) theory and contains the significant core variables of user motivation (perceived usefulness and perceived ease of use).

In their study, Marangunić and Granić (2014) indicate that TAM developed by Fred Davis several years ago is considered one of the common models in examining factors influencing the acceptance of users towards using technology. TAM postulates the mediating role of two variables, namely, perceived usefulness and perceived ease of use in a complicated relationship between system features (external variables) as well as potential system utilization. TAM is derived from (TRA), which stands for the theory of reasoned action as well as (TPB), which stands for the theory of planned behavior. It plays a critical role in clarifying the behavior of the users towards using the technology. Lee and Lehto (2013) found that TAM robust and valid model that has been commonly used; thus, suggesting its potentially wider applicability. On the other hand, other scholars have different opinions. To elaborate, Chutter (2009) argues that TAM lacks rigorous and sufficient research. In addition, Holden and Karsh (2010) suggest making some modifications and additions to TAM.

A technology acceptance model is more specifically formulated around the prediction of the satisfactoriness and appropriateness of an information system. The major driver of this model is to predict the agreeableness of a tool and identify the adjustments that must be made to the framework to make it tolerable and conventional for the intended users. TAM is considered an applicable and commonly applied theory for the management information context (Chen, Li, and Li, 2013). Therefore, TAM has unfailingly garnered scholars' attention over the last decades.

The rationale behind selecting TAM in this study in particular is attributed to the fact that TAM is regarded as the most commonly used framework in the information systems for examining the acceptance of technology/ information systems (Durodolu, 2016; Diop et al., 2019). Besides, TAM theory is considered a robust theory that is beneficial for explaining a particular technology or information system (Chen, Li, and Li, 2013). The literature reveals that this theory started in 1985, particularly after the development of the theory of reasoned action (TRA) (Fishbein and Ajzen, 1975; Fishbein, 1967).

As mentioned by Sonika and Phiri (2019), the TAM model pays much attention to the users' perceptions, suggesting that there are two major influencers (variables) that determine whether a specific or suggested system will be accepted by its intended users: (1) perceived usefulness and (2) perceived ease of use. That is, it is both a factor to consider (belief) and the ease with which the system can be adopted and used (user-friendly). TAM is of paramount importance for evaluating the user's acceptance of a model (Davis, 1989).

It is worth mentioning that TAM consists of six factors, perceived ease of use, perceived usefulness, behavioral intention, external factors, actual system use, and attitudes toward the new technology (Davis, 1989). Regarding the two factors; perceived ease of use and perceived usefulness that suggest that the system will not deem appropriate if the users are not satisfied with using it (Al Breiki and Al-Abri, 2022), in turn, both of the above-mentioned factors will trigger the behavioral intention in terms of using or not using the system (Nov and Ye, 2008). Three variables shape the user's actual utilization of a system; perceived, behavioral intention, and perceived ease of use (Park, 2009). TAM further reveals that a user's acceptance of a technology system depends on two factors, namely, perceived ease of use and perceived usefulness (Davis, 1989).

Regarding perceived ease of use, He, Chen, and Kitkuakul (2018) stated in their study that perceived-ease-of-use is mainly related to an individual opinion about how the new approach/system or technology is easy to use (He, Chen, and Kitkuakul,2018). He, Chen, and Kitkuakul, (2018) identified the perceived ease of use as a significant attack of TAM. As a result, perceived ease of use has been measured a prompt to adopt new technological development by the students (Arora and Kaur, 2018).

Respect perceived usefulness is defined as the extent to which the person contends that employing a specific information technology or information system might promote the individual's live performance, along with improving his/her job (Chen, Li, and Li, 2011). Behavioral intention (BI) means the extent of the individual's belief regarding his/her continuity towards using the system (Venkatesh, Thong, and Xu, 2012). Several studies (Taylor and Todd, 1995; Tractinsky, 2017) suggest that behavioral intention in ATMs is affected by two primary factors, namely, perceived ease of use and perceived usefulness. Both of them play a pivotal role in constructing BI behavioral intention (Binyamin, Rutter, and Smith, 2019; Binyamin, Rutter, and Smith, 2013). To sum up, TAM postulates that individuals' use of language is affected by the usefulness and the easiness of the technology. In addition, the behavioral intention (BI) to use technology determines the actual use of technology, which in turn, BI is influenced by two aspects, namely, perceived usefulness and attitude toward using technology (Siwale, 2022).

A plethora of studies on BI adopted TAM as a theoretical and conceptual framework to examine the relationship among variables. To elaborate, Bach, Eljo, and Zoroja (2016) proposed a research framework relying on TAM, which was expanded using the notions of information quality, project management, and technology-driven strategy. In the present study, the Technological Acceptance Model (TAM) is used as a conceptual framework to investigate the effectiveness of BI on the quality of decision-making in construction firms in Jordan.

Business Intelligence (BI)

Before delving into the definition of BI, it is important to note that it emerged as a result of significant advancements in communication and information technology. A variety of institutions use BI to achieve the following goals: dealing with business mechanisms development, competition, customer possession and retention, and remaining at the forefront of the marketplace (Zabadi, Alshura, and Rahim, 2015). BI means a group of software platforms that enhance organizations' retrieval of valuable knowledge and information to enhance the efficiency of decision-making (Bach, Celjo, and Zoroja, 2016). According to Al-Azmi (2013), BI is defined as obtaining information through mining. In their study, Zabadi, Alshura, and Rahim (2015) defined BI as a set of comprehensive processes and tools that are coherent and integrated and are utilized to extract, collect, update, store, and analyze information and data to provide support and information presented to take business-related decisions.

Alshura and Rahim (2015) defined BI as a set of comprehensive processes and tools that are coherent and integrated and are utilized to extract, collect, update, store, and analyze information and data to provide support and information presented to take business-related decisions. The authors have further defined BI as a methodology, theories, characteristics, techniques, and processes that rely on converting raw data into beneficial and meaningful information for business purposes. BI can handle a huge amount of information to help organizations in determining and improving new business opportunities, take advantage of new opportunities, and implement an efficient strategy. According to Shi, Peng, and Xu (2012), BI is defined as a set of effective approaches and tools used to increase the value of the enterprise and to improve business operations and executive decision-making.

According to Huie (2016), BI is a rapidly evolving technology that is commonly used in a variety of organizations to convert data into beneficial information and distribute such information when necessary. It is worth mentioning that Wieder and Ossimitz (2015) define BI as an analytical technology process that collects and transforms the fragmented data of firms and markets into knowledge or information regarding the opportunities, goals, and positions of the organizations.

According to Rouhani, Asgari, and Mirhosseini (2012), BI is considered a comprehensive concept that includes a broad range of instruments, techniques, and solutions that enable managers to grasp the business situation. Several scholars (Gilad & Gilad, 1986; Ghoshal & Kim, 1986) have defined BI as a tool and a managerial philosophy utilized to help organizations refine and manage business information to make influential business decisions. Lönnqvist and Pirttimäki (2006) argued that BI is a systematic and organized process that enables organizations to acquire, disseminate, and analyze significant information from two information sources, internal and external, that are essential for both business activities and DM. For Persson (2017), BI is defined as a decision-support system for institutions to enhance their business and improve their competitiveness. Olszak and Zurada (2015) opine that BI is essential for the success of a business for various organizations. BI mirrors a greater strategic tendency and utilization of information (Frates and Sharp, 2005).

Regarding the use of BI, the process of BI is used by organizations to add value, collect information, and submit the results to managers to solve a huge range of problems or to meet the demands for information. The projects of BI vary from competitive information concerning customers or rivals to knowledge about recruiting, acquisitions, and mergers. Knowing that the required answers to satisfy such requests contain biographies, demographics, financial information, news articles, economic indicators, and competitor and customer information.

According to Negash and Gray (2008), BI combines analytic tools with operational data to provide competitive and complex information to decision-makers and planners. BI has recently gained widespread attention in the domain of information technology and communication in the majority of companies to enhance the performance of the organization (Urumsah and Ramadhansyah, 2019). They add that the main goal of BI tools lies in converting the data into valuable and meaningful information. Sohollo (2011) argues that BI means a process of applying techniques and tools to collect and analyze the collected information from various resources (internal and external) to establish knowledge that helps in DM. Regarding the input factors of BI, Huie (2016) points out that quality of information, reliability, frequency of use, accessibility, security, quality of decisions, and relevance are the main input factors.

Othman (2021) has provided a comprehensive definition of BI as a modern instrument that is distinguished by its ability to promote the competitiveness of the institution and enhance its performance using advanced and modern technological capabilities during the process of gathering, processing, organizing, and generating data from its sources due to its ability to enhance the management of the organization and operation of the business and enhance its competitive stance. Several studies (Jenster and Silen, 2013; Fourati-Jamoussi and Niamba, 2016) show that BI is a suitable solution for unexpected events, whether small or large, that may harm the business.

To trace back to the emergence of BI, an American professor named Vilensky was the pioneer of proposing the concept of BI in 1967; he opines that intelligence entails data collection and information processing to identify the correct organization (Peterson, 1968). It should be mentioned that the boundary of business practice that was expanded due to the development of artificial intelligence technology induced the prominence and the application of BI, which has enhanced the transformation of information techniques to maximize business operations and business decisions (Chen and Len, 2021). On the other hand, Chen, Chiang, and Storey (2012) point out that the BI system has emerged due to recent information system development and technological development. Several studies (Cheung and Li, 2012; Anandarajan et al., 2004) indicate that BI emerged in the mid-nineties to respond to the significant changes in the competitive environment, the massive development of technology, and the global diffusion of the internet.

BI has unfailingly garnered the attention of decision-makers and executives because of its ability to provide competitive and complex information input for the decision process (Ain et al., 2019). Niu et al. (2021) argue that BI helps in gathering vital information from a diverse range of unorganized data that is converted into enforceable information, which permits firms to improve business productivity and efficiency by making informed policy decisions. The main tasks of BI include aggregation, integration, intelligent exploration, and multidimensional analysis of data yielded from several information resources (Mohammad, 2012). However, some challenges hinder the application of BI and decision-making in any organization, namely, risk-taking capability, lack of preparation, plan failure, and resource failure (Niu et al., 2021).

The main objective of BI lies in familiarizing the decision-makers with the threats, opportunities, and changes in the business environment of the company to create a competitive advantage and enable them to make optimal decisions (Vuori, 2007). In their study, Ramakrishnan et al. (2012) found that BI improves performance, supports decision-making, and provides useful insights. To achieve BI in an organization, mining tools, including text mining, web mining, and data mining, are employed to detect hidden knowledge, find hidden relations, and predict future events. Such hidden information helps in building better customer relationships, detecting fraud, and gaining a competitive advantage. Therefore, enterprises depend on a set of automated instruments for discovering knowledge to gain business intelligence and insight (Al-Azmi, 2013).

Regarding the effect of BI on an organization, Jahantigh, Habibi, and Sarafrazi (2019) indicate that BI is highly important in an organization because it enables the organization to gain a sustainable competitive advantage and develop its capabilities. The present study is concerned with BI and its effects on the quality of decision-making in the management and construction industries in Jordan. This study seeks to explore the direct and indirect effects of BI on the quality of decision-making (Wieder and Ossimitz, 2015). Chaudhuri, Dayal, and Narasayya (2011) suggest that the technologies of BI are significant in running the business.

Along similar lines, Azma and Mostafapour (2012) conducted a study on the effectiveness of BI in the development of the organization because it is considered the key strategy for any organization to attain a competitive advantage and enhance profitability by employing intelligent and precise decision-making. They add that the main features of BI are organizational learning and smart processing. On the other hand, they indicate that the main components of BI are data sources and data marts. More importantly, they summarized the implementation of the BI process into four steps, namely, planning and conducting the steps, obtaining information from the database, processing the information, and analyzing and producing the information. In conclusion, they summarize the rationale and the benefits of applying BI in organizations by indicating that BI plays a significant role in saving money, energy, and time; increasing the organization’s income; elevating customers’ satisfaction; and enhancing customers’ loyalty.

Weng et al. (2016) indicate that the tools of BI are constantly developing, but the ability to apply BI technologies constitutes an integral resource for organizations running in a dynamic, uncertain, and complicated business environment. The primary reason behind implementing BI is attributed to the fact that BI facilitates the interactions among information sharing and data management to equip managers and analysts with task implementation (Elbashir et al., 2013; Turban et al., 2010). Practitioners and scholars have claimed that BI execution has several impacts (Weng et al., 2016). To elaborate, the success of BI entails achieving a variety of benefits; including, improved efficiency, reduced costs, and improved profitability (Işık et al., 2013). The capabilities of BI might enable the organization to enhance overall performance and agility in facing the change (Brands, 2014; Watson and Wixom, 2007).

Ranjan (2009) investigated the concepts, components, and benefits of BI. The BI concept of the study has two-fold meanings; the former means the applied capacity of human intelligence in business activities/affairs. The latter means that intelligence information is valued for its relevance and activity. The study indicated that there are five components of BI, namely, online analytical processing, advanced analytics, corporate performance management (dashboards, portals, and scorecards), and data marts as well as a data warehouse, As for the benefits of BI, it reduces the guesswork in the organization, promotes communication within departments by coordinating activities, BI enhances the general performance of the companies implementing it, and it enables the companies to quickly respond to the alterations in supply chain operations, customer preferences, and financial conditions.

A survey study was conducted by Al-Azmi (2013) to explore text, data, and web mining for BI. The study indicated that data mining, text mining, and web mining are utilized to find hidden knowledge on the internet or in large databases. These automated software tools are utilized to achieve BI by predicting future events and finding hidden relations from huge volumes of data. The study adds that such uncovered knowledge facilitates attaining better customer relationships, fraud detection, and achieving competitive advantage. The study concludes that because BI execute decision trees, SM technologies, NLP, and AI techniques; they are regarded as highly specialized and sophisticated.

In their study, Negash and Gray (2008) investigated BI in the industry and concluded that BI combines analytical tools with operational data to present competitive and complicated information to decision-makers and planners. His study sought to enhance the quality of inputs and the timelessness of the decision process. The study indicated that BI is utilized to understand the available capabilities in the firm, trends, the state of the art, technologies, future directions in the market, and the regulatory environment in which the institutions compete.

Ghazanfari, Jafari, and Rouhan (2011) suggest that some tools can be used to evaluate the BI of enterprise systems, namely, Supply Chain Management (SCM), Customer Relationship Management (CRM), and Enterprise Resource Planning (ERP), Customer Relationship Management (CRM). They add that such tools are important for applying BI in the DM processes. Therefore, the authors recommended developing a model to assess and evaluate the intelligence level of an enterprise system. Such a model should consist of six dimensions, namely, Optimization and Recommended Model, Stakeholder Satisfaction, reasoning, Providing Related Experimentation and Integration with Environmental Information, Analytical, and Intelligent Decision-support, and Enhanced Decision-making Tools. Such a model can be used by enterprises to buy, select, and evaluate systems and software that support DM and achieve competitive advantage.

Djerdjouri (2020) conducted a study on data and BI systems for achieving competitive advantage. The study underscored the importance of adopting BI in a firm due to its effectiveness in raising the organization’s awareness of the critical role of BI in the competitiveness and survival of the firm. The study further showed that the majority of small and medium-sized businesses do not implement BI technologies due to the high cost of implementing and managing BI systems. The study highlighted the positive role of applying BI systems in terms of improving a company's performance and competitiveness.

According to Anusha et al. (2019), the process of business intelligence consists of the following f ive four phases:

· Data sourcing: Business Intelligence collects, modifies, and mixes data from a variety of business domains, including but not limited to marketing, finance, and human resources, among others.

· Risk assessment: During this phase, managers can foresee, detect opportunities and dangers, and fulfill the demands of the organization.

· Situation awareness: In this stage, you have a better grasp of the current situation and may make educated judgments based on the findings of your data analysis.

· Decision support: One of the key functions of business intelligence is to provide decision assistance for managers working with current data.

Several studies have been conducted to investigate the impact of BI on various management sectors. The impact of BI on decision-making quality was addressed in the information technology sector in Australia (Wieder and Ossimitz, 2015); their study underscored the mediating impacts of BI and data quality. Visinescu, Jones, and Sidorova (2017) concluded that BI improved the quality of decision-making.

Decision Making (DM)

In the past, managers viewed DM as an art that relied on judgment, experience, intuition, and creativity (Persson, 2017). Nowadays, managers who concentrate on a more systematic approach, concentrating on a thoughtful, methodical, and analytical Decision-Making Process (DMP) instead of depending on interpersonal communication skills, succeed (Turban et al. 2010). Decision Making (DM) is of paramount importance not only for managers who are responsible for making decisions for the interests of their organizations but also for other stakeholders and employees, as well (Negulescu and Doval, 2014).

As the name suggests, DM means the process of making decisions in general. At the organizational level, this concept means making good decisions that require managers to have accurate, essential, and timely information to retrieve their decisions and mitigate the uncertainty concerning DM (Cooke and Slack, 1991; Frishammar, 2003). In their study, McShane and Glinow (2010) defined DM as the conscious process of making choices among alternatives to change the current state of affairs. Similarly, DM is defined as the process of choosing from a list of alternatives to attain one or more objectives (Turban et al., 2011).

The definitions of DM provided above are similar to those provided by Darma (2016), who defines DM as a conscious process conducted by an individual in identifying a choice from a variety of alternative actions to achieve the goal of moving from the present to a better future. According to Trewartha and Newport (1976), DM entails selecting a course of action from among two or more optimal alternatives to solve a particular problem. The key determinants of board failure or success are both speed and quality of decision-making (McGregor, 2010). As Harris (1980) puts it, DM seeks to identify and select alternatives depending on the preferences and values of the decision-makers; DM suggests that there are alternative options to be taken into account; DM is not only concerned with choosing a decision out of several alternatives, but also it is concerned with choosing an option that meets our objectives, desires, values, and goals.

It is worth mentioning that the current DM is affected by the organization’s history (Walsh and Ungson, 1991). DM further requires knowledge about the required improvements, along with information about how, why, and what has been done (Hajric, 2018). DM is further considered one of the biggest challenges encountering organizations in terms of making timely and important business decisions (Huie, 2016).

The process of DM plays a cardinal role in any organization, and it should be resolved and planned in a reliable, transparent, and comprehensive manner (Shimizu et al., 2006). The decision-making process depends significantly on the accuracy and quality of the knowledge, information, and data (Mugnaini and Fujino, 2017; Toivonen, 2015). The success of any organization relies heavily on the quality of decision-making (Shimizu et al., 2006). In decision-making, the organization is involved in putting forward suggestions and proposals, along with brainstorming ideas associated with enhancing the organizational operations to meet its goals, as well as determining the essential information required and identifying the pros and cons of each proposal or idea, which help in determining the most suitable proposal and making modifications until coming up with the most suitable decision (Alkatheeri et al., 2020).

Schoemaker and Russo (2014) investigate decision-making and define it as the process by which individuals, groups, or individuals come to conclusions regarding the future actions they should pursue according to a set of limits and objectives based on available resources. Such an iterative process involves intelligence gathering, issue framing, learning from experiences, and coming to conclusions. They add that the DM process contains corporate strategic choices along with adopting a strategic approach to make organizational and tactical decisions in organizations.

In their study, Panpatte and Takale (2019) examine the impact of the DM process on the effectiveness of the organization. They indicate that the management of any organization relies heavily on DM. They conclude that decisions are complicated, highly unstructured, risky, and time-consuming, and have a significant impact on the organizations' future. They define DM as the process of making choices by determining a decision, collecting information, and evaluating alternative resolutions. The DM process helps in making more thoughtful and deliberate decisions by organizing related information and determining alternatives.

Obi and Agwu (2017) carried out a study on the role of the DM and organizational goal achievement, particularly in a sluggish economy. The study took place in Lagos. The sample contained 20 corporate organizations, while the number of participants amounted to 80 respondents. To collect the data, a 5-point Likert scale questionnaire was used. The data were analyzed qualitatively. The study concluded that any decision leads to action. The study recommended that experienced executives identify the barriers that hinder taking an effective decision and develop strategies to overcome such barriers.

Quality of Decision-Making

Information quality is highly important to the success of the organization, and it has also been indicated that information accuracy and completeness improve the quality of decision-making (Houhamdi and Athamena, 2019). Organizations have a common sense of the quality of their decision-making processes, but they do not have a common practice of this concept (Bujar et al., 2019). Having a quality decision-making process is common sense to organizations, but not always common practice.

In their study, Negulescu and Doval (2014) underscore the effectiveness of the quality of DM in organizations and define such a concept as a process that should take into consideration the following key drivers: the nature of the environment, strategy, ethics, empowerment, information, feedback, the types of programs, options, and risk avoidance, along with the availability of resources and opportunities. In their study of the quality of DM, Dale and Duncalf (1985) indicated that the engagement of market purchasing, design, along with market feedback is essential to guarantee that quality-related DM is consistent and effective with policy.

It is worth mentioning that the quality of DM might become poor when the decisions take longer time than they are required and when they are made by the wrong information, the wrong part of the organization, or by the wrong people, they might turn out badly (Blenko et al., 2011). The lack of decision-making quality harms public management. To elaborate, the absence of decision-making quality is significantly associated with the little use or absence of intelligence elements and knowledge management in public management (Melati, Janissek-Muniz, and Curado, 2020). The importance of decision-making quality is manifested in enhancing the general competence of the product innovation development process (Szutowski, 2019). There are several factors affecting the quality of DM, such as content quality, data quality, information quality, business intelligence scope, and business intelligence management (Urumsah and Ramadhansyah, 2019).

The Impact of Business Intelligence (BI) on Decision Making (DM)

There is a strong correlation between BI and decision-making. Girsang et al. (2018) define BI as "the knowledge to process data stored and gathered to provide businesses with essential information that is beneficial in making decisions." Besides, BI requires tools and data both to store and process historical data for DM. Companies make critical business decisions by relying on large datasets maintained in their databases; thus, BI has a direct impact on decision-making (Al-Azmi, 2013).

According to Arnott, Gribson, and Jagielska (2004), BI is tailored to enhance the DM process. Since the role of BI is manifested in extracting crucial information for the business and manipulating or presenting that data as useful information for managerial DM (Arnott et al., 2004). On the other hand, Negash (2004) argues that BI is employed to understand the available capabilities in the state-of-the-art, firm, future directions in the markets, trends, technologies, the actions of competitors, and the impacts of such actions, and the regulatory environment in which the firm competes.

According to Berhane, Nabeel, and Große (2020), BI has a positive impact on the process of decision-making and the foundations for decisions. BI systems are considered the solutions responsible for the transcription of data into knowledge and information, and they further create a suitable environment for effective acting, strategic thinking, and decision-making in organizations. BI systems concentrate on building competitive advantage and increasing managerial effectiveness (Kulkarni, Robles-Flores, and Popovi, 2017). BI systems are used to support decision-making at all management levels (Mohammad, 2012).

Urumsah and Ramadhansyah (2019) investigated the effectiveness of BI on the quality of decision-making. The study took place in Indonesia, particularly in fertilizer companies. The following factors were examined: BI, BI scope, data quality, information quality, and content quality. To attain this aim, a survey questionnaire was used. The sample included operational and top-level management BI systems. A roughly 111-item questionnaire was distributed to the participants. To analyze the data, a statistical analysis was used. The findings revealed that the main factor influencing the quality of DM is BI. To enhance the quality of DM, the study recommended considering the above-mentioned factors.

Dumitru-Alexandru (2016) investigated the impact of BI on DM. The study concluded that using intelligent systems depending on BI and Big data for creating policy has a positive impact on DM made by corporations, and individuals, along with providing feasible solutions for governments. In their study, Pourshahid et al. (2014) presented a goal-oriented BI-supported methodology for DM, which allowed enterprises to start with limited data, capture stakeholders' perspectives, explore the essential data to build models and identify the model impact, opportunities, and threats. The models of tool-supported methodology sought to promote the experience of the user with common BI applications. Such a model enables managers to monitor the effect of decisions on the organization's objectives and enhance both business processes and decision models.

Polanen (2018) investigated the role of BI in the decision-making process. The study indicated that successful implementation of strategic decisions enables the firms to improve the structure of their DM process. To achieve this objective, BI is regarded as an instrument that supports the strategic decision-making process. The sample consisted of the customs brokers' branch collected from World Customs Organization. The findings revealed that there is a positive and strong correlation between BI and the decision-making process. The study recommended business owners and managers yield new skill sets to approach DM at the strategic level because BI effectiveness relies on an individual's strategic thinking abilities.

Lieber (2016) conducted a study on BI and strategic DM. The study indicated that applying BI and DM has both advantages and disadvantages. As for the advantages, they are manifested in duplicable data findings that are real-time or fast, updating dashboards combining various data feeds, and rapid generation of predictive models or alternatives. As for the disadvantages, they are embodied in overload information if it is not properly designed for end users, the non-careful craft of business needs might lead to poor metrics provided, and an idea of decision is required to concentrate to program it properly.

Similarly, Wieder and Ossimitz (2015) conducted a quantitative study on the impact of BI on the quality of DM. It sought to examine the direct and indirect impacts of BI management quality on managerial DM quality. The study took place in Australia. The sample consisted of (500) Australian Stock Exchange Companies. The respondents consisted of (44) senior IT managers. The data were collected using a survey. To analyze the data, Partial Least Square (PLS) analysis modeling was used. The findings confirmed the overall relationship between BI and the quality of DM and information quality. However, it revealed the mediating impacts of information/ data quality and BI. The study recommended managing BI properly due to its importance in information/data quality. More importantly, the study recommended managing data to guarantee consistency, transparency, completeness, and trust in data to yield high information quality levels.

Lahbi (2018) conducted a study on the impact of BI on the DM process. The study took place in Sweden at Linkoping University. The study indicated that BI helps in taking better decisions; therefore, its popularity has increased in many organizations. The study was analyzed qualitatively by interviewing strategic decision-makers and BI users. The results showed that the BI system has a significant and positive impact on the decision-making process.

In their study, Zamani, Maeen, and Haghparast (2017) investigated the effectiveness of implementing BI in increasing the quality of DM processes. The study took place at Saman Kish Electronic Payment Company. The sample consisted of thirty experts. In selecting the participants, snowball sampling and purposive non-probability sampling were used. The data were analyzed quantitatively. The findings showed that BI has a positive impact on DM processes.

Negro and Mesia (2020) examined the impact of BI on DM. The study reviewed the literature by conducting a systematic literature review (SLM). The study concluded that there is a high level of connection, direct interrelationship, and interdependence between BI and organizational DM in a universal business environment. Tvrdíková (2007) investigated the support of DM by BI tools. To this end, the study addressed issues of BI applications, and the main elements of BI tools were investigated. The study concluded that BI tools are essential for applying data mining, which is considered an effective instrument for obtaining new information from large datasets.

Olszak and Ziemba (2006) investigated BI systems' role in supporting DM in organizations. The study concluded that BI contributed towards enhancing the quality of DM in any organization in terms of improving customers' service and increasing customers' loyalty. The study concentrated on three main elements of BI systems, namely, key information technologies such as data warehouse and ELT tools, key information technologies, such as data mining and OLAP techniques, and the applications of BI which support making various decisions in an organization.

To investigate the impact of BI on the decision-making process, Persson (2017) found in his qualitative study at higher education institutions that BI influences the decision-making process by reducing time, enhancing efficiency, and improving the quality of information for the user. Similarly, Herschel (2011) indicates that BI is highly important owing to its significance for governments’ and businesses’ decision-making activities. Along similar lines, Hernaus Pejic Bach and Rebac Jirous (2010) carried out a study on the impact of BI on the decision-making process.

The authors underscored the importance of BI in management by stating that BI is considered an essential managerial issue. They further indicated that its significance lies in developing analytical DM capabilities. Their study sought to investigate the importance of BI on the DM process. To this end, (68) Croatian companies were selected. The study distributed a questionnaire to (200) IT and CIO experts. The questionnaire consisted of (32) questions that highlighted several aspects; including, IT, and BI. DM, and so forth. The data were analyzed quantitatively using statistical analysis. The results revealed that there is a positive relationship between BI and IT. The results further showed that BI is strongly associated with the DM process. The study concluded that using BI in a company enables it to gain a competitive advantage.

Pranjić (2018) carried out a study on the DM process in the BI. The study indicated that BI is considered the essential instrument for achieving fact-based quality and decision by enabling the decision-makers to make the appropriate decision at a suitable time. BI further enables decision-makers to enhance the quality of their decisions and to be more effective in meeting business demands, which in turn, BI enables them to achieve a competitive advantage.

Vizgaitytė and Rimvydas (2012) investigated the impact of BI in the process of DM. The study indicated that BI has unfailingly garnered scholars' attention due to its importance in value creation, its potential, and its role. The study underscored the importance of human intervention in BI applications. To elaborate, the study indicated that achieving effective BI applications relies heavily on human factors. In conclusion, the study indicated that BI plays a pivotal role in the process of DM.

Lioyd (2011) identified the key components of the BI system and their role in managerial decision-making. The study underscored the strong correlation between BI and DM by stating that BI is employed to create knowledge to enable DM business. To this end, the literature from 2001 and 2010 were reviewed and determined the 4 well-known components of the BI system; including, OLAP techniques, data warehouses, ELT tools, and data mining. The study indicated that the function of each BI component was utilized to facilitate managerial DM at three organizational management levels, namely, technical, strategic, and operational.

Prem and Karnan (2013) conducted a study on BI and its optimization techniques for DM. The study defined BI as a huge category of technologies and applications for providing access to, collecting data, and analyzing them to help the users of enterprise to enable obtaining optimal business decisions. The authors articulated the correlation between BI and DM by indicating that BI entails obtaining the right data for the right decision-makers.

There is a strong correlation between BI and DM. To elaborate, Karim (2011) suggests that BI means the combination of the gathered, cleaned, and integrated data from several resources that provide outcomes in a manner that can promote decision support and business decision-making. Kulkarni, Flores, and Popovic (2017) opine that BI is regarded as a special type of capability of information technology that is associated with a company's ability to furnish decision-makers with high-quality information. It further enables the decision-maker to expect the optimal events in the future that might have a critical impact on the company (Rouibah and Ould-Ali 2002; Thomas Jr. 2001).

In his study, Othman (2021) found that there is a strong positive impact of BI (analytical skill, level of use, and quality of data) on the quality of decision-making in Jordanian banks. He further recommended paying more attention to the use of BI in the quality of the DM process. Similarly, Darma (2016) points out that BI promotes DM by collecting, classifying, analyzing data, and harnessing them to help managers to make better decisions and to enhance the performance of the organization. It is commonly known that BI presents significant tools that assist in effective reporting and in analyzing business issues to acknowledge the internal and external organizational environments (Søilen, 2015; Fourati-Jamoussi and Niamba, 2016), which in turn, equips the managers with the essential data that is utilized in DM processes (Othman, 2021).

Business Intelligence in Construction Industry Firms

A large amount of dynamic and heterogeneous data is generated in the construction sector that is attributed to its fragmentation during the project's life cycle. Therefore, accurate and immediate access to information is essential to the decision-making and management process (Rodrigues, Alves, and Matos, 2022). BI is highly important in the construction sector because BI can rapidly analyze implicit information from the data to enable the decision-makers to better grasp the process and solve the problems (Zhao, 2019).

In their study of the impact of BI on the construction sector, Zhang, Qi, and Meng (2021) indicate that BI presents effective DM knowledge for a variety of corporations by applying the methods of data mining and data warehousing. Sugata, Widodo, and Kumara (2016) designed their study a BI application model for investigating the construction project cost to present follow-up data as early and as quickly as possible to help the executives make valuable decisions within the context of business. The authors concluded that BI plays a pivotal role in enhancing a construction project's performance.

Malik et al. (2022) conducted a study on managing integration complexities in construction projects according to resilient capabilities to tackle supply chain risks. The study underscored the importance of managing the complexities by combining resilient capabilities and supply chain for a resilient supply chain in construction. The study addressed the complexity contained in the dynamics of effects among organizations' supply chains and resilient capabilities to avoid disruptive events. The study collected the data by reviewing the literature and conducting content analysis. Also, expert opinions and a questionnaire survey were conducted on the data qualitatively and quantitatively. The findings showed that construction organizations are subjected to insufficient management oversight, health pandemics, poor information coordination, error visibility to stakeholders, and budget overruns. Interestingly, the study found that the most effective resilient capabilities contain collaborative information exchange, assets visibility, inventory management systems, alternative suppliers, and business intelligence gatherings, alternative suppliers, and inventory management systems.

In their study, Rodrigues and Matos (2022) conducted a study on construction management that is supported by BI tools and BIM. The study underscored the importance of the construction sector in generating large amounts of dynamic and heterogeneous data attributed to fragmentation during the life cycle of a project. The acquisition of accurate and dynamic access to the data is of paramount importance to the decision-making and analysis and management by construction owners, technicians, managers, and supervisors engaged in various phases of the life cycle of the project.

Since the construction project data is uncorrelated, dispersed, difficult to visualize, and diverse, the study developed a methodology for data management throughout building construction employing data with BI analysis tools and BIM. This methodology extracted data from the 3D parametric model and combined it with a BI tool in which the data are interrelated with a similar database, namely, the BIM model. A case study was carried out, to unravel the applicability of the methodology. The findings showed that the methodology provided a collaborative platform regarding accurate data analysis to the supervision team and construction management, enabling the stakeholders of the projects to obtain and update data in actual time and a permanent connection with the BIM model. The methodology further improved DM process reliability and ensured project deliverability. It contributes to a more sustainable management process by reducing resource consumption i.e., energy and error. In conclusion, the study sought to provide a methodology for data analysis for BIM models combined with BI for sustainable construction management.

Smith, Aigbavboa, and Thwala (2021) investigated the elements of competitive intelligence for the competitive advantage of construction firms in Ghana. The study sought to investigate competitive intelligence elements that impact the competitive advantage in the Ghanaian Construction Industry and the extent to which each element influences firms' competitive advantage. The study underscored the importance of intelligence on competitors' risks as well as existing opportunities in the industry. The study further recommended prioritizing them in competitive intelligence gathering. Regardless of the importance of applying technologies in the construction industry, construction firms are hesitant to incorporate innovative technologies, such as information and communication technologies, automation, and digitization, into their standard practices (Alaloul et al., 2020). 

Construction Industry in Jordan

Before proceeding to examine the importance of the construction industry in Jordan, it is necessary to define the construction industry concept. According to Abu Yahiya and Abdullah (2022), a construction sector is a group of activities associated with engineering constructions and buildings of all types. Its close relationship with other economic sectors is the characteristic that distinguishes such a sector. Therefore, the construction industry is considered a significant indicator of the national economy. However, such a sector might confront a variety of risks that might affect the execution of the project, might lead to increased costs, might delay delivery, and might affect quality.

Smith, Aigbavboa, and Thwala (2021) define construction firms as road and building contractors who execute civil engineering works that might be foreign or local.

Any economy relies heavily on the construction industry, which is highly important to the economy (The Construction and Housing Sector in Jordan, 2019). Therefore, the economic industry is considered one of the largest industries (Department of Commerce, 2017). In Jordan, the construction industry plays a pivotal role in the economic activities of Jordan (Mahfouz, Awang, and Muda, 2019; Abu Salem and Suleiman, 2020). Along similar lines, The Construction and Housing Sector in Jordan (2019) indicates that the construction industry constitutes one of the largest segments of the economy.

Similarly, Sweis, Hiyassat, M, and Al-Hroub (2016) suggest that the construction industry is considered one of the critical sectors in Jordan; its importance is manifested in Jordan's facing the challenges related to the political crises in the region and the increasing population. In their study, Albtoush et al. (2019) found similar results that the construction sector is not only one of the most significant drivers of the national economy of any country but also plays a pivotal role in improving individuals’ lives. They add that the success of the project in the construction industry is essential because it has a positive impact on the development of the national economy.

Regardless of the importance of the construction industry in Jordan, it suffers from a serious issue that is manifested in the weakness of business (Report, 2018). In their study, Abu Salem and Suleiman (2020) indicate that the Jordanian construction industry faces a major risk related to time delay. In addition, the construction industry in Jordan faces a variety of problems that obstruct its outstanding role in achieving sustainable development in Jordan (Al Balkhy, Sweis, and Lafhaj, 2021).

In the same vein, Abu Yahiya and Abdullah (2022) indicate that the construction industry in Jordan is fraught with challenges that are associated with several factors, including lack of equipment, manpower, financing, other construction requirements, and resources. Besides the multiplicity of phases, the length of the execution period, beginning from the decision stage to the implementation stage, and final delivery all enhance the likelihood of the occurrence of risks and uncertainty. More importantly, the present study is concerned with investigating the impact of BI on the quality of decision-making in the construction industry in Jordan. It is primarily concerned with the impact of BI on the quality of decision-making in construction firms in Jordan.

Information Technology (IT)

In his study, Gunda (2019) defined information technology (IT) as a technology that utilizes computers to process, store, maintain, and convey information. Nowadays, the term "information and communications technology" (ICT) is used because it is impossible to work on a computer that is not connected to the network. Regarding the application of IT, Lee et al. (2018) indicate that IT is usually applied to the products and goods in the electronics sector, including phones, TVs, computers, and so forth. The researchers further add that a platform grounded in intelligent information technology (IIT) should be built to transform existing sectors, including medical, finance, the service industry, and manufacturing, relying on data and IIT.

In respect of the importance of IT for firms, the tools of IT are assumed to enhance the external and internal information flaws, thus enhancing the information processing capabilities of the firms and having a positive impact on the innovation program's performance (Kroh et al., 2018). Likewise, Chen and Liu (2018) point out that IT investment has a positive impact on a company's performance; they construe that applying IT influences external and internal operations by altering the method of business conduct, supply chain operation rules, and reconstructing the relationship between supply chain individuals.

Using IT is considered a competitive weapon that has recently gained popularity. To elaborate, IT plays a cardinal aspect in achieving a competitive advantage in the company. Therefore, information system managers, strategic planners, and senior executives are increasingly drawing their attention to seize the opportunities for gaining competitive advantage through information technology by using innovative communications and information systems (Bakos, and Treacy, 1986). The opportunities that might be created as a result of applying IT in an organization are manifested in linking with suppliers and customers to enhance their switching codes and creating new business by product or service (Rockart and Morton, 1984).

Several studies (Mithas, Ramasubbu, and Sambamurthy, 2011; Melville, Kraemer, and Gurbaxani, 2004; Zaqout et al., 2018) underscored the positive correlation between information technology and decision-making; such studies indicated that information technology systems aim to improve firm performance by improving decision making. Regarding the correlation between BI and information technology, Olszak and Zurada (2015) carried out a study on the effect of BI tools on BI developments in an organization. To collect the data, the literature was critically analyzed, along with creative thinking, case studies, and an interpretive philosophy. The study concluded that BI is an essential factor that promotes the ability of organizations to maintain their competitive advantage (CA). The study indicated that some IT tools can be used to enhance BI development, namely, online transaction processing, online analytical processing, predictive modeling and data mining, text mining, data cleaning, web mining, search-based applications, exponential random graph models, in-memory databases, and BI.

In respect of IT in the construction industry, Ahmad, Russell, and Abou-Zeid (1995) argued in their study that IT has an effective impact on the integration of the construction industry. To elaborate, they indicated that IT plays a pivotal role in redesigning a variety of organizational processes and activities. It is of paramount importance in the construction industry due to the dynamic nature of construction processes in terms of a high degree of coordination, the need for teamwork, flexibility, and interdependence of various participating entities.

The authors suggested that management's commitment, suitable IT investment, along with the ability to present leadership under the altered atmosphere are essential for the successful execution of IT in the construction industry. The effect of IT on construction organizations and design should be managed by understanding both internal and external factors that influence business organizations. Finally, the authors indicated that the implementation of IT is not only considered a technical enhancement but also considered a managerial decision that entails re-engineering institutional operations and functions.

Summary

From the literature review, several conclusions can be drawn which leads to the identification of gaps that needs to be addressed in this study. The gaps are discussed in the following paragraphs. In a developing country like Jordan, the construction industry is fundamentally reluctant to innovate (Yap et al., 2019). Besides, the adoption proportion of new technologies is still relatively poor in the construction industry (Yap et al., 2021).

The researchers add that the construction industry faces some challenges, such as technological limitations, a poor safety culture, and prohibitive costs. BI is essential for researchers, specialists, and practitioners due to its significance for firms’ future developments and overall strategy to enhance their performance and rationalize their actions by including the newest management ideas and theories (Zeng, 2018). As a result, it will lead to a well-studied DM that would improve the institutions and guarantee that their diverse and numerous resources are employed in the optimum manner (Lotfi and Mansourifard, 2021).

Until now, the literature has only concentrated on the impact of business intelligence on decision-making and the impact of business intelligence in construction industry firms. However, none of these studies took place in Jordan. Besides, none of these studies have investigated the impact of BI on the quality of DM in construction firms. The paucity of studies that have been addressed within this field has prompted the researcher to bridge this gap in the literature by investigating the effect of BI on the quality of decision-making in construction industry firms in Jordan.

Chapter 3: Research Method

However, the construction sector enterprises in Jordan need to pay more attention to the significance of using BI to enhance the quality of their decision-making (Almomani et al., 2019; Lina et al., 2021; Sarvari et al., 2021). In addition to the instability in the surrounding area, Jordan is also confronted with the challenge of dealing with its limited natural resources. The existence of such concerns suggests that Jordan is facing a diverse set of economic challenges. As a result, the purpose of this quantitative cross-sectional study is to investigate the effect that BI has on the caliber of decision-making in companies that are involved in the construction business in Jordan.

This chapter will be divided into (11) sections. First, the research methodology and design will be elaborated. Second, the population and sample will be presented. Third, materials or instrumentation will be provided. The fourth section will present the operational definitions of variables. The fifth section will describe the study procedure. In the sixth section, the data analysis will be elaborated. The seventh section will present the assumptions. In the eighth section, the limitations will be presented. In the ninth section, delimitations will be provided. Ethical assurances will be provided in section tenth. The last section will present the summary of this chapter.

Research Methodology and Design

In contrast to quantitative publications, which had established study variables and sub-variables before data collection, the majority of qualitative articles in the review entailed the identification of themes that were open to researchers' interpretations. It's important to remember that various people's perceptions of the same event might be influenced by many things, including their own moods, upbringings, cultural norms, and experiences. Therefore, qualitative research tends to take an interpretative stance (Mwita, 2022). On the other hand, for decades, researchers have argued about whether or not to generalize from qualitative study results. The use of relatively small samples in qualitative studies has been called into question. Furthermore, qualitative researchers often acknowledge that the generalizability of their studies is limited due to the small sample sizes they use. (Vasileiou et al., 2018).

The confidentiality of informants' identities is critical to ensuring they feel safe sharing information. Respondents are more likely to provide information when they know their identities won't be revealed, which is only one of many benefits of upholding the ideal of anonymity. As a consequence, it prevents responders from being harmed if sensitive data are included. Since the researcher interacts with respondents directly during data collection for qualitative studies, this might be challenging. Even though respondents know their names and information will always be kept secure, they may nevertheless be reluctant to provide some information. When conducting quantitative studies, respondents may be asked to answer questions anonymously. This ensures that they will feel safe sharing their information for the study (Mwita, 2022).

Another alternative method in a qualitative study is field research, which provides social facts, but it requires the researcher to make several visits to a field research project because the data might not appear at the first moment (Blackstone, 2012). A focus group provides a wide range of information and clarification of the topic, but it is hard to control and manage, and the researcher might struggle with encouraging people to participate (Queirós, Faria, & Almeida, 2017). Regarding case studies, they provide a good opportunity for challenging current theoretical assumptions and for innovation, but their limitation is manifested in establishing a cause-effect connection to come to a conclusion, and it might be difficult to generalize, especially when there is a small number of case studies (Mwita, 2022).

Mixed method methodology is all about the incorporation of both qualitative and quantitative methods of research, the researcher can produce more complete knowledge necessary to inform theory and practice. Despite the overwhelming support of researchers for this research procedure, it also has a few drawbacks. First, due to its duplicity, the implementation of mixed methodology in a single study can be difficult for a single researcher to manage. This is especially true when the researcher must employ two or more approaches simultaneously.

In addition, a researcher who chooses to use this method must be knowledgeable of multiple methods and approaches and comprehend how to appropriately combine them. Similarly, a large number of researchers believe that each researcher should employ either the qualitative or quantitative method. Due to its duplicity, the hybrid method of research is also more expensive and time-consuming than other research methods. When qualitative data is quantified, it loses its flexibility and depth, which is one of the primary benefits of qualitative research. This is one of the primary drawbacks of this approach, which is also one of the primary advantages of qualitative research. This arises because qualitative codes are multidimensional (Bazeley, 2004), but quantitative codes are one-dimensional and fixed. As a result, transforming rich qualitative data into dichotomous variables results in the production of one-dimensional data that is unchangeable (Driscoll et al., 2007). It is feasible for a researcher to avoid quantitatively analyzing qualitative data; however, doing so might turn into a highly time-consuming and complicated procedure since it needs the researcher to analyze, code, and integrate data from unstructured to structured data (Driscoll et al., 2007; Roberts, 2000).

Accordingly, the researcher believes that the quantitative approach, such as a questionnaire will be more effective than using a qualitative approach, such as the interview because it enables the researcher to acquire information from the population in large samples (Aldhaen, 2020).

Population and Sample

For example, engineers can create comprehensive designs and specifications for commercial construction projects, such as electrical and mechanical installations. They evaluate the risk and performance requirements of the projects and guarantee that the designs adhere to the industry's building standards. Decision-making is crucial for managers because it enables them to allocate resources, solve problems, seize opportunities, and achieve team objectives. Effective managerial decision-making requires gathering and analyzing information, considering alternative solutions, and selecting the best course of action based on goals, constraints, and available data. Managers can propel their teams forward and position them for success in a continually changing environment by making sensible decisions.

As indicated in the statement of the problem, BI needs to be addressed in construction firms in Jordan, and how its importance in the quality of decision-making is further ignored. Therefore, this study seeks to investigate the effectiveness of BI on the quality of decision-making and information quality in construction firms in Jordan. Given the study problem, purpose, and research questions, the population is appropriate as it includes the considerable number of managers who use or have recently used BI. The sample will consist of 120 managers, including top and operational managers working in construction firms in Amman, Jordan. The sample includes four construction firms in Amman that operate BI or have recently used it. Mooi et al. (2018) suggested that researchers consider calculating the number of participants they will really be able to contact, the proportion of people who will agree to participate, and the proportion of people who will actually fill out the questionnaire correctly. This may aid in determining an appropriate sample size.

Since it is usually impossible to gather data from every member of a community, a sampling technique is usually required (Kumar et al., 2013; Sekaran, 2003). Therefore, selecting a sufficient sample size is critical for achieving reliable results in research. While crucial to the success of any empirical study, this stage is frequently overlooked due to its perceived complexity (Dattalo, 2008). Sekaran and Bougie (2010) argue that sample size is part of a population that is needed to make sure there is enough information to draw conclusions. Simply, sample size means the number of people who will take part in a study or make observations.

Memon et al. (2020) claim that there are many ways to measure group size in research. These factors can be put into different groups, such as item-to-sample ratios, population-to-sample tables, and general rules of thumb for figuring out sample sizes. In the last few decades, most people have used Roscoe's (1975) set of rules for figuring out the sample size. Roscoe said that for most studies of behavior, a sample size of more than 30 but less than 500 is best, while a sample size of more than 500 may lead to a Type II mistake (Sekaran & Bougie, 2016).

This study will use random sampling, which is a type of probability sampling, to gather information about the attitudes, effectiveness, and advantages of using BI in construction firms. The power analysis, including effect size, Alpha, and beta, will be calculated. Besides, the participants will be divided into four groups according to their age, gender, educational background, and experience. The study will use a smaller sample size (a minimum of 120). The participants will be recruited by email from professional organizations.

Instrumentation

The validity of the questionnaire will be checked using content validity by expert judgment to investigate if the instrument has content validity in terms of covering all aspects related to the impact of BI on the quality of decision-making in construction firms. Besides, the face validity will be achieved by distributing a questionnaire to (20) participants out of (120) who will be selected randomly. After a detailed discussion with them concerning each item and checking their understanding, the final version will be applied based on their modifications.

Cronbach's Alpha will be used to measure the reliability of the study. Cronbach (1951) maintains that reliability entails the extent to which a measurement includes a correct group of items that the researcher can accurately rely on to evaluate a construct. A pilot application needs to be completed. Accordingly, a pilot testing will be conducted with (20) managers in construction firms in Jordan. The results will show that the majority of the participants agreed on the effectiveness of BI in making informed decisions. The study will conduct some modifications by focusing more on the correlation between BI and the quality of decision-making.

Operational Definitions of Variables

This section will present the operational definitions of variables, i.e., how the variables will be measured in the study.

Business Intelligence:

Business intelligence will be defined as systems that integrate operational data with analytical tools to present complicated as well as competitive information to decision-makers and planners (Negash, 2004). BI will be the independent variable in this study. The statistical analysis that will be used to measure the BI is one-way ANOVA to investigate the impact of BI (the independent variable) on the quality of decision-making (the dependent variable).

The validity and reliability of the instrument will be achieved using Cronbach's Alpha to estimate the internal consistency of a questionnaire, which helps quantify the reliability of a score and summarize the data from multiple questionnaire items (Christmann & Van Aelst, 2006).

Quality of Decision-Making:

Quality of decision-making will be defined as a set of informed decisions that can be achieved by taking into consideration the organization's strategy, environmental factors, empowerment, programs, resources and opportunities, risk avoidance, information and feedback, and ethics (Negulescu & Doval, 2014). The quality of decision-making will be the dependent variable in the study. The statistical analysis that will be used to measure the dependent variable is regression analysis, which is valid and reliable to examine the effect of BI on the quality of decision-making.

To measure the association between the independent and dependent variables, the study used cross-tabulations using data tables and displayed the results of each participant. Cross tabulations will help construction firms make informed decisions by identifying the trends, correlations, and patterns between the study parameters.

The level of measurement of each variable is 5-Point Likert Scale, which is an ordinal scale that ranges from strongly agree, agree, neutral, disagree, and strongly disagree. Then the means, standard deviations, and the agreement degree for each item will be calculated. The study will further use Spearman’s correlations for analyzing ordinal demographic characteristics, including age, gender, level of education, and years of experience.

The construction firms will be contacted to obtain permission to collect the managers' email addresses. The data collection process will last for (30) business days. The questionnaire will be distributed electronically via email as a web-based questionnaire to the intended sample of the population, i.e., top and operational managers working in construction firms in Jordan. Information on the study's dates, what will be involved in participating, and how to get in touch with the researcher will all be posted. All participants who agreed to take part in the research will be given a permission letter that explains everything from the study's goals and methods to the possible dangers involved and how to withdraw from the study at any time without penalty.

The researcher will collect the questionnaire from the participants, and the responses of the participants will be coded and analyzed statistically using SPSS to extract the means, the standard deviations, and the degree of agreement among the items. Adopting a questionnaire from the literature and doing statistical analysis are only two examples of the well-defined actions that will be taken to bring this research to fruition. Researchers would be able to follow the same steps used in the original research. Therefore, the study procedures could be replicated.

The data of the questionnaire will be analyzed quantitatively by using SPSS version 25 to analyze the items of the questionnaire according to a 5-point Likert scale, namely, agree, strongly agree, neutral, disagree, and strongly disagree. The researcher will convert the answers into numbers before transferring the answers to the statistical analysis program. Then, the study will gather the frequencies and percentages. The researcher will convert the responses into numbers before transferring them to SPSS. Then, the frequencies and percentages will be counted. The analyzed data will be used to answer the research questions regarding the positive direct impact of BI on decision-making quality, information quality, and the positive direct impact of information quality on decision-making quality in construction industry firms in Jordan. To analyze the participants' responses, the study will identify the frequencies and percentages of their responses per each item in the questionnaire.

To analyze the impact of BI (the independent variable) on the quality of decision-making (the dependent variable), one-way ANOVA will be used, ANOVA, is a statistical technique for comparing three or more means. When there is only one independent variable in an analysis of variance test, it is referred to as a one-way ANOVA (Kim, 2017). To analyze the impact of the quality of decision-making (the dependent variable) on BI (the independent variable), regression analysis will be used. Regression is used to make estimates or predictions for the dependent variable with the help of single or multiple independent variables (Sykes, 1993).

Then the study will identify the means, the standard deviation, and the degree of agreement between the items to measure the impact of BI on the quality of decision-making in the construction firms in Jordan. To test the hypotheses of the study, “there is a statistically significant positive direct impact of BI on decision-making quality in construction industry firms in Jordan (α ≤ 0.05)”, “there is a statistically significant positive direct impact of BI on information quality in construction industry firms in Jordan (α ≤ 0.05)", and "there is a statistically significant positive direct impact of information quality on decision-making quality in construction industry firms in Jordan (α ≤ 0.05)", inferential statistics will be used. The statistical test chosen is appropriate to test the hypotheses and the data meet the assumptions of the statistical tests. According to Kern (2014), inferential statistics do not neglect the null hypotheses. It further means creating conclusions that reach beyond the observed data, which satisfies research questions. Comment by The Iammartino Family: Please make sure your survey has the data that can test or serve as a direct proxy to test your hypotheses. There should be a fairly direct linkage and the question structure should be validated based on past studies that have proposed similar hypotheses with comparable survey questions.

Assumptions

Limitations

The use of TAM theory, which is related to company regulations and guidelines regarding how technology is utilized in various business processes, will be a significant limitation of the study. TAM facilitates the use of technology, while the intention and adoption of technology are governed by a company's policies and procedures (Ajibade, 2018). This limitation may impact the inferences made regarding the adoption of BI in construction firms.

A further limitation of the study will be the development and design of a questionnaire, which will necessitate rigorous testing to ensure validity and reliability in an effort to generate more exhaustive and reliable results (Sousa, Matson, and Dunn Lopez, 2017). A different constraint of the study is the small sample size; consequently, the results may be less reliable due to the limited amount of data. In turn, this compromises the ability to generalize study results to a larger population (Denscombe, 2010). The tiny sample size is considered one of the limitations of quantitative studies, given the preceding information.

Because the proprietors of the construction firms assisted the researcher with the sampling process, respondents may have felt compelled to concur or firmly agree with the positive effects of using BI in construction firms. To mitigate bias by dissociating it from the study, the questionnaire was disseminated at a public location rather than the commercial office of the construction company. The final limitation of the study will be the BI technology used for this research. This study's respondents may not have access to as many business intelligence (BI) technologies as other companies do.

The main delimitation that will affect this study will be where it takes place. The research will be done at different building companies in Amman, Jordan. The city was chosen because it is the center and capital of Jordan, which is where most of the building companies are, which will serve the purpose of the study. The study will only look at 120 managers who work for different building companies in Amman, Jordan. The poll was sent to the managers because they use BI in their building companies or have the opportunity to use it.

The selection of this geographical area and the size of the sample will help achieve the goals of the research on the effect of BI on the quality of decision-making and information in Jordanian construction enterprises. The study's problem—how to determine whether or not using BI enhances decision quality—will also be addressed by the incorporation of the psychometric response approach. In addition, it will address the research questions of whether or not BI has a direct, positive impact on the quality of decisions made by construction industry firms in Jordan, whether or not BI has a direct, positive impact on the quality of information used to make those decisions, and whether or not information quality has a direct, positive impact on the quality of those decisions.

Upon completing this study, both of them will be discarded at the appropriate time. To guarantee the feasibility of this study, ethical considerations will be ensured. These will be the following issues: First, to ensure confidentiality, participants’ identities will be kept confidential. The researcher will explain the aim and the procedures of the study to the participants and present the required information, which will be kept confidential. The researcher will obtain full consent from the participants before conducting the study. The researcher will inform the participants that they have the right to participate voluntarily and the right to withdraw from the study. The researcher will further inform the participants about the risks and benefits of this study. The participants will be informed that they can contact the researcher if they have any questions.

The researcher might face a problem with the gender of the participants, as the majority of managers in Jordan are male. To avoid these biases, the researcher will attempt, as much as possible, to distribute the questionnaire to both males and females. The strategies that will be used to prevent these manifest themselves in visiting several construction firms to ensure that the sample is made up of both men and women. 

Summary

Exploring the impact of BI on companies is essential for construction firms. A large number of construction firms use BI to help in decision support, reduce costs in construction project management, and improve the managerial quality of decision-making (Mandiák, Behnová, and Mesáro, 2016; Wieder & Ossimitz, 2015). This BI will be explored in this quantitative study by using TAM. Random sampling will be used to recruit 120 managers in construction firms in an attempt to unravel the underlying usefulness, ease of use, and advantages that drive them to use BI. Random sampling is also called probability sampling; it allows individuals to have an equal chance of participating in this study (Etikan & Bala, 2017). Participants will be chosen from various construction firms in Amman, Jordan, that have used or recently used BI. As the data will be collected using a questionnaire technique to investigate the participants' levels of agreement towards applying BI in their firms, a TAM will be used to understand why managers opt for using and/or not using BI in their construction firms and the impact of using BI on improving the quality of decision making.

The following chapter will present the quantitative findings that will emanate from distributing a questionnaire to the managers of construction firms in Amman, Jordan. It will further answer the research questions regarding the impact of BI on the quality of decision-making, the impact of BI on information quality, and the impact of information quality on the quality of decision-making.

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· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

5. The use of BI tools has led to better collaboration and communication among team members involved in decision-making in my construction firm.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

6. The use of business intelligence tools has influenced my construction firm's ability to identify and respond to project risks and opportunities.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

7. The use of BI tools has improved the quality of decision-making in my construction firm.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

8. I recommend the use of business intelligence tools to other construction firms for decision-making purposes.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

9. The use of business intelligence tools has impacted the level of trust and confidence in decision-making among stakeholders in my construction firm.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

10. The use of business intelligence tools has influenced my construction firm's competitiveness and ability to support business decisions.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

11. The decisions being made by using the BI system are more likely to achieve the perceived desired outcome.

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

Part3:

Impact of BI System on information quality in construction industry firms in Jordan

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

2. The use of the BI system has provided up-to-date information in my construction industry firm in Jordan.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

5. The implementation of a BI system has improved the consistency of information in my construction industry firm in Jordan.

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

8. The cost of implementing a BI system has matched the expected benefits in terms of improved information quality .

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

Part4:

1. Poor quality information has led to incorrect decision-making in my construction industry firm in Jordan.

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

2. Poor quality information has delayed the decision-making in my construction industry firm in Jordan.

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

3. Poor quality information has increased risks associated with the decision-making in my construction industry firm in Jordan.

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

4. The quality of information available to me affects my ability to make informed decisions.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

5. Consistent and accurate information has led to efficient and effective decision-making processes in the construction industry.

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

6. I can consider all relevant information when making decisions.

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

7. My decisions in the construction industry are based on reliable data and information.

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree

8. The decision-making process in the construction industry considers the long-term implications of each decision.

· Strongly agree.

· Agree

· Somewhat agree.

· Somewhat disagree.

· Disagree