Chapter 1
Introduction

1.1 Introduction to the problem

Information quality is a critical concern for any researcher. A deficiency of clear and precise understanding of Information quality properties can result in brutal errors, misperception, threatening risks, and wasted opportunities. Hence, the most challenging part of dealing with information quality is to find the right way to analyze and evaluate the quality of information. The two barriers that prevent the finding of relevant information are ‘information overload’ and ‘information quality.’ Therefore, every researcher must remain aware of the value of sources that support and frame their research. Each researcher must take into account the work previously carried out and then build on the openings and gaps that research has exposed to provide new and meaningful knowledge for the community. This body of information must meet specific quality dimensions that come from; reliable, accurate data and prove relevance, recency, compatibility, comparability, credibility, accessibility, and interpretability (Keller et al., 2017). One of the significant concerns with information comes from the bodies that stand for the given source of information. Researchers have to make several decisions about their research. They cannot reinvent the wheel, but they might collate information from several sources. However, researchers need to ensure that an information source meets specific standards in terms of quality. Every researcher must deal with the quality of their information to remain convinced of its reliability. This aspect ensures that the findings of their research are worth relying on and their intellectual use. This information quality allows the researchers to perform a meaningful examination of the facts that matter in their field of expertise (Lukyanenko,2016).

1.2 Background of the study
1.2.1 Information Quality

Information quality was deliberated for a long-time through different studies and perceptions. The five main frameworks for information quality are (Wang and Strong, 1996), Eppler & Mengis, 2003), (Koniger and Reithmayer, 1998), (Redman, 1997), and (English,1999). (Wang and Strong, 1996) are the pioneers in the concept of “data quality.” Their studies discussed the data quality problems in corporations and their impacts on social and economic. Wang and Strong 1996, have developed a framework that captures the aspects of data quality that are important to data consumers. (Eppler & Mengis,2003) discussed one of the significant information quality problems, which is information overload, and their study reviewed the theoretical basis of the information overload and analyzed over 30 years of the existing researches in a conceptual framework to identify future researcher directions.
Furthermore, (Redman,1997) has invented many modern methods for data quality, he published a comprehensive book to provide and support business leaders, decision-makers, and information professionals with the necessary methods to set up a data quality program, generate and sustain improvements, and create a unique and significant business advantage.
Moreover, (English 1999) was the publisher of “Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits.” His book discusses major points regarding information quality of how and when to measure information quality, how to define the business costs of poor quality information, how to select the right information quality tools for the environment and the ways to reorganize and cleanse data to improve the information product before it reaches the data.

These above studies are based on how information quality works and how it can be evaluated and perceived. Also, Information quality can be classified into four categories. Intrinsic, contextual, representational, accessibility. According to Wang and Strong (2003), Jarke and Vassillio(1997) and Wand and Wang (1996), accuracy, correctness, objectivity, and simple are considered intrinsic information quality dimensions. While relevance, completeness, and timeliness are contextual information quality dimensions. Also, concise and consistent dimensions are representational information quality dimensions.

1.2.2 Information Overload

One of the chief information quality barriers is information overload. Information overload is simply defined as too much information to receive. Information overload occurs when the amount of information sources overdoses on the limited personal information processing capacity (Eppler et al., 2003). Dysfunctional effects, such as stress or confusion, are the result. Jacoby et al. (1974) Malhotra (1982) Meyer (1998). Also, it is defined as “the amount of reading matter ingested exceeds the amount of energy available for digestion, the surplus accumulates and is converted by stress and over-stimulation into the unhealthy state known as information overload anxiety.” Wurman (1990), Wurman (2001), Shenk (1997).
A decision-maker is considered to have experienced information overload when the amount of information integrated into the decision begins to decline. Beyond this point, the individual’s decisions reflect a lesser utilization of the available information. Chewning & Harrell (1990) Cook (1993) Griffeth et al. (1988)Schroder et al. (1967)Swain & Haka (2000). (Prusak, 1995) argue that if the quality of the information was modified, it could significantly affect the prospect of information overload. Improving the quality of information can improve the individual’s information processing capacity, as they can use high quality quicker and better than ill-structured and unclear information. It is also discussed that the nature of the information itself causes information overload. It can be triggered by the quantity and the quality of the information., it can also be affected by the level of ambiguity, complexity, novelty, and intensity. (Eppler & Mengis, 2003).

1.3 Information Quality Value

The study’s value comes from the vital aspect of the concept information; our lives are run and developed by information. Thus, any type of information with a form of exchange through any medium must have specific standards and dimensions to be considered useful. The value of information quality consists of the question, “which data is may be useful and relevant, so worth collecting, curating and querying?”. Also, the quality of information can be specified with “the fit for purpose or use.” The value of information can be determined through different methodologies and approaches to analyze and evaluate the sources. Fit-for-purpose can be assured by analyzing the Information quality dimensions. This includes dimensions of quality such as accuracy, completeness, consistency, timeliness, accessibility, simplicity, objectivity, e.t.c.
1.4 Information Quality Dimensions Definitions

Information quality dimensions that will be used in this study are accuracy, accessibility, completeness, concise, completeness, verifiable, correct, consistent, up-to-date, well organized, simple, relevant, flexible, well-organized, comprehensive, subjective and objective.

According to Menditto et al., (2007), Accuracy is a feature of qualitative performance characteristic, expressing the closeness of agreement between a measurement result and the value of the measured. Accuracy can be applied when the information is correct, specific, and précised. Accurate information should be free from errors, reaches the target, and agrees with the facts. Accessibility features can be defined as attainability and obtainability. Accessibility is the capability to be reached, easy to use, and deal with; it can be defined as the fact of being able to be reached or obtained easily. Completeness feature has all the necessary parts, elements, or steps. Highly proficient, to bring an end into a perfected state. Complete information is also defined as if the issue or the topic is covered broadly within an information object, and possibilities for further information are given as complete information will not leave any uncertain areas in an information object. The concise feature is marked by the use of few words to convey much information or meaning; it also describes the terseness of an information object. Information gets adequate by fulfilling the criterion. Short, clear, and straight to the point and summarized. The verifiable feature is defined as credible and approved information. The Correct feature means flawless, and agreeing with the truth or a fact or a standard. A consistent dimension means not showing any apparent conflict. The up-to-date information feature can also be defined as the timeliness of the information that declares if the information is outdated or up-to-date as required. Timely information comes in time and will not deliver delayed information. A well-organized dimension is a well-documented and well-presented information.

A simple information feature is straight to the point and unambiguous information; it also means freedom of complexity. Relevant feature summarizes all information that is meaningful for the user. Hence, the standard judges if the information has the potential to respond satisfactorily to a request (Friberg & Reinhardt 2009). Relevant information can vary from every request and is subjective. It is also defined as useful information for what the researcher is looking for. Flexibility in information feature can be defined as the adaptability to change and meet new requirements; it can also mean variable information. Subjective information is an opinion-oriented type of information. It is defined as the related or belonged information to a single person. Comprehensive information is inclusive and widely knowledgeable, covering broadly, including large proportions of information with details about relevant topics.

Objective information describes the judgment based on observable phenomena and how uninfluenced information is by emotions or personal prejudices. Objective information delivers a neutral. It also means dealing with facts or conditions as perceived without distortion by personal feelings, prejudices, or interpretations (8th International ISCRAM Conference, 2011). Objective information should be free from bias and only state facts.

1.5 Information Quality Significance

The research literature supports the importance of the concept of information quality. It contributes to the body of knowledge in two ways. First, it presents a pilot test of the information quality dimensions in a research setting. Second, it provides KISR researchers’ perception of the concept of information quality. Third, the study discusses one of the major current issues with information quality: information overload. The study results are expected to benefit KISR as a research institution and its researchers by applying the information quality standards on all the researches conducted from the institution.

1.5.1 The White House

Information quality is approvingly significant by the most sophisticated organizations and has concerned many organizations. In the United States, it is proved that information quality is referred to as data quality. According to the Kennedy report 2001, “all health care is information-driven, so the threat associated with poor information is a direct risk to the quality of healthcare service and governance in the NHS.” Meanwhile, the White House has set rules and guidelines to ensure the public’s quality of disseminated information. These guidelines focus on objectivity, utility, and integrity of information. Also, The Office of Science and Technology Policy (OSTP) of the White House has its laws and regulations regarding information quality, and the OSTP is responsible for the quality of the information the agency disseminates. The OSTP treats information quality as essential to every step of its development of information, including creation, collection, maintenance, and dissemination to be delivered to the public. OMB, which refers to the Office of Management and Budget, the OMB defines the quality of information as the consistent of utility, objectivity, and integrity. Utility in the White House is viewed as the usefulness of the information to the users. Objectivity consists of a presentation and substantive components. As the information must be presented in an accurate, clear, complete, unbiased manner, and substantively the information must be accurate, reliable, and unbiased. Unsurprisingly, these information quality guidelines have received much criticism and have been under review ever since.

1.5.2 Kuwait Organizations

In terms of Kuwait organizations, a study was made on investigating the strategic relationship between information quality and e-government benefits in Kuwait; it was found that there is a significant relationship between information quality and strategic benefits and the study highlights that improvements in different aspects of information quality can lead to a better organizational image. Especially, usability and usefulness features of information quality are the key influencers on both strategic benefits and institutional value (Alenezi et al., 2015). All the examples mentioned above emphasize the importance of information quality dimensions in an organized manner, and it precisely mentions highly important features for quality to look at amongst all the other quality dimensions.

1.5.2.1 Kuwait Institute for Scientific Research
1.5.2.1.1 History

Kuwait Institute for Scientific Research (KISR), located in Kuwait City, Kuwait, was found in 1967 by the Japanese organization titled Arabian Oil Company. In 1973, the Council of Ministers of Kuwait was entitled to operate KISR, decreed by the Emir of Kuwait. The Council made several changes to the objectives of the institute at that time. In 1981, KISR was reorganized to become an independent public institution for scientific research in Kuwait, even though it was first known as a semi-official entity (Al-Kharafi, El-Rayyes, & Janini, 1987). Since then, KISR is the largest institution of research in different domains of theoretical and applied sciences in Kuwait.

1.5.2.1.2 KISR Vision and Mission

In terms of vision, it is stated that the organization would like to be known globally as the most respected science, technology, and innovation (STI) hub and gateway of knowledge in the region. Therefore, KISR’s mission is to lead, and partner globally to enhance, deploy, and exploit the optimum science, technology, knowledge, and innovation for Kuwait. KISR would like to contribute to the growth of Kuwait and other nations with similar challenges and opportunities.

1.5.2.1.3 KISR Operation

According to Tétreault (1995), KISR conducts applied research that prompts the development of the national industry and carries out studies that help to conserve the environment and national resources. There are different departments and centers in the institute, each of which works in different ways to serve different purposes outlined by the management of the KISR. Alfadly (2011) states that each of the centers is managed by different management teams, which consist of many qualified scientists in their respective fields of work.

1.5.2.1.4 KISR Services

According to Alfeeli (2013), KISR provides different services relating to scientific, technological, and environmental research. Besides, KISR carries out training courses, seminars, surveys, and statistics, studies for organizations or individuals that wish to acquire the ability to do research professionally. KISR also offers business consultations to scientific projects.

1.5.2.1.5 KISR Roles and Functions

KISR has been divided into six main research centers. Each of them takes different roles and functions in conducting scientific research. Barreca (1990) and Alfeeli (2013) claim that the Environment and Life Sciences Research Center was established to carry research studies on the environment and life science. Its research sheds light on the discovery of approaches to prevent environmental disasters caused by human actions and influence on the environment. Second, Energy and Building Research Center are aimed at analyzing and discovering renewable energy. Its research can contribute to the development of the energy industry. Third, Petroleum Research Center plays a vital role in the development of the petroleum industry in Kuwait and countries with the petroleum sector. Its research is mainly about petroleum production and refining. Fourth, the Water Research Center is to research the management of water and find the optimum solution to wastewater.
Fifth, Techno Economics Division was set up to conduct studies relating to economics using quantitative methods and modeling. Therefore, Systems and Software Development Department develops new software and technological applications for particular demands and geographic information systems.

1.5.2.1.6 KISR and The Information Concept

In terms of information, KISR has its center for information management and monitoring. The National Scientific and Technical Information Center, commonly known as NSTIC. This center offers many services for KISR researches. It is the only center supporting researchers at KISR by providing a wide range of science information resources. These resources are made available online and offline so that researchers can easily access an internal information system. NSTIC was initially being set up to serve the purpose of researching KISR conveniently and effectively.

1.5.2.1.7 NSTIC Objectives

NSTIC is a vital division of KISR, which was developed for some strategic objectives. These objectives include improving scientific and technical information resources and collection, increasing the number of employees in librarianship and digital content, enhancing knowledge sharing for those doing research, and promoting the awareness of science and technology.

1.5.2.1.8 NSTIC Services

There are a number of both online and offline services that NSTIC offers. Firstly, this center provides a wide range of references for KISR’s researchers. Podsakoff et al. (2003) emphasize that reviewing related works is one of the essential practices in scientific research. Without referring to previous research studies, researchers cannot develop research hypotheses and make their research reliable. Therefore, one of the most basic services that NSTIC provides is making thousands of published papers available in its online and offline libraries. Getting access to this system, researchers at KISR can freely review the latest research studies. Secondly, NSTIC provides training services in user education and librarianship. New researchers who are not familiar with the information systems at KISR, NSTIC will help to train them on how to use the resources. Those who would like to become librarians can also participate in the training courses at NSTIC. Finally, NSTIC offers an exceptional service for its external clients, called “Current Awareness Services.” This service is helpful for those who wish to be informed or updated with the latest news, developments, and research papers in the fields of their interests. Based on the clients’ profile, NSTIC will recommend suitable resources to them automatically. NSTIC mainly works on supporting the scientific centers by giving all the information needed to the research projects in order to raise the development of the institute.

Additionally, NSTIC works to cater to the needs for information by governmental institutions, organizations, and different foundations and establishments. NSTIC has many objectives and goals in order to spread awareness and raise quality services, these goals and objectives are: to prove researchers the needed information to beneficial inside and outside the institute, to perform the role of the national library to house governmental literature and annual reports in the domain of science and technology and build the “Kuwait information sources” of references in respect to Kuwait, and to be at the beneficiaries disposal, and develop automated scientific applications to support research projects.
As this research has been designed on the aspect of information quality for KISR researchers, the result will be suggested for NSTIC to apply an informational quality standard for KISR researchers.

1.6 Problem Statement

Gathering and effective use of quality information may lead to several problems such as inaccuracy, overload, repetition, overlap, and many other inadequacies. This research has been designed to address this need for assessing the quality of information the researchers use in their work. This research addresses relevant dimensions of information quality. The policies, criteria, and qualities of information need to be systematically examined. Since KISR is the largest institution of research in different domains of theoretical and applied sciences in Kuwait, this research undertakes to assess information quality these researchers in KISR use.

1.7 Research Questions

This researcher addresses the following research questions:
⦁ How do KISR researchers use information about the following dimensions of quality?

⦁ What are the most important information quality features in KISR researcher’s perception?

⦁ In what ways does information overload affect the quality of the information sources, and what are the best ways to minimize it?

1.8 Research Objectives

⦁ to examine the information quality dimensions with KISR researchers and explore their perceptions toward the concepts of “Information Quality” and “Information Overload.”
⦁ to address the highest important dimension of information quality
⦁ To discuss the information quality barriers such as information overload and find solutions to minimize it.

1.9 Nature of the Study

The study is a quantitative analysis to explore KISR’s researcher’s knowledgeability towards the concept of information and information overload. Data were collected using a survey instrument and employing a Likert scale to measure KISR researchers’ perceptions of the information quality.

1.10 Organization of of the Study

This study is presented in five chapters. Chapter 1 introduces the study, highlighting the importance of information quality globally and locally, defining the information quality dimensions and viewing the information quality problem with information overload. Chapter 2 provides a review of literature related to information quality and information overload and case studies about the issues regarding it. Chapter 3 details the methodology employed in the study. Chapter 4 presents the analysis of the data, including descriptive analysis and interpretations and hypothesis testing. Chapter 5 presents a discussion of the implications of the findings from the analysis followed with limitations of the study and recommendations for further research.
Chapter 2
Literature Review

2.1 Introduction

This chapter presents a review of the literature on information quality, information evaluation, information quality crisis, information quality and business, KISR, information overload, and solutions. This chapter reveals different studies and real-life examples of information quality in an organizational and research setting.

2.2 Information Quality

The quality of information is an essential aspect of any scientific research. Information quality is a critical concern for researchers. Researchers must remain aware of the value of the sources that support their research. Researchers must consider the work previously carried out to build their research and to create new knowledge. Every innovative research should aim to complete missing aspects of the literature. Thus, emphasis on reviewing and expanding the literature will augment research knowledge comprehensively. Therefore, research information has to come from reliable sources, prove relevant to the community, and remain ahead of the pressures of the current timeframe in terms of new issues facing humanity and the world. Therefore, scientific research should be able to check all quality dimensions to present trustworthiness and credibility. Searching process and information collecting can lead to problematical concerns. These concerns can root to endless obstacles for a researcher during their searching process. These concerns can be addressed as inaccurate, useless, irrelevant, repetitive, and duplicated. This searching process can consume immense effort and time from the researcher and might begin an information overload issue latterly. The main object of this study is to emphasize the importance of information quality of scientific researchers, evaluate the quality dimension of information, discuss the information overload issue, and find a solution toward it.

2.2.1 Information Quality Definition

Information quality is defined by a variety of attributes whereby when these attributes are right, the information is considered of high quality. Information quality is also referred to as interchangeable data quality, described as data that is “fit-for-use” by data users Kandari (2010). According to Madnick, et al. (2009) information can be defined as fitness of use as this term is the most adopted in the information quality literature, they would argue that the consumer of data/information is the person who decides about the information fitness for use. Mouzhi & Helfert (2007) stated that data quality had separate definitions from information quality; it can be defined as the information that meets the user’s specifications or requirements.

As a matter of fact, information quality has become a critical issue in various fields of scientific use and the general populace. The awareness of information quality has proliferated with the revolution of the information era (Madnick et al., 2009). With the open web, social media, blogs, and big data finding the right information with high quality has become more complicated. Information quality plays a massive role in terms of the decision-making process. The quality of information can involve saving lives, diminishing global or local conflicts, reducing panic, and avoiding embarrassment. Poor quality of data can cause poor decisions hence prosaic outcomes and studies.

2.2.2 Information Quality Dimensions

Information quality has several dimensions; these include currency, accuracy, trustworthiness, credibility, relevancy, comprehensiveness, completeness, e.t.c. As Kahn, Strong, and Wang (2012) defined accuracy: is the correctness of the information, while completeness: is the measure of the information’s comprehensiveness. It refers to the extent to which the data gives a complete picture of reality and not just part of the picture. According to Sellitto, Burgess, & Hawking (2017), some characteristics of quality information include timelessness, appropriateness, and reliability. Timelessness refers to the stable situation of information over time. Information stability through a timeframe means better quality. On the other hand, appropriateness refers to the suitability of the information to the receiver, while reliability is the extent to which data can be relied on.
One of the main attributes that concern researchers in their searching process are the relevance of a particular source or study. Researchers need to ensure that their research builds on the field of knowledge available at a given time. This field of knowledge is crucial because it keeps the researcher focused on discovering new insights, rather than irrelevant ones. However, they need to have a clear basis for declaring particular insights as new and others as old. Researchers need to evaluate the elements they include in their research to conclude the value of information from one source or another. The best basis for this type of conclusion is the expertise of a given researcher (Batini & Scannapieco, 2016).

Another primary concern for researchers is the age of a given document for research purposes in which the age of a source should be up-to-date. Research that happened a long time ago is difficult for one to examine because much of the logic served as the basis for this research may be outdated. However, sometimes old information can be timeless and considered great quality, but this depends on the researcher’s needs to balance this with knowledge of what information is within the scope of reliable knowledge and what information is outdated. Researchers’ work is a process of combinations based on previous work and conclusions. The judicious researcher, therefore, needs to balance and remain aware of the developments within their field. Today that includes understanding the role of information technology in these areas (Barata & Cunha, 2015; Batini & Scannapieco, 2016; Restuccia et al., 2017). This process of balancing requires researchers’ expertise about the way they work and their connection to the source of information.

Besides, other significant researchers’ concerns are information credibility, which comes from the bodies that stand for the given source of information. Researchers have to make several decisions going forward with their research. They cannot reinvent the wheel and must, therefore, collect information from several different sources. However, they must have a way of knowing that a source should be in line according to the quality dimensions such as being verifiable and trustworthy. A sole individual or an independent source can have access to truthful information. However, verification of this information or its trustworthiness is both expensive and challenging (Keller et al., 2017). Sources verification requires an in-depth analysis and special programs to check sources originality such as (SDV) Source Data Verification and (SDR) Source Data Review on is to check quality and compliance of source and the other to check the data collection system compared to the source of information which verify that the data was recorded correctly (Adler, 2019).

2.3 Criteria of Sources’ Evaluation

Several researchers have suggested that the criteria used to evaluate and choose sources affect the quality of the information in a scientific study. Friberg, Prödel, and Koch (2011) suggest five criteria that can help the evaluation and choice of sources credibly. The criteria are currency, coverage, authority, objectivity, and accuracy. One should first check the source’s currency, that is, whether the publication date of the source is sufficient to cover the requirements of a research topic. What is more, one should consider whether the source is relevant or adequately covers the topic’s needs. Besides, the authority and credentials of the authors should be checked before deciding to use a source. The authors should be screened to ascertain their expertise and knowledge of the topic in question. One should also check the accuracy and objectivity of a source to ensure that its information resonates with the research topic. When any of the above aspects are absent in a particular material, the source does not qualify for scientific research.

Walby and Luscombe (2017) suggest a different way of selecting the best sources for scientific research. One of the criteria is that the source should be a topic that one can find interesting. Researchers claim that it is easier to generate better ideas from something that one can actively react to positively or negatively. One should, therefore, have some background information or opinion on the topic. The other criteria are that the source should be academic, either a published book or journals. Nonacademic sources, such as websites and newspapers, should not be used as sources of information for scientific research since they do not only contain undetailed information but are also challenging to cite (Alzougool et al., 2013). Besides, some newspapers contain biased information, which makes them unreliable. A good source, according to (Lee et al., 2012), should also contain a straightforward language that is easy to understand. A source that contains too many vocabularies may inconvenience a researcher since they would spend more time referring to the dictionary at the expense of the cardinal ideas presented by the author. The length of a source is also an important aspect to be considered (Pontis et al., 2017). Depending on the topic of the study, a researcher should choose a lengthy source to cover the topic’s requirements. In the final analysis, a higher score in each of the above attributes suggests that the information is of good quality, and a lower score indicates that the quality of the data is poor.

2.4 Information Quality and Business

Many studies have discussed the outcomes of poor-quality information. Jiménez-Pernett et al. (2010) explained that research information is as useful as the source used to find it. Poor sources result in low information quality, which associates with several problems. Some of the issues, as viewed by business aspects; as Gorla et al. (2010) stated that poor information quality could include; poor decision making lost opportunity, lost revenue, business inefficiencies, and mistrust. Information is mainly collected to be used in decision-making. Inadequate information results in poor decisions, which, in turn, lead to more negative consequences, such as business inefficiencies. In business, for firms that depend on research reports to make orders for items and any other business activity, poor information quality could result in expensive reworks and fund diversion activities. Another effect of poor-quality information is mistrust. For instance, when an organization uses poor quality information to report its financial position, it may lose its reputation and customer confidence (Sharabi, 2014). Besides poor decisions, business inefficiencies, and mistrust, poor-quality information, according to Gorla et al. (2010), leads to missed opportunities and lost revenue. Tomich, Kilby, and Johnston (2018) explain how quality information problems could lead to missed opportunities. The authors used an example of a marketing company that conducted substandard research and ended up ignoring some critical product development information. The information was later discovered by a rival company that utilized it and gained a competitive advantage. Besides lost opportunities, quality information problems lead to lost revenue. Peltier, Zahay, and Lehmann (2013) explain how unreliable customer data can cause a failure in sales. In a multichannel selling, poor data can result in inaccurate targeting and communications, which could be detrimental to a business’s profitability. Other studies, such as Green (2012), also support the idea that poor quality information can waste revenue. Green relates the imperfect quality information to a shortage of sales that lead to lower revenues.

2.5 Information Quality Crisis

A study in Africa has shown that one of the problems that scholars face when conducting research is that data and information in Africa are of poor-quality. Indeed, the data does not comprehensively exist, or it lacks in terms of validity, reliability, and credibility. This results to many problems that Africa as a continent, encounters such as limiting the continents ability to generate a pertinent body of knowledge and also poor data quality prevents analysts from generating the evidence that decision-makers need to make the right decisions in influencing the development of the continent (Kinyondo & Pelizzo, 2018). Eventually, this study shows that information and data quality are crucial to have a proper system in the country, make the right decisions to run organizations, and gain credibility.

2.5.1 Information Quality & Scientific Research

In a scientific setting such as medicine & laboratory work; poor quality of information can cause disastrous consequences. Medical research and Information quality have a very critical relationship. Clinical information can either save lives or cause losses if it was not accurate and misinformed. While medicine is in continuous development and has enormous publishing of medical research in the past years, yet it experiences more complex challenges on how to deal with poor research (Human Reproduction, 2018). According to MacLeod et al., 2014; Moher et al., 2016, 85% of all research funding are wasted due to inappropriate research questions, flawed study design, irrelevancy, faulty execution, poor presentation and non-publication (MacLeod et al., 2014; Moher et al., 2016). Besides, Pirani (2004) reported from an Australian hospital that one piece of wrong biopsy information caused a patient’s fatality. Many are also familiar with the Institute of Health’s estimate of between 44,000 and 98,000 deaths a year in US hospitals due to medical errors (Kohn et al., 2000). As mentioned earlier, the quality of medical publications should go through verification systems such as (SDV) Source Data Verification and (SDR) Source Data Review to ensure that it can be published in high-quality standards and flawlessly. Nevertheless, the quality of the information in the health industry means the safe use of drugs, thus, saving lives.

2.5.2 New SARS-CoV2 & Information Quality

Moving to the current times, with the new (SARS-CoV-2) or (COVID-19) Coronavirus disease, which has been spreading globally in a very short-time period, the number of cases and fatality rates are increasing day by day. Misinformation has spread wide as well, obscuring credible sources and factual information. Scientists are destressed to find a vaccinate for the disease, and physicians are restless to save lives. While this pandemic is going viral, fake news, rumors, and uncertain studies are spreading virally. Some studies are struggling with quality. These unreliable studies can lead people to panic. Ioannidis (2020) stated, while the new coronavirus is viral, a scientific study was published claiming that: “the new coronavirus spike protein bears uncanny similarity with HIV-1 proteins”. This study has spread widely on March 5, 2020. Shortly, this paper was promptly showed to be flawed and was redacted by the author within days.Nevertheless, this paper has caused significant confusion to the scientific community and public health. Although the paper, redacted promptly, the damage was caused. Thus, conspiracy theorists used the paper, which was then plied by the anti-vaccination campaign. Along the lines, “The Lancet” is an independent, international weekly medical journal that publishes the latest high-quality researches. During the pandemic spread, The Lancet published an account from two front line Chinese nurses fighting new Coronavirus; The Lancet withdrew the paper declaring it was not a first-hand account. Misinformation and uncertainty did not only have the scientific community confused but also led people to panic buying, depleted surgical masks supplies, and led to a shortage of medical personnel yet might risk their lives. Masks are meaningless for the healthy general population, but it is necessary for an infected person and his caregiver (Ioannidis, 2020). These examples are few for what is happening with the pandemic, the medical community is risking their lives for our safety, yet rumors and uncertainty are still going viral. The world is having a critical time for all humanity, and people must look for the right information and evaluate it before believing uncertain studies and spreading it to others.

2.5.3 Constructions Safety & Information Quality

On the other hand, Attah, (2019) conducted a study about the relationship between information quality and construction safety, the study came to a concern about the fatal occupational injury in the constructions site in the United States, “Fatality rates among specialty trade contractors made up the largest percentage of fatalities in construction at 62% per year.” said Attah, (2019). The study emphasizes on the role of on the importance of having a high quality of information to avoid constructions accidents; the study hypothetically indicated that there were no statistically significant differences to support that relevance, accuracy, timeliness, and completeness of information in construction safety plans predicted construction safety of specialty trade contractors. However, the study shows that these quality dimensions are not related to construction accidents, but other quality dimensions can emphasize a role in reducing accidents in specialty trade contractors. These dimensions of information quality can be comprehensiveness, well-documented, well-presented, and unambiguous. Along the lines, Attah (2019) mentioned that further research is needed to find a possible correlation between safety and information quality in the construction sector.

2.6 Kuwait Institute of Scientific Research

As mentioned in Chapter 1, Kuwait Institute for Scientific Research (KISR) was founded in 1967. In 1981, KISR was reorganized to become an independent public institution even though it was first known as a semi-official entity (Al-Kharafi, El-Rayyes, & Janini, 1987). It was envisioned that KISR would be known globally as the most prestigious science, technology, and innovation (STI) hub and gateway of knowledge in the region. KISR’s mission is to lead, and partner globally to enhance, deploy, and exploit the optimum science, research, technology, knowledge, and innovation for Kuwait’s sake. KISR would like to contribute to the growth of Kuwait as well as other nations with similar challenges and opportunities. Speaking of information quality for an institution like KISR, which is specialized in scientific research, the information is given or taken should be of high standards, as international institutions have internal qualitative standards by which they must commit.

AlMatrouk & Juszczak (2007) state that KISR needs an improved performance in research departments, where progression is highly required through new knowledge and technologies to be utilized by the stakeholders. The study conducted correlated improved research skills and data collection, which showed an improvement in work development and performance in KISR research activities. The study includes analysis of KISR literature, which resulted in KISR as a scientific institution is not following a system to measure the performance of research and its collection, and that could lead KISR with no avenues to which it can improve their performance. As a matter of fact, this study reveals that if there is no measurement system in a research institution to evaluate information, then the research quality and institution performance could degrade. Thus, it is imperative to evaluate research performance and whether it falls in line according to quality standards.

2.6.1 Challenges in Scientific Publication in High-Quality Journals

Neelamani (2020) reflected on the challenges in scientific publications in high-quality journals. He suggested that the best way to a researcher assures his work is published in a high-quality journal is by knowing it can be accepted to be published. An important consideration is to make sure the information is up-to-date and useful for global society. It is also essential to demonstrate the usefulness of the work for socioeconomics. Researchers ought to ensure that the data used for the paper is accurate and authentic. Also, sources for the literature review should be up to date, and the language of writing should be acceptable. Speaking about the correlation of possibility for the paper to be published and the institution’s reputation, the more the institution is well known and has a high reputation of high-quality journal publications, the higher the possibility for the researchers to have their paper published.
2.7 Information Overload

The impact of information load can determine the decision-making process. Information overload refers to a situation where there is more information than one can handle (Davis, 2011). Information overload is a flood of potentially relevant information that originates from any type of searching process. Information overload is also known by different synonyms such as; information overabundance, infobesity, information pollution, information fatigue, social media fatigue, information anxiety, Data smug, info stress, infoxication, reading overload, communication overload, information violence, etc.. (Bawden & Robinson, 2020). According to Ownen (1999), information overload is not a function of the volume of information. It is a gap between the volume of information and the tools we have to assimilate the information into useful knowledge”. It can also be defined as the vast quantities of wide-ranging sources of information regularly received at a rate that limits assimilation and increased by unsolicited information (Rochat, 2002).

Bawden & Robinson (2020) stated: “As long as there has been information recorded, there has been that perception humanity has been overloaded by it.” Information Overload has been listed as an important factor in science, medicine, education, politics, governance, business and marketing, planning for smart cities, access to news, personal data tracking, home life, use of social media, and online shopping, etc.

2.7.1 Information overload problems

Along the lines of Lee, Son, and Kim (2016) claim that information overload has existed for over a century, but its impact has been more profound in recent times than never. One of its implications is that it affects the quality of decision making. Overload causes psychological and physiological problems, which reduce the quality of decision making (Karr-Wisniewski & Lu, 2010). According to The Reuters survey of business managers “Dying for information” (1996), it has stated facts contributing with the information overload in the workplace: Two-third of decision-makers thinks that information overload had caused a loss of job fulfillment, others think the overloaded information have caused issues in their relationships. Nearly half of decision-makers admit that it is difficult to handle the amount of enormous information they received, causing many work delays and, therefore, facing difficulties in making important decisions. Simpson and Prusak (1995) argue that the main reason for the overload is people’s inability to understand how the information process gives value to the information itself. The main four processes of information are truth, accessibility, guidance, and weight. Some of these attributes can be referred to as information quality dimensions as well. Truth is the most required attribute, and it is one of the most important information quality requirements and accessibility, which certainly means the availability of information. However, guidance and weight refer to the directions of information and relevancy of the information action (Rochat, 2002). In some research firms, information overload is a big issue that can devalue a sound information system. Less research has been carried out to determine how the problem can be solved. Therefore, there is a need for stakeholders to find the most appropriate way of reducing information overload. Among the topics that need to be researched in this area include ways of reducing quality information problems and how invalid sources of information can be made reliable or used reliably.

2.7.2 Information Overload Solutions

The problem of information overload in the first place occurred when individuals did not realize the importance of the information that we daily get exposed to and how to deal with it in the first place. Minimizing information overload can be done through several methods such as; selection, avoidance, filtering, and discarding methods. These methods all drives under information selection. Information selection is an essential component of decision making. In terms of selection, the most crucial questions for researchers is “how much resources should be allocated to the search?”,(Simon, 1972). Information avoidance and filtering methods are also best described to minimize the overload of information. According to Farhoomand & Dury (2002), many decision-makers suffer from information overload in their workplace. Solutions were put forward to filter information, to eliminate sources, to delegate and to prioritize.

Besides, according to Bawden & Robinson (2020): the best ways to avoid information overload while searching, is filtering, withdrawing, queuing, and satisficing. A better design of information systems can play a massive role in the selection process for the information. Furthermore, one of the more crucial solutions to deal with overloaded information Floridi (2014) states: “We have shifted from the problem of what to save to the problem of what to erase. Something must be deleted or never recorded in the first place.”
One of the organizational solutions for the overloaded information is adding more value and collaborations between the information provider or specialist and the recipient (Rochat, 2002). This can be translated into emphasizing communication between the provider and the recipient and setting clear and insightful goals for the aimed projects, studies, or research

According to Walgrave, S., & Dejaeghere, Y. (2017) a study conducted on elite politicians regarding information selection, they follow specific rules to overcome information overload. 1st rule is to organize and prioritize the incoming information; second is having specific heuristics or shortcuts to follow; third is to trust their procedures and have the self-confidence that they are making the right informed choices and accept their mistakes. Figure (1)

Figure (1) Walgrave, S., & Dejaeghere, Y. (2017)

All in all, information quality for scientific research is a topic that has been widely explored in almost all aspects. Since information overload has existed for a long time with profound consequences, more research should be dedicated to informing its dangers and how they can be avoided.

2.8 Conclusion

Every researcher must wrestle with the quality of their information to remain convinced of its reliability. This aspect ensures that their research findings are worth relying on insofar as any concern is merited. Moreover, this information allows the researchers to carry out meaningful examinations of the facts that matter within their field of expertise. The first method of verifying this information’s value is to ensure that it comes from institutions that have a record of being trustworthy. Secondly, researchers must ensure they retain the competence to evaluate relevant and irrelevant sources of information. Finally, researchers have to remain abreast of the latest outcomes in their field. This move ensures their information is trustworthy, relevant, and up-to-date.

References

Adler, C. (2019). Source Data Review (SDR) vs. Source Data Verification (SDV).
Retrieved from https://forteresearch.com/news/sdr-vs-sdv-examining-the-differences/

Alenezi, H., Tarhini, A., & Sharma, S. K. (2015). Development of quantitative
model to investigate the strategic relationship between information quality and e-government benefits. Transforming Government: People, Process and Policy.

Ali, F. (2019). How reliable is Google Scholar? . Retrieved from
https://researchhub.org/how-reliable-is-google-scholar

Al-Kharafi, F., El-Rayyes, N., & Janini, G. (1987). Science research in Kuwait –
a bibliometric analysis. Journal of Information Science, 13(1), 37-44.

AlMatrouk, H. S., & Juszczak, M. D. (2007). Knowledge Impact on Measurement:
A Conceptual Metric for Evaluating Performance Improvement (PI) at the Kuwait Institute for Scientific Research (KISR). International Journal of Industrial and Manufacturing Engineering, 1(6), 249-256.

Alzougool, B., Chang, S., Gomes, C., & Berry, M. (2013). Finding their way around: International students’ use of information sources. Journal of Advanced Management Science, 1(1), 43-49.

Attah, A. (2019). The Relationship Between Information Quality and Construction Safety.

Balcerzak, A. P., &Pietrzak, M. B. (2016). Human development and quality of institutions in highly developed countries. Financial Environment and Business Development Eurasian Studies in Business and Economics, 231-241. doi:10.1007/978-3-319-39919-5_18

Bawden, D., & Robinson, L. (2020). Information Overload: An Overview.
Barata, J., & Cunha, P. R. (2015). Synergies between quality management and information systems: A literature review and map for further research. Total Quality Management & Business Excellence, 28(3-4), 282-295. doi:10.1080/14783363.2015.1080117

Batini, C., & Scannapieco, M. (2016). Information quality dimensions for maps and texts. Data-Centric Systems and Applications Data and Information Quality, 53-86. doi:10.1007/978-3-319-24106-7_3

Batini, C., & Scannapieco, M. (2016). Data and information quality.
Cham, Switzerland: Springer International Publishing. Google Scholar, 43.

Bettis, R. A., Helfat, C. E., & Shaver, J. M. (2016). The necessity, logic, and forms of replication. Strategic Management Journal, 37(11), 2193-2203. doi:10.1002/smj.2580

Boutron, I. (2016). Increasing value and reducing waste in biomedical research:
who’s listening?. The Lancet, 387(10027), 1573-1586.

Davis, N. (2011). Information overload, reloaded. Bulletin of the American Society for Information Science and Technology, 37(5), 45-49.

Dictionary Cambridge (n.d) retrieved from
https://dictionary.cambridge.org/dictionary/english/accessibility

Elmore, D. E., Guayasamin, R. C., & Kieffer, M. E. (2010). A series of molecular dynamics and homology modeling computer labs for an undergraduate molecular modeling course. Biochemistry and Molecular Biology Education, 38(4), 216-223.

English, L. P. (1999). Improving data warehouse and business information quality:
methods for reducing costs and increasing profits (Vol. 1). New York: Wiley.

Eppler, M. J., & Mengis, J. (2003). A framework for information overload research in
organizations. Università della Svizzera italiana.

Farhoomand, A. F., & Drury, D. H. (2002). Managerial information
overload. Communications of the ACM.

Floridi, L. (2014A). The fourth revolution: how the infosphere is reshaping human reality.
Oxford: Oxford University Press.

Floridi, L. (2014). Big Data and information quality. In The philosophy of information
quality (pp. 303-315). Springer, Cham.

Friberg, T., Prödel, S., & Koch, R. (2011). Information quality criteria and their importance for experts in crises. In Proceedings of the 8th International ISCRAM Conference (pp. 145-149).

Friberg, T., & Reinhardt, W. (2009). An Empirical Study on Criteria for Assessing Information Quality in Corporate Wikis. In ICIQ (pp. 217-230).

Gorla, N., Somers, T. M., & Wong, B. (2010). Organizational impact of system quality,
information quality, and service quality. The Journal of Strategic Information Systems, 19(3), 207-228

Green, A. (2012). Understanding the value of customer data. Journal of Direct, Data, and Digital Marketing Practice, 13(3), 221-233.

Grudzień, Ł, &Hamrol, A. (2016). Information quality in design process documentation of quality management systems. International Journal of Information Management,36(4), 599-606. doi:10.1016/j.ijinfomgt.2016.03.011

Ioannidis, J. P. (2020). Coronavirus disease 2019: the harms of exaggerated information
and non‐evidence‐based measures. European journal of clinical investigation.

Jarke, M. and Y. Vassiliou. Data Warehouse Quality: A Review of the DWQ Project.
in Proceedings of the 1997 Conference on Information Quality. Cambridge, MA: pp. 299-313, 1997.

Kandari, J. (2010). Information quality on the World Wide Web: a user perspective.
Kane, G. C., &Ransbotham, S. (2016). Research note—content and collaboration: An affiliation network approach to information quality in online peer production communities. Information Systems Research, 27(2), 424-439. doi:10.1287/isre.2016.0622

Karr-Wisniewski, P., & Lu, Y. (2010). When more is too much: Operationalizing technology overload and exploring its impact on knowledge worker productivity. Computers in Human Behavior, 26(5), 1061-1072.

Keller, S., Korkmaz, G., Orr, M., Schroeder, A., & Shipp, S. (2017).
The evolution of data quality: Understanding the transdisciplinary origins of data quality concepts and approaches. Annual Review of Statistics and Its Application, 4(1), 85-108. d

Kinyondo, A., & Pelizzo, R. (2018). Poor Quality of Data in Africa:
What Are the Issues? Politics & Policy, 46(6), 851–877. https://doi-org.kulibrary.vdiscovery.org/10.1111/polp.12277
oi:10.1146/annurev-statistics-060116-054114

Kohn, L. T., Corrigan, J., & Donaldson, M. S. (2000). To err is human: building a safer
health system (Vol. 6). Washington, DC: National academy press.

Kuwait Institute for Scientific Research. (n.d.). Retrieved February 1, 2019, from http://www.kisr.edu.kw/en/

Lee, A. R., Son, S. M., & Kim, K. K. (2016). Information and communication technology overload and social networking service fatigue: A stress perspective. Computers in Human Behavior, 55, 51-61.

Lukyanenko, R. (2016). Information quality research challenge. Journal of Data and Information Quality, 7(1-2), 1-3. doi:10.1145/2856038

Glasziou, P. (2014). Biomedical research: increasing value, reducing waste.
The Lancet, 383(9912), 101-104.
Madnick, S. E., Wang, R. Y., Lee, Y. W. & Zhu, H. (2009). Overview and framework
for data and information quality research. Journal of Data and Information Quality (JDIQ), 1(1), 1-22.

Markey, R., Ravenswood, K., & Webber, D. J. (2012). The impact of the quality of the work environment on employees’ intention to quit. Economics Working Paper Series, 1220, 1-35.

Merriam W. (n.d) Retrieved from
https://www.merriam-webster.com/dictionary

Merriam W. (n.d) Retrieved from
https://www.merriam-webster.com/thesaurus

Menditto, A., Patriarca, M., & Magnusson, B. (2007). Understanding the meaning of
accuracy, trueness Friberg, T., Prödel, S., & Koch, R. (2011, May). Information quality criteria and their importance for experts in crisis situations. In Proceedings of the 8th International ISCRAM Conference (pp. 145-149).
and precision. Accreditation and quality assurance, 12(1), 45-47.

Mouzhi, G., & Helfert, M. (2007). A review of information quality research. In Proceedings
of the 2007 International Conference on Information Quality.

Owen, J. (1999). Coping with Information Overload. Carreerbuilder.

Peltier, J. W., Zahay, D., & Lehmann, D. R. (2013). Organizational learning and CRM
success: A model for linking corporate practices, customer data quality, and performance. Journal of Interactive Marketing, 27(1), 1-13

Pirani, C.: How safe are you hospital? The Weekend, Australia (2004)

Pontis, S., Blandford, A., Greifeneder, E., Attalla, H., & Neal, D. (2017). Keeping up to date: An academic researcher’s information journey. Journal of the Association for Information Science and Technology, 68(1), 22-35.
Price, R., & Shanks, G. (2016). A semiotic information quality framework: Development and comparative analysis. Enacting Research Methods in Information Systems, 219-250. doi:10.1007/978-3-319-29272-4_7

Protect Us From Poor-Quality Medical Research (2018), Human Reproduction, Volume 33, Issue 5, Pages 770–776, https://doi.org/10.1093/humrep/dey056
Redman, T. C., & Blanton, A. (1997). Data quality for the information age.
Artech House, Inc..

Restuccia, F., Ghosh, N., Bhattacharjee, S., Das, S. K., &Melodia, T. (2017). Quality of information in mobile crowdsensing. ACM Transactions on Sensor Networks,13(4), 1-43. doi:10.1145/3139256

Reuters Business Information. (1996). Dying for Information: An Investigation into
the Effects of Information Overload in the UK and Worldwide.

Rochat, C. (2002). Possible solutions to information overload. SA Journal of Information
Management, 4(2).

Scheffler, I. (1982). Science and subjectivity. Hackett Publishing.

Sellitto, C., Burgess, S., & Hawking, P. (2017). Information quality attributes associated with RFID-derived benefits in the retail supply chain. International Journal of Retail & Distribution Management, 35(1), 69-87.

Simon, H. A. (1972). Theories of bounded rationality. Decision and organization
1(1), 161-176

Sharabi, M. (2014). Today’s quality is tomorrow’s reputation (and the following day’s business success). Total Quality Management & Business Excellence, 25(3-4), 183-197.

The White House, (2017) President Barack Obama. Information Quality Guidelines.
Office of Science and Technology Policy. Retrieved from
https://obamawhitehouse.archives.gov/administration/eop/ostp/library/compliance/infoquality

Tomich, T. P., Kilby, P., & Johnston, B. F. (2018). Transforming agrarian economies: Opportunities seized, opportunities missed. New York, NY: New York Cornell University Press.

Walby, K., & Luscombe, A. (2017). Criteria for quality in qualitative research and the use of freedom of information requests in the social sciences. Qualitative Research, 17(5), 537-553.

Walgrave, S., & Dejaeghere, Y. (2017). Surviving information overload:
How elite politicians select information. Governance, 30(2), 229-244.

Wand, Y. and R. Y. Wang, Anchoring Data Quality Dimensions in Ontological Foundations.
Communications of the ACM, 39(11) 1996, pp. 86-95