Knowledge Management System Adoption to Improve Decision ...

Research ArticleKnowledge Management System Adoption to ImproveDecision-Making Process in Higher Learning Institutions in theDeveloping Countries A Conceptual Framework

Hanan Mohammed Oumran 1 Rodziah Binti Atan1 Rozi Nor Haizan Binti Nor1

Salfarina Binti Abdullah1 and Muaadh Mukred 2

1Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang 43400 Malaysia2Sanarsquoa Community College Mareb Street Al-Hushaishiya Road Sanarsquoa Yemen

Correspondence should be addressed to Hanan Mohammed Oumran gs46483studentupmedumy andMuaadh Mukred muaadhscceduye

Received 7 May 2021 Revised 15 June 2021 Accepted 26 June 2021 Published 7 July 2021

Academic Editor Lazim Abdullah

Copyright copy 2021HananMohammedOumran et al-is is an open access article distributedunder theCreativeCommonsAttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Currently higher learning institutions (HLIs) are facing their most challenging problem in inefficient information management-e knowledge management system (KMS) application calls for providing several benefits to lecturers and students producingdaily information documenting records for evidence of a transaction and eventually improving the decision-making processKnowledge management can be coupled with fuzzy logic to deal with imprecision and uncertainty of data in a KMS -e ICTdynamic development has shifted the HLI operations from manual to electronic-based handling of related information KMS isone of the systems that are of significant consideration in this regard Nevertheless such a system has not been extensively adoptedas expected due to usersrsquo rejection of its use In the present paper the factors affecting the decision to adoptreject KMS arehighlighted-e study is qualitative and entails a critical review of the related literature concerning the topic backed by interviewsKMS experts working with highly reputable HLI were interviewed A total of 11 factors were focused on in light of their effect onthe decision to adoptreject KMS as argued by the technological adoption theories and literature review All the factors werevalidated and placed in ranks by the experts From the results a novel conceptual framework of KMS adoption was developed forLibyan HLIs to bring about technology adoption and improved decision-making

1 Introduction

Many countries have realized the need to adopt an outcome-based approach to provide ongoing educational improve-ment for increasing unemployed graduates Higher educa-tional institutions have responded by concentrating onstudentsrsquo sufficient professional and career preparationthrough the stress on market demands of specific outcomesor abilities Such outcome-based approaches are directedtowards assessing the studentsrsquo performance and knowledgemitigating the gap between university learning and practicein their actual careers [1ndash3]

HLIs in various countries generally acknowledge thevalue of information in the management and decision-

making processes-is has paved the way for further systemsdevelopment computer hardware software and Internetusage Furthermore an information system refers to anorganized integration of people hardware software chan-nels of communication and data resources functioning intandem to collect transform and spread information in theorganization [4] In this combination HLIs will find theOBE application and implementation aided by its evalua-tion and particular system invaluable [3] In the KMSinformation is furnished for education institutions to makedecisions and assessments and oversee and evaluate edu-cational activities [5]

In the context of educational institutions adopting KMScan minimize the education demand-supply gap [6 7] and

HindawiMathematical Problems in EngineeringVolume 2021 Article ID 9698773 15 pageshttpsdoiorg10115520219698773

such notion has resulted in heightened awareness and in-vestment in KMS innovation in the majority of nations toenhance their system of education [8 9] Additionally KMSadoption that constitutes the education provision has beenconsidered a set of processes to be implemented to enhancethe effectiveness of HLI in terms of its performance andobjectives achievement In literature several barriers to KMSadoption have been evidenced by studies in the context ofdeveloping countries including Garrett [10] Shroff et al[11] and Alharthi [12] in the Libyan context

Such studies revealed that the adoption of technologyand systems specifically KMS is still at the initial stages[9 12ndash14] Most studies of this caliber have stressed threemain barrier categories namely human-related organiza-tion-related and technology-related barriers (eg [15 16])Heeks [17] reported that information systems combinedwith technical social organizational and environmentalfactors have been successful although evidence backed bytheory regarding adopting KMS at the individual and en-vironmental level is still scarce

In Arab nations Gholam and Kobeissi [18] reported theabsence of technology implementation for evaluation tosupport professional development In this regard Alfahadiet al [19] and Alharthi [12] presented a critical look at theimplemented evaluation process in Libya that lacks tools andtechniques leading to an ambiguous view of the studentsrsquoperformance Evaluation procedures in Libyan institutionsneed reformation for validation realism and authenticimplementation and use [20]

-e stress of the above discussion is the requirement ofexamining innovation and technology adoption to allowhigher education institutions competitiveness and ability todevelop into global leaders in the educational platform-us a clearer picture of such adoption is called to extendand promote learning innovations adoption and usage [21]

Moreover KMS use and adoption in educational in-stitutions for their improvement are part of the advancementof technology Research studies of this caliber have high-lighted KMSs as a crucial tool in assessing the process ofevaluation (eg Bartlett [22])

-is manuscript is structured to include KMS and thedecision-making process in Section 2 after the introductionSection 3 presents related works on KMS adoption andSection 4 provides the methodology Discussion and in-terpretations of the finding are presented in Section 5 andSection 6 is dedicated for the conclusion

2 Knowledge Management Systems andDecision-Making Process

Information system (IS) refers to integrating a group ofcomponents used to gather store and process data todistribute the information and knowledge obtained [23] Itrefers to a combination of hardware software and tele-communication networks built to collect create and dis-tribute required data generally in organizations On theother hand KMS consists of a class of information systemsemployed to manage the organizationrsquos knowledge [24]KMS is a category of IS used in organizations to manage

knowledge with the help of IT-based systems created toprovide support and improvement to the processes in-volving the creation storage retrieval transfer conversionprotection and application of knowledge [25 26]

KMS refers to an IT-based system developed to providesupport and enhancement to the processes of organizationsrelating to the creation storage transfer and application ofknowledge [24] It was similarly described by Alatawi et al[27] as a system created and designed to provide theknowledge needed for decision-making and tasks under-taking among decision-makers and users [27] As Alavi andLeidner [24] definition corresponds to the present studyrsquosobjective of examining the adoption of KMS in Libyanuniversities it best describes the university practices set-tings and processes when it comes to KMS adoption-erefore their definition is adopted Initiatives of KMSdepend mainly on IT which enables and supports KM inseveral ways including knowledge sharing and collaborationin a virtual environment by team members accessing priorproject information and documenting knowledge sourcesthrough online directories and search databases [28 29]

Related studies in the literature (eg [29]) examine thecritical success factors (CSFs) following KMS adoption andimplementation and their significance to the system -estudy found organizational readiness to affect KMS adoptionor continued intention towards such adoption significantlyIn this regard potential adopters with high behavioraluncertainty need to ensure consistency between themselvesand the process of KMS-e two subgroups in Shrafatrsquos [29]study indicated that expected advantages had significantimpacts on the intention towards adoption or continued useof KMS-is is empirical evidence confirming that perceivedbenefits have a substantial role in adopting and diffusinginnovation-related activities -is also ensures that KMSadoption and continued use success boost experimentationand risk-taking In contrast organization-environmentalinteraction requirements can be established via dialogueinteraction and participative decision-making process -estudy findings supported the relationship between organi-zational readiness and KMS adoption or continued use andintention among potential adopters compared to currentones [29]

Decision-making (DM) is considered one of the topexecutive roles and available authentic knowledge sourcesplay a crucial role in DMP Knowledge sources may take theform of oral written or computer-based sources KMS iscreated to enable users to access knowledge that is essentialin achieving their activities on the job -e premise of usingcomputer-based systems to support DM has existedthroughout the years and the issue of how computer-basedsystems can be utilized to provide support to DM under theDSS nomenclature can be traced back to the later years of the1970s [30]

On the whole organizations have increased theircomplexity and stressed decentralized DM which tends tolead to using KMS with DSS to support decision-makingsuccess According to Turban et al [31] DSS covers aknowledge component that is useful for supporting DMPSuitable DSS integration with KMS will thus help the

2 Mathematical Problems in Engineering

interaction and develop new opportunities to enhance thequality of support provided by the system [32] Meanwhileother authors like Martinsons and Davison [33] are con-vinced that KMS and IS success in providing DMP support isdependent on the way IT applications are enhanced andadapted to match the usersrsquo decision styles -erefore KMSand IS global implementation should be flexible enough tosatisfy various decision styles and fit DMP [31 34 35]

In Bolloju et alrsquos [32] related study the authors men-tioned the advantages of DSS-KMS integration -ey in-cluded improving support quality in real-time adaptiveactive decision support supporting acquisition exploitationcreation gathering knowledge in organizations facilitationof patternstrends discovery in the gathered knowledge andsupporting the development means and tools in the orga-nizational memory

Along a similar line of study Turban et al [31] dem-onstrated that DSS employment could facilitate severaladvantages provision of support in all DMP phases andmanagerial levels (individuals groups and organization)improvement of DM effectiveness mitigation of the re-quirement for training enhancement of managementcontrol facilitation of communication saving effort of theuser ease of costs and enabling DM objectives

In addition to the above advantages DSS can also beutilized by management analysts and even intermediariesBals et al [36] emphasize technology as a tool that decision-makers and users can use to leverage their knowledge toachieve the work at hand Nevertheless most organizationsadministering KMS initiatives display various success levels-us the perception of decision-makers and users oftechnology and their interaction play a key role in KMS andDM initiativesrsquo success

However decision-making can be defined as evalu-ating assessing and developing human performance inan organization (HLI as an example) Performanceevaluation at educational institutions or organizationshas been the subject of several studies in the literature Asa result performance evaluation is critical in both re-search and instruction At educational institutions per-formance evaluations are commonly undertakenregularly Universities and research organizations fre-quently use the outcomes of evaluations in making de-cisions such as promoting lecturers or funding researchWithout reliable performance evaluation tools goodperformers may not receive enough positive feedback feelupset and depart resulting in high recruitment expensesfor the firm [37]

-e input data for a performance can be from multipleperiods Hence a dynamic decision-making procedure thatuses fuzzy logic is required [38] In such a method alter-native and importance weights of criteria are represented astriangular fuzzy integers in time sequence [39] Fuzziness istypical in challenges on decision-making as well as fuzzylogicrsquos advantages Fuzzy decision-making occurs whensingle or several criteria are used to discover the ideal option[40]

In this context Tong et al [41] presented a method forcomparison that is notably ambiguous Using the fuzzy

extent analysis method (F-EAM) the relative weight of eachparameter was measured

-e steps of the fuzzy extended analysis included in theirstudy are as follows

Let Z Z1 Z2 Zn be an object setlet V V1 V2 Vn be an object set

Following this the value calculation results for each ithobject in each stage are obtained and shown in the followingform

Mji (1)

where i 1 2 n j 1 2 m

Step 1 Utilize values of fuzzy extended analysis syn-thesis to acquire priority weights

si 1113944m

i1a 1113944

m

i1b 1113944

m

i1cotimes

11113936

m11 a

1

1113936m11 b

1

1113936m11 c

1113896 (2)

Step 2 -e following is the expression for comparingdegrees of possibility by the degree of probability ofN2geN1V (N2geN1)

a

0 if b2 le b1

1 if b1 ge c1

a1 minus c1(b2 minus c2) minus (b1 minus c1)

otherwise

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(3)

Step 3 Assume that you want to obtain the weightvector dprime(Bi)min V (TigeTk) for k 1 2 n kne ithen the weight vector is defined as follows Wprime (dprime(B1) dprime (B2) dprime (Bn))T where Bi (i 1 2 n) are nelementsStep 4 Calculate the vector of normalized weightsW (d(B1) d (B2) d (Bn))TStep 5 After determining the component weights thecomponents are ranked

3 Related Works in Knowledge ManagementSystem Adoption

Following the adoption of new technology and becoming atrend in organizations even individuals new to the tech-nology will adopt it for their survival and competitiveness Inother words adoption is a crucial premise when it comes totechnology According to Shih et al [42] adoption is atechnology diffusion step involving the inclination of theorganization and the individual to select and use thetechnology

In Arpacirsquos study [43] the primary objective was toexamine the antecedents and outcomes of cloud computingadoption in education to achieve knowledge management

Mathematical Problems in Engineering 3

-e study focused on implementing cloud computing in anactual learning environment to support KM practices andprovide training and education to participants -eir re-search focused on the causal relationship between the KMpractices expectations and the perceived usefulness of cloudcomputing services Based on the obtained results there is asignificant relationship between the perceived usefulnessand the creationdiscovery storage and sharing knowledgeexpectations Among them knowledge storage and sharingexpectations have stronger relationships with perceivedusefulness In addition innovativeness and training andeducation were significantly related to the promotion ofcloud computing adoption in education by enhancing KMpractices awareness

Furthermore Al-Rahmi et al [44] proposed a model formeasuring sustainability in the education sector and in-cluded big data adoption and knowledge managementsharing as variables Based on their findings behavioralintention to use big data supported big data adoptionsustainability in education and knowledge managementinfluenced the intention to use big data and educationalsustainability -eir study used UTAUT and knowledgemanagement sharing factors to examine behavioral inten-tion towards big data use and adoption for sustainableeducation-e study contributed to the literature on big dataadoption and knowledge management sharing for sustain-able education proposing combining knowledge manage-ment sharing and UTAUT model to obtain the overallresults

Along with the same study caliber Tsai and Hung [45]employed empirical methods to examine KMS adoptiondeterminants based on a national survey -ey found KMSadoption to be influenced by the organizationrsquos character-istics enablers of KM and attributes of KMS According tothem KMS adoption is rife with complexity as it is highlydependent on KM enablers and characteristics of the or-ganization instead of just system characteristics -eirfindings had several implications for theory and practicewith the conclusions supporting a majority of the proposedhypotheses Overall the KMS adoption determinants can beconsidered through the characteristics of the organizationthe KM enablers and the characteristics of KMS

In a related empirical study Shrafat [29] examined thedifferential impact of three contextual variables organiza-tional readiness expected benefits and organizationallearning capability on KMS adoption or decisions forcontinued use -e author gathered data from 220 seniorexecutives working in major Taiwanese firms and testedvarious relationships in the research model through PLSanalysis Based on the results organizational readiness ex-pected benefits and organizational learning capability sig-nificantly influence KMS adoption and intention towardscontinued use-eir study also supported the organizationalreadiness-KMS adoption or intention towards continued userelationship which was more significant for potentialadopters than for current ones In theory the study con-tributed a model that successfully explained the KMSadoption or inclination towards continued use determinantsin light of potential and current adopters Based on the

managerial viewpoint the findings obtained establishguidelines for companies willing to adopt KMS by over-coming possible barriers and leveraging the most benefits inthe preadoption and postadoption phases -e potential forKMS adoption has been focused on by SMEs but limitedstudies dedicated to the little topic information are known[5 29] Hence the present study contributes to explainingand clarifying the factors driving the adoption of KMSamong SMEs

In the same line of study Rohendi [26] revealed thatKMS enables the organization and documentation of theinstitutionrsquos knowledge -e study developed a prototype ofKMS to organize and document knowledge in universitiesand carry out document aggregation based on a totalnumber of subjects and writers -e prototype was devel-oped using SharePoint to collect store and publish digitaldata at the university to make them accessible online Ag-gregation is a process that uses the percentage of the numberof documents subjects and writers -e result of suchaggregation among the number of digital files was comparedto the number of courses and lecturers which equated tobelow 10 each -e university was recommended to boostlecturers to increase the gathering of digital files that couldindirectly enhance the quality of educational services -eauthor highlighted the need to examine and determinevarious factors to contribute to the enlightenment of thefield

In Nigeria Salami and Suhaimi [5] focused on the factorsrelating to KMS adoption among academicians using anexplanatory quantitative survey approach According to theobtained findings individual and management supportfactors have a crucial role in KMS adoption in Nigeriacompared to organizational and technological factors -estudy results can assist future studies in verifying and ex-ploring these factors particularly management support andindividual factors -e study focused on structure gov-ernment support culture and organizational infrastructurefrom the organizational factors -e individual factorsknowledge personal innovativeness experience and atti-tude were included and management support trainingmanagement initiatives and management were includedLastly their study focused on trialability compatibilityvisibility and complexity for the technological factors

In Libya Alhaj [46] investigated the effects of organi-zational factors on innovation among oil firms (public andprivate) while determining the role of social capital andknowledge sharing using the integrative and comprehensiveconceptual model -e focus was on the direct and indirecteffects of organizational factors on innovation using asample of 418 employees from the public and private oilsectors Data were analyzed using PLS-SEM and the authorrecommended that future authors add factors that couldhave a mediating role in the effect of organizational factorson innovation A longitudinal study could improve theinformation on indirect effects accuracy and evaluate itseffectiveness due to the long-term outcomes related to suchfactors

Concerning the above studies Haque et al [47] lookedinto the factors influencing knowledge management and


knowledge sharing and their potential benefits to the de-cision-making process and the overall performance -eirstudy primarily aimed to examine antecedents of academicsrsquoknowledge management and knowledge sharing intentionamong universities Further studies were recommended tovalidate and generalize the findings using a greater samplesize in the cross-national university contexts

Meanwhile Alshahranirsquos study [48] aimed to determinethe critical success factors (CSFs) for effective knowledgemanagement in universities using Nonakarsquos model andcomparing the Western Sydney University (WSU) inAustralia and King Fahd Security College (KFSC) -eauthors extended Nonaka et al [49] study to include CSFs inproper KM implementation Such extension provided asignificant practical and theoretical foundation for the ex-amination of KM among universities In addition the au-thors conducted a comparison of the two universitiesrsquoimplementation excluding other factors that may contributeto KM implementation success -e study found knowledgeproduction and distribution in universities of both countriesnot to be an explicit activity and one that is not limitedwithin one static framework in that it has a contextual anddynamic nature Added to the prior highlighted CSFs of KMother major factors were also proved to affect four knowl-edge conversion modes -ose elements involving severalrational cognitive and intuitive processes and practiceshave several characteristics and dynamics mutually facili-tating knowledge generation and distribution According tothe findings obtained effective KM practices and initiativesimplementation in both countries originate from thecomplexity of factors and behaviors linked to the knowledgeenvironment -ere were 14 internal and six external factorsthat substantially contributed to Nonakarsquos knowledge con-version model (ie socialization externalization combi-nation and internalization) to manage KM properly Hisstudyrsquos internal factors included leadership organizationalstructure organizational rules responsibilities of the em-ployees information technology infrastructure trainingteamwork and measurement

4 Methodology

-e study methodology comprises four stages (see Figure 1)which are conducting a thorough literature review andidentifying the critical factors consulting the expertsrsquo in-formation on the KMS factors and stressing the most sig-nificant of them which are used to develop the studyframework

In the method the researchers conducted a literaturereview It determined the critical variables to assess be-havioral intention towards KMS adoption among the HLIsin Libya following which the field experts reviewed thefactors -e following are the details of all the steps in themethodology

41 Factor Extraction through Literature Review In thispaper literature was analyzed using KMS adoption factorsfactors for technology adoption in education and KMS and

education decision-making A review of relevant studiesregarding KMS was conducted to determine the relevantfactors highlighted by the authors -e factors were thenclassified into dimensions and provided to the experts forperusal and review

-e authors selected the libraries and the main keywordsrelated to KMS adoption so that the searched words andterms remained in the research range -e keywords in-cluded KMS adoption KMS factors KMS frameworks KMSadoption decision-making and KMS education -esources provided information based on the keywords typedin and thus the information was utilized to develop apathway for developing and validating the keywordsthemselves Different publishers were included in this stage

Moreover KMS-dedicated studies that were reviewed todetermine the general factors used by the authors unearthed65 factors Table 1 lists the highlighted factors from whichthe determination of the top mentioned factors in literaturecan be discerned

Frequency refers to the number of citations for each ofthe extracted factors mentioned in the previous works ofliterature and it does not reflect the typical and commoncharacteristics of factors [51]

-ough a total of 65 factors were identified the study waslimited to the top-cited factors (24 factors) concerning KMSand technology adoption specifically in the educationalfield Table 2 shows the most cited factors that were extractedfrom the literature review

Only 24 factors out of the 65 extracted factors were themost cited ones -e rest of the factors were only cited a fewtimes in literature and therefore were not included in thefinal list of frequencies-e present study defined KMS fromtechnical and nontechnical perspectives It adopted a cate-gorization type that has its basis on TOE theory whichcovers technological organizational and environmentalfactors

42 Experts Consultation and Factors Ranking As the list of24 most cited factors that affect KMS adoption was for-warded to the experts (lecturers who use KMS and are fa-miliar with it) interviews were conducted with them to gaintheir perception of KMS of education Along with the in-terviews the experts also answered different questions in aquestionnaire regarding the items of each factor A total of10 factors were identified to be the top essential factorsregarding behavioral intention towards KMS use andeventually its actual use Recommendations provided by

Conduct an intensive literature review and

extract the factors Experts rank

Conceptual framework construction

Figure 1 -e methodology of the study as adopted from Mukredet al [50]


Hawking and Sellitto [52] and Ahmad and Pinedo Cuenca[53] were followed when determining the significant factorsTen experts who work in higher educational institutions andare familiar with KMS technology adoption were consultedfor their knowledge -e experts are PhD holders workingin different affiliations in Libya Yemen Malaysia and SaudiArabia -e expertsrsquo profile is listed in Table 3

-e experts highlighted 12 of the top factors that mightinfluence the behavioral intention towards adopting and

using KMS -e experts dissected the factors based on se-lection criteria and interviews -e aim was to assess thefactors that influence KMS adoption

Based on the literature 24 factors were confirmed but 12factors were dropped as the experts had mixed feedbackabout them following further validation -e list of factorsranked by the experts is provided in Table 4

Table 5 shows the final list of factors that were extractedbased on the interviewees -e table also shows the source ofeach factor with the overall percentage after the analysis

-e calculation of the percentage and validity belongingto all questions is done by the following the equationsuggested by Mukred et al [70]

VTotal 111394410

i1vilowast i (4)

where i is the rank given from 1 to 10 and vi is the number ofexperts for each rank value

Table 1 Extracted factors from the literature review

Dimension Factors No offactors

Individual Attitude gender education age experience training subjective norm self-efficacy satisfactionmotivation personal normative belief 12

TechnologicalReliability perceived performance expectancy service quality perceived effort expectancy featuresused system quality perceived ease of use IT infrastructure perceived usefulness self-identity trust

compatibility privacy efficiency interactivity information quality usability efficiency18

OrganizationalTraining motivation policy social influence perceived financial support change management

information need competition top management support facilitating conditions effectivecommunication organization readiness standardization outsourcing

14

Environmental Clear vision and planning big data analytics laws and legislations cloud computing policycompetitiveness pressure security concerns safety 8

Behavioralintention Intention to use intention to adopt habit user expectations extrinsic motivation 5

Use User satisfaction decision-making organizational competency user involvement perceived benefitsoverall satisfaction performance output quality 8

Total 65

Table 2 Frequency of the extracted factors

No Factor Total1 Top management support 332 Big data 253 Perceived usefulness 304 Competitive pressure 285 Effective communication 286 Clear vision and planning 277 Training 278 Gender 259 Change management 2510 User involvement 2511 Government role 2412 Cloud computing 2413 Social influence 2314 Perceived effort expectancy 2315 Usability 2316 System quality 2017 Policy 1918 Service quality 1719 Perceived performance expectancy 1720 Financial support 1721 Information quality 1622 Intention to adopt 1523 Teamwork and composition 1424 Decision-making 13

Table 3 Expertsrsquo profiles

Gender Specialist areas Years ofexperience

E01 Male Information science 8E02 Male Technology adoption and education 12E03 Female Technology adoption and education 8E04 Male Technology adoption and education 7E05 Male Technology adoption and education 10E06 Male Computer science 10E07 Male Information science 9E08 Male Technology adoption and engineering 11E09 Male Technology adoption and engineering 14E10 Male Technology adoption and engineering 9


Table 4 -e experts ranking for the factors and items

Factor No Questions 1 2 3 4 5 Rank

Perceived effort expectancy

How easy is KMS to use1 KMS is easy to use 5 2 3 762 KMS can be used without referring to a user manual 1 3 2 4 783 KMS is flexible to interact with 4 2 4 804 It is easy to get information using KMS to do what I want to do 1 3 2 4 785 It is easy to detect and correct errors in student records using KMS 1 1 4 4 82

Perceived performanceexpectancy

How useful is KMS1 KMS enhances my work effectiveness 3 2 5 842 KMS increases my productivity in my work 3 1 6 863 KMS enables me to accomplish tasks more quickly 3 1 6 864 KMS makes my work easier 3 2 5 845 KMS gives me greater control over my work 3 1 6 86

IT infrastructure

IT infrastructure for adopting KMS1 It provides remote users with seamless access to centralized data 2 3 5 86

2 It captures data that is made available to everyone in our organization in real-time 2 3 5 86

3 It can easily incorporate software applications and can be used across multipleplatforms 3 3 4 82

4 It provides interfaces that give transparent access to all platforms andapplications 3 3 4 82

5 It offers multiple interfaces or entry points to external users 3 3 4 82

Training

Training on KMS1 It should be developed to meet the requirements of users 2 2 6 882 It should have customized materials for each specific job 2 3 5 863 It should have materials for the entire business task of the system 2 2 6 88

4 It should be tracked to ensure that employees have received the appropriatetraining 2 1 7 90

5 It should be adequate for all involved staff 3 1 6 86

Financial support

Financial support for adopting KMS is important for1 Purchasing a system 1 4 5 882 Incentive payments 1 4 5 883 Securing infrastructure and equipment 1 3 6 904 Technical assistance cost 1 4 5 885 Maintenance cost 1 4 5 88

Organizationrsquos readiness

How ready is your organization to adopt KMS

1 If we have the system to engage in the knowledge management we will nothesitate 1 1 1 7 88

2 We feel comfortable (regarding security privacy etc) thus we will adopt it 1 1 3 5 843 We are willing to adopt the KMS completely 1 1 1 7 884 We consider it essential to engage in the system 1 1 1 7 88

5 We consider it essential to improve coordination and collaboration regardingthe use of knowledge 1 1 3 5 84

Change management

Change management in KMS adoption1 It ensures that employees understand how their work fits into the system 1 2 2 5 822 It receives input from employees about how their jobs will change 1 1 3 5 843 It actively works to alleviate employee concerns 1 2 1 6 844 It makes available a support group to answer concerns about job changes 1 1 2 6 865 -e roles of all employees are communicated 1 1 2 6 86

Competitiveness pressure

With KMS adoption1 My job frequently requires me to rely on the KMS 1 1 3 5 822 My everyday work tasks require me to need the support of the KMS frequently 1 2 2 5 803 I frequently have to use the KMS to meet my work obligations 1 2 3 4 784 I am expected to use the KMS all the time to meet my work obligations 1 2 2 5 805 KMS is vital to ensure competitiveness 1 1 1 6 76


43 Framework Development An essential aspect in con-ducting any study is examining and determining the the-oriesmodels underpinning the study topic so that they canbe used for guidance in developing a premise of the con-structsrsquo relationships during framework development [3] Ina study of KMS adoption the level of adoption can beenhanced if the determinants of such adoption are deter-mined and examined Prior literature on the topic has thusproposed several theories and models [71ndash73] to examinethe technology adoption in institutions -e major theoriesused and reviewed included the Technology AcceptanceModel (TAM) Unified -eory of Acceptance and Use ofTechnology (UTAUT) -eory of Planned Behavior (TPB)Diffusion of Innovation (DOI) theory and Technology-Organization-Environment (TOE) framework

In this study the KMS framework is developed andproposed by identifying five interrelated variables (tech-nological dimensions organizational dimensions environ-mental dimensions KMS adoption intention andeducational institutionsrsquo decision-making see Figure 2) -evariables are examined and categorized under technologyadoption factors in the study framework

-is study reviews the unified theories and models tochoose the most appropriate to achieve the studyrsquos objec-tives Top extensively used models in literature in educationincluded TAM TOE UTAUT and DOI as Alharbi [71] andAl-Jabri [72] mentioned

Accordingly UTAUT was validated in the reviewedliterature as a robust model UTAUTwas selected because of

its use suitability validity and reliability in examiningtechnology adoption in different contexts [74ndash77] -epresent study used UTAUT to examine the factors thatinfluence KMS adoption consistent with the suggestion byAbdullah et al in the case of Libyan HLIs -us the mainUTAUT features include technological differences charac-teristics of the organization and environmental set-tingsmdashthese are all viewed as determinants of KMS adoptionbehavior in HLIs in Libya UTAUT is suitable for the un-derpinning theory of the present study in light of its ob-jectives and context

5 Discussion and Interpretation

-e interviewed experts agreed that perceived effort ex-pectancy and perceived performance expectancy are sig-nificant factors that influence KMS adoption Regarding theusers the majority of them are inclined to use the system ifthey are convinced that it can enhance their work quality andis easy to use Other factors such as financial support andtraining were also included in the top-listed factors Fur-thermore three experts (E2 E7 and E8) perceived that bigdata facility and cloud computing ability could potentiallyinfluence KMS adoption In contrast others proposed fi-nancial support for such adoption in the HLI sector of Libya

In addition experts E1 and E6 suggested that the en-vironmental dimension factors may also be considered newfactors to be included in the conceptual framework based onwhich successful and timely adoption can occur Experts E3

Table 4 Continued


Big data analytics

-e use of big data should have1 Ability to save huge volumes of information 2 3 5 862 Ability to handle real-time data processing 2 3 5 863 Data integration 2 2 6 884 Rapid and interactive analysis 2 3 5 865 Flexibility to consolidate data from various sources into one single place 2 2 6 88

Cloud integration

-e cloud feature of KMS1 It provides a high degree of interconnectivity 1 4 5 882 It is sufficiently flexible to incorporate electronic connections to external parties 2 3 5 863 It is a factor that determines whether or not to choose KMS 1 3 2 4 78


5 It provides remote users with seamless access to centralized data 3 3 4 82

Intention to adopt kms

My intention regarding KMS adoption is1 Assuming I have the KMS I intend to adopt it 2 3 5 862 Given that I have the KMS I predict that I would adopt it 2 2 6 883 In my work if I have KMS I want to use it as much as possible 2 3 5 864 I prefer to use electronic records even though I can do my work with other tools 2 3 5 865 KMS is essential to my work and I need to adopt it 2 2 6 88

Decision-making

KMS gives decisions that provide the following1 Quality 2 1 7 902 Effectiveness 2 1 7 903 Accuracy 2 1 7 904 Performance 2 1 7 905 Transparency 2 1 7 906 Integrity 2 1 7 907 Accountability 2 1 7 90


E4 and E5 also agreed that competitiveness pressure is oneof the top influencing factors of KMS adoption in the HLIs ofLibya to get expected exceptional outcomes-us this factorwas included in the present study -e experts agreed on theimportance of perceived effort expectancy perceived per-formance expectancy and IT infrastructure as essentialdeterminants of KMS adoption -us they were included inthe study framework Moreover E9 and E10 stressed theimportance of testing the influence of the identified factorson behavioral intention towards adopting KMS as the role ofsystem adoption in improving decision-making has yet to beconfirmed

-e proposed studyrsquos conceptual framework is displayedin Figure 2 -e framework was developed using tenidentified factors validated and ranked by experts in the fieldand the factors are arranged based on underlying theories

-e proposed framework was examined in light of theinfluence of the factors on KMS adoption and the factorsinclude those adopted from the UTAUT framework (per-ceived effort expectancy and perceived performance ex-pectancy) which directly determine behavioral intention toadopt KMS Other factors include IT infrastructure trainingfinancial support organization readiness change manage-ment cloud computing and big data analytics

-e propositions and description of each factor includedin the proposed conceptual framework are detailed in thefollowing sections

51 Technological Variables In any sector technology useprovides the potential for enhancing service quality providedand the workforce efficiency and effectiveness and mini-mizing the organizationrsquos costs -us technology adoptionis essential in institutions as it has been evidenced andhighlighted as a critical issue [78] Although several studiesin the literature revealed that technology adoption positivelyinfluences organizations empirical works presented barriersand challenges to technology adoption in educational in-stitutions -erefore it is pertinent to examine factors thatinfluence technology adoption for successful technologyimplementation and use [69 79]

In this study the technology dimension factor refers tothe level to which the user believes that using a specificsystem would enhance hisher job performance [54]

In the line of this study Ahmed and Ward [80] adoptedUTAUT in their measurement of KMS acceptance amongacademic and professional development departmentrsquos em-ployees Based on their findings perceived performanceexpectancy has a significant effect on the intention of theusers Meanwhile performance expectancy and effort ex-pectancy are the two main predictors of behavioral intentiontowards IS adoption as evidenced by Venkatesh et al [81]

In this study perceived performance expectancy is re-ferred to as the perception of managers and employees of theusefulness of KMS -is variable has been examined in lightof the systemrsquos ability to enhance productivity effectivenessand performance at work Empirical findings also showedthat perceived effort expectancy is a determinant of intentiontowards system use and adoption [82 83]

In a similar line of study Tarcan et al [84] concentratedon the factors that affect intention to use IT among aca-demicians -ey found effort expectancy to be one of the topfactors Elkaseh et al [85] also found that intention towardsIT use and adoption among users is affected by the usersrsquoperceptions and beliefs including effort expectancy andperformance expectancy -ese are the two significant ISadoption antecedents [74]

IT infrastructure which is another factor in the tech-nological dimension is significant [2 69] It includes the ITplans business aims ITarchitecture and ITworkforce skillsconsistency In this regard Broadbent and Weill [86]revealed that the capabilities of IT infrastructure enablevarious applications to reinforce the present and potentialorganizationrsquos objectives and its competitive status in thebusiness market

Based on the above definition and discussion of IT in-frastructure it is clear that there are two components of thevariable technical IT infrastructure and human IT infra-structure -e first one is made up of data technology andapplication -e second one is made up of knowledge andcapabilities for IT resources management [86]

KMS has been studied in several empirical works[5 26 27 29] each with its objectives and conclusions butthe general trend among the studies is that technologicalfactors of perceived performance expectancy perceivedeffort expectancy and IT infrastructure have the potential toinfluence KMS adoption Based on the above discussion andthe importance of the factors in boosting KMS adoption thisstudy proposes the following proposition for testing

(P1) Technological factors have a positive influence onthe intention to adopt KMS in HLIs in Libya

52 Organizational Variables Generally speaking the suc-cessful adoption of KMS depends on the engagement of thewhole organization -erefore senior management needs topromote new records management system as part of thechange management initiative In addition organizationalimplementation methods of further KMS vary but the focusshould not be on ITalone According to Binyamin et al [87]

Table 5 List of factors recommended by experts

No Factor Percentage

1 Perceived effort expectancy [54ndash56] 79002 Perceived performance expectancy [54ndash56] 8223 IT infrastructure [57ndash59] 8364 Training [60] 8765 Financial support [61 62] 8846 Organization readiness [63] 8647 Change management [2 64] 8448 Competitive pressure [65ndash67] 7929 Big data analytics [59] 86810 Cloud integration [68] 832

11 Behavioral intention (intention toadopt) [54ndash56] 868

12 Decision-making [69] 9000


organizational factors are as significant as their technologicalcounterparts when it comes to adopting technology in theinstitutions of higher learning -e authors found that or-ganizational support plays a crucial role in successful ISadoption and use

In this regard [3] adopted a mixed explanatory approachto continuously explore the experienced education staff whomanaged to transition beyond adopting the technology stagein their practices Based on this study some factors preventthe adoption of technology in the form of challenges in-cluding learning to use a computer Technology optimalusage could be enabled by assessing and enhancing the userrsquoscomputer skills working towards data entry and system useconsistently via training [88]

Staff training ensures that risks that crop up are over-come Otherwise such risks could prevent the successfulKMS implementation and adoption [89] Moreover the lackor absence of training and support could cause a barrier tosystem adoption among users [90] Insufficient trainingcould also lead to discomfort at dealing with system andcomputer and eventually it may lead to implementationfailure [89 90]

Another organizational factor that has a crucial role intechnology adoption is financial support [91] Technologyadoption has become increasingly dependent on financialsupport and therefore financial aid has a positive effect onthe successful adoption of technology towards enhancingfuture efforts in information [92] -us in the present study

financial support is examined in terms of its influence onKMS adoption in educational institutions

Readiness is another crucial factor for KMS adoption Itrefers to the level of inclination of a country to be a part ofthe networked global village by evaluating its developmentin different aspects of ICT adoption [50] Readiness is de-scribed as the capacity to meet the organizationrsquos requiredinstitutional legal framework and ICT infrastructure Ad-ditionally according to Griffiths et al [93] readiness is oneof the factors with which progress is measured in contrast tothe overall ability of organizations to adopt or use thesystems It is therefore a vital driver for assessing the be-havioral intention to adopt the KMS among HLIs

On top of that researchers commonly acknowledgedchange management as a necessary factor and in the case ofKMS application the organization is faced with severalchanges In this situation change management is a methodstrategy adopted for the proper management of the tran-sition from traditional frameworks to newer ones -us inusing the KMS aspect the organization and the employeesneed to be ready for any eventual change that needs tohappen-is is particularly true when it comes to the need ofthe organization to develop such management as early aspossible to tackle issues (eg employee resistance redun-dancies and confusion and the errors that crop up duringthe implementation of the framework [79 94])

In the same line of argument administrators may be thebasis of change management initiatives but not IT initiatives


Perceived performance expectancy

Technological factors

Training

Financial support

Organizational readiness

Organizational factors

Environmental factors

Adoption of KMS

Behavioural intention to use

Decision-making

Use

P1

P2

P3

P4

IT infrastructure

Competitive pressure Big data analytics Cloud computing

Change management

Figure 2 -e proposed framework for KMS adoption in HLIs


[95] In addition in cases where individuals are work in-volved they acquire a more extensive view of the advantagesleading to more acceptance of the novel technologyframework [2]

As a whole factors related to the organization were themost often cited reasons for the limited KMS usage -esefactors include organization readiness financial supporttraining and change management-us this study proposesthe following proposition

(P2) Organizational factors have a positive influence onthe adoption of KMS in HLIs in Libya

53 Environmental Factors Prior literature dedicated toKMS adoption mainly studied organizational and techno-logical factors and human and individual factors [5] In thefield of education KMS adoption should also focus on theenvironmental dimension-us the present study considerssuch dimensions and factors competitive pressure big dataanalytics and cloud computing

To this end competitive pressure is a significant factorunder the environmental dimension at the local and globallevels -is pressure forces the organization to search forways to enhance its efficiency and effectiveness throughtechnology adoption [96] Both dynamic competition anddigital technology advancement have left governmentsworldwide wide open to leveraging new methods for de-velopmental progress Awareness of such technologyadoption has resulted in the transition of government ser-vices from outdated approaches to e-methods in the currentdecade [97]

Other environmental factors that influence KMSadoption are cloud computing and big data analytics asmentioned and illustrated by Mohamad et al [98] Med-vedeva et al [99] and Dening et al [100] Prior studies alsoindicated that the used software and hardware whenimplementing KMS are among the factors -is issue hasmore to do with developing KMS software with a user in-terface that could be customized for cloud computing ca-pability Cloud computing is an alternative solution to helpkeep data and help HLIs use KMS at any time In otherwords an effective KMS system should be compatible withany platform and database andmaintenance-friendly-is isbecause an inefficient hanging system could minimize usersas they refuse to waste their time and effort to achieve theirgoals As a result it is critical to select an appropriate andefficient technology compatible with the application andhardware to facilitate the institutionrsquos implementation[101 102]

In sum environmental factors are crucial for successfulKMS adoption [103] Based on the above discussion thisstudy proposes the following proposition for testing

P3 Environmental factors have a positive influence onthe adoption of KMS in HLIs in Libya

54 Intention to Adopt KMS Factors Behavioral intentionindicates the readiness of the individual to perform a specificbehavior and it is proposed to be an antecedent of behavior[104] In the present study intention is defined as the

willingness of the individual to try or the effort they arewilling to exert to perform a future behavior

According to Venkatesh et al [81] behavioral intentiontowards technology is the primary determinant of actualbehavior -e three factors predicting intention to use areattitude subjective norms and perceived behavioral control

In the same study Ahmed and Ward [105] comparedcompeting technology acceptance frameworks to examinepersonal academic and professional portfolio acceptancebehavior -e authors revealed a positive direct effect ofperceived ease of use on perceived usefulness Furthermoreperceived ease of use was found to have an immediatepositive impact on intention

Overall the identification of content and context di-mensions offers a suitable method to shed light on thecurrent adoption state of KMS in educational institutionsand the barriers that prevent such adoption [43 106]

-erefore there is a need to examine intention to adoptbased on technological organizational and environmentalfactors in KMS adoption -is study thus proposes thefollowing proposition for testing

P4 Behavioral intention to adopt KMS has a significantrelationship with the decision-making process in HLIs inLibya

6 Conclusion

In the present work the lack of studies dedicated to ex-amining KMS adoption and its key role in supporting andenhancing the performance of educational institutions byimproving the decision-making process is highlighted -estudy also highlighted the limitations of the existing studieswhen it comes to such examination and thus it developedand proposed a conceptual framework KMS adoption inHLIs requires a robust framework Accordingly the presentwork reviewed the literature concerning KMS use adoptionand implementation and the factors included in the studyframework -e study conducted a thorough review of KMSfactorsrsquo literature extracted them and forwarded them toexperts for validation -e factors were categorized intothree dimensions technological organizational and envi-ronmental dimensions -e panel of experts perused thefactors and recognized the significance of KMS initiatives ineducational institutions Based on the highlighted factorsthe study developed a conceptual framework that is ap-propriate to examine the factors influencing the adoption ofKMS in educational institutions However the frameworkwas based on the reviewed literature which had its limi-tations According to the confirmation of experts ten factorswere found to influence the adoption of KMS in the LibyanHLIs with two adopted from UTAUT and eight adoptedfrom a literature review -e examined factors includedperceived effort expectancy perceived performance expec-tancy IT infrastructure training financial support orga-nization readiness change management competitivenesspressure cloud computing and big data analyticsmdashall thesefactors were tested for their significant influence on KMSadoption in HLIs in Libya -e study revealed the role ofKMS in enhancing the decision-making process-e present


study contributes to the literature by identifying the factorsinfluencing behavioral intention to adopt and use KMSMoreover it contributes to practice by directing limitedmanagement resources to the significant areas that wouldmake successful and smooth system adoption

Data Availability

-equalitative data used to support the findings of this studyare included within the article

Conflicts of Interest

-e authors declare that they have no conflicts of interest

References

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[2] M Mukred Z M Yusof and F M Alotaibi ldquoEnsuring theproductivity of higher learning institutions through elec-tronic records management system (ERMS)rdquo IEEE Accessvol 7 pp 97343ndash97364 2019

[3] M Mukred Framework for Electronic Records ManagementSystem Adoption in the Higher Professional Education inYemen Universiti Kebangsaan Malaysia (UKM) BangiMalaysia 2017

[4] C Hassall and D I Lewis ldquoInstitutional and technologicalbarriers to the use of open educational resources (OERs) inphysiology and medical educationrdquo Advances in PhysiologyEducation vol 41 no 1 pp 77ndash81 2016

[5] A Salami and M A Suhaimi ldquo-e adoption of knowledgemanagement systems (KMS) among academicians in Nigeriauniversitiesrdquo Journal of Information Systems and DigitalTechnologies vol 1 no 1 pp 47ndash64 2019

[6] J Chuan-Chuan Lin and H Lu ldquoTowards an understandingof the behavioural intention to use a web siterdquo InternationalJournal of Information Management vol 20 no 3pp 197ndash208 2000

[7] S Ali N Ullah M F Abrar Y Zhongguo and J HuangldquoFuzzy multicriteria decision-making approach for mea-suring the possibility of cloud adoption for software testingrdquoScientific Programming vol 2020 Article ID 659731624 pages 2020

[8] J R Baron and A -urston ldquoWhat lessons can be learnedfrom the US archivistrsquos digital mandate for 2019 and is therepotential for applying them in lower resource countriesrdquoRecords Management Journal vol 26 no 2 2016

[9] A Adade A Y Quashigah and P Eshun ldquoAcademic recordsmanagement in Ghanaian basic schools a study of basicschools in the Ashiedu Keteke sub-metro in the greater Accraregionrdquo British Journal of Education vol 6 no 4 pp 33ndash492018

[10] N Garrett ldquoAn e-portfolio design supporting ownershipsocial learning and ease of userdquo Journal of EducationalTechnology amp Society vol 14 no 1 p 187 2011

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14th International Conference of Electronic Business TaipeiTaiwan 2014

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[83] O Mosweu K J Bwalya and A Mutshewa ldquoA probe intothe factors for adoption and usage of electronic documentand records management systems in the Botswana contextrdquoInformation Development vol 33 no 1 pp 97ndash110 2017

[84] E Tarcan E S Varol and B Toker ldquoA study on the ac-ceptance of information technologies from the perspectivesof the academicians in Turkeyrdquo Ege Akademik Bakis (EgeAcademic Review) vol 10 no 3 pp 791ndash812 2010

[85] A M Elkaseh K W Wong and C C Fung ldquoPerceived easeof use and perceived usefulness of social media for E-learningin Libyan higher education a structural equation modelinganalysisrdquo International Journal of Information and Educa-tion Technology vol 6 no 3 pp 192ndash199 2016

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study of King Abdulaziz Universityrdquo in Proceedings of the2017 International Conference on E-Learning Orlando FLUSA 2017

[88] S Babaee ldquoE-portfolio as a higher training professional toola comparative-descriptive studyrdquo American Journal of Hu-manities and Social Sciences Research vol 4 no 2pp 225ndash233 2020

[89] A Carl and S Strydom ldquoE-portfolio as reflection tool duringteaching practice the interplay between contextual anddispositional variablesrdquo South African Journal of Educationvol 37 no 1 2017

[90] M Abuzaid ldquoPerceptions of E-portfolio use in lifelonglearning and professional development among radiologyprofessionalsrdquo Current Medical Imaging Reviews vol 13no 4 pp 495ndash501 2017

[91] T Hasani J Bojei and A Dehghantanha ldquoInvestigating theantecedents to the adoption of SCRM technologies by start-up companiesrdquo Telematics and Informatics vol 34 no 5pp 655ndash675 2017

[92] C Kim ldquoEvaluating effects of mobile CRM on employeesrsquoperformancerdquo Industrial Management amp Data Systemsvol 115 2015

[93] S Griffiths L Voss and F Rohrbein ldquoIndustry-academiacollaborations in robotics comparing Asia Europe andnorth-Americardquo in Proceedings of the 2014 IEEE Interna-tional Conference on Robotics and Automation (ICRA) HongKong China 2014

[94] F F-H Nah and S Delgado ldquoCritical success factors forenterprise resource planning implementation and upgraderdquoJournal of Computer Information Systems vol 46 no 5pp 99ndash113 2006

[95] S M Arachchi S C Chong and A Lakshanthi ldquoLiteraturebased review-risks in ERP systems including Asian coun-triesrdquo European Journal of Computer Science and Informa-tion Technology vol 3 no 1 pp 1ndash14 2015

[96] A Azadeh M A Mofrad and M Khalojini ldquo-e role oforganisational infrastructure in successful ERP imple-mentation an empirical study by hierarchical regression andPCArdquo International Journal of Business Information Systemsvol 10 no 1 pp 40ndash67 2012

[97] C Cartman and A Salazar ldquo-e influence of organisationalsize internal IT capabilities and competitive and vendorpressures on ERP adoption in SMEsrdquo International Journalof Enterprise Information Systems vol 7 no 3 pp 68ndash922011

[98] S N A Mohamad M A Embi and N M Nordin ldquoDe-signing an E-portfolio as a storage workspace and showcasefor social sciences and humanities in higher education in-stitutions (HEIs)rdquoAsian Social Science vol 12 no 5 p 20162016

[99] I Medvedeva O Martynyuk S Panrsquokova and I SolovyovaldquoOn the formation of studentrsquos E-portfoliordquo in Proceedings ofthe 11th International Scientific and Practical Conferencevol 2 Rezekne Latvia 2017

[100] K H Dening D Holmes and A Pepper ldquoImplementationof e-portfolios for the professional development of admiralnursesrdquo Nursing Standard vol 32 no 22 p 46 2018

[101] M S Aksoy and D Algawiaz ldquoKnowledge management inthe cloud benefits and risksrdquo International Journal ofComputer Applications Technology and Research vol 3no 11 pp 718ndash720 2014

[102] H Rezaei B Karimi and S J Hosseini ldquoEffect of cloudcomputing systems in terms of service quality of knowledge

management systemsrdquo Lecture Notes on Software Engi-neering vol 4 no 1 pp 73ndash76 2016

[103] T Shea and S Parayitam ldquoAntecedents of graduate studentsatisfaction through e-portfolio content analysisrdquoEducation +Training vol 61 2019

[104] I Ajzen ldquo-e theory of planned behaviorrdquo OrganizationalBehavior and Human Decision Processes vol 50 no 2pp 179ndash211 1991

[105] E Ahmed and R Ward ldquoA comparison of competingtechnology acceptance models to explore personal academicand professional portfolio acceptance behaviourrdquo Journal ofComputers in Education vol 3 no 2 pp 169ndash191 2016

[106] M N Yakubu S I Dasuki A M Abubakar andM M O Kah ldquoDeterminants of learning managementsystems adoption in Nigeria a hybrid SEM and artificialneural network approachrdquo Education and InformationTechnologies vol 25 pp 3515ndash3539 2020


such notion has resulted in heightened awareness and in-vestment in KMS innovation in the majority of nations toenhance their system of education [8 9] Additionally KMSadoption that constitutes the education provision has beenconsidered a set of processes to be implemented to enhancethe effectiveness of HLI in terms of its performance andobjectives achievement In literature several barriers to KMSadoption have been evidenced by studies in the context ofdeveloping countries including Garrett [10] Shroff et al[11] and Alharthi [12] in the Libyan context

Such studies revealed that the adoption of technologyand systems specifically KMS is still at the initial stages[9 12ndash14] Most studies of this caliber have stressed threemain barrier categories namely human-related organiza-tion-related and technology-related barriers (eg [15 16])Heeks [17] reported that information systems combinedwith technical social organizational and environmentalfactors have been successful although evidence backed bytheory regarding adopting KMS at the individual and en-vironmental level is still scarce

In Arab nations Gholam and Kobeissi [18] reported theabsence of technology implementation for evaluation tosupport professional development In this regard Alfahadiet al [19] and Alharthi [12] presented a critical look at theimplemented evaluation process in Libya that lacks tools andtechniques leading to an ambiguous view of the studentsrsquoperformance Evaluation procedures in Libyan institutionsneed reformation for validation realism and authenticimplementation and use [20]

-e stress of the above discussion is the requirement ofexamining innovation and technology adoption to allowhigher education institutions competitiveness and ability todevelop into global leaders in the educational platform-us a clearer picture of such adoption is called to extendand promote learning innovations adoption and usage [21]

Moreover KMS use and adoption in educational in-stitutions for their improvement are part of the advancementof technology Research studies of this caliber have high-lighted KMSs as a crucial tool in assessing the process ofevaluation (eg Bartlett [22])

-is manuscript is structured to include KMS and thedecision-making process in Section 2 after the introductionSection 3 presents related works on KMS adoption andSection 4 provides the methodology Discussion and in-terpretations of the finding are presented in Section 5 andSection 6 is dedicated for the conclusion

2 Knowledge Management Systems andDecision-Making Process

Information system (IS) refers to integrating a group ofcomponents used to gather store and process data todistribute the information and knowledge obtained [23] Itrefers to a combination of hardware software and tele-communication networks built to collect create and dis-tribute required data generally in organizations On theother hand KMS consists of a class of information systemsemployed to manage the organizationrsquos knowledge [24]KMS is a category of IS used in organizations to manage

knowledge with the help of IT-based systems created toprovide support and improvement to the processes in-volving the creation storage retrieval transfer conversionprotection and application of knowledge [25 26]

KMS refers to an IT-based system developed to providesupport and enhancement to the processes of organizationsrelating to the creation storage transfer and application ofknowledge [24] It was similarly described by Alatawi et al[27] as a system created and designed to provide theknowledge needed for decision-making and tasks under-taking among decision-makers and users [27] As Alavi andLeidner [24] definition corresponds to the present studyrsquosobjective of examining the adoption of KMS in Libyanuniversities it best describes the university practices set-tings and processes when it comes to KMS adoption-erefore their definition is adopted Initiatives of KMSdepend mainly on IT which enables and supports KM inseveral ways including knowledge sharing and collaborationin a virtual environment by team members accessing priorproject information and documenting knowledge sourcesthrough online directories and search databases [28 29]

Related studies in the literature (eg [29]) examine thecritical success factors (CSFs) following KMS adoption andimplementation and their significance to the system -estudy found organizational readiness to affect KMS adoptionor continued intention towards such adoption significantlyIn this regard potential adopters with high behavioraluncertainty need to ensure consistency between themselvesand the process of KMS-e two subgroups in Shrafatrsquos [29]study indicated that expected advantages had significantimpacts on the intention towards adoption or continued useof KMS-is is empirical evidence confirming that perceivedbenefits have a substantial role in adopting and diffusinginnovation-related activities -is also ensures that KMSadoption and continued use success boost experimentationand risk-taking In contrast organization-environmentalinteraction requirements can be established via dialogueinteraction and participative decision-making process -estudy findings supported the relationship between organi-zational readiness and KMS adoption or continued use andintention among potential adopters compared to currentones [29]

Decision-making (DM) is considered one of the topexecutive roles and available authentic knowledge sourcesplay a crucial role in DMP Knowledge sources may take theform of oral written or computer-based sources KMS iscreated to enable users to access knowledge that is essentialin achieving their activities on the job -e premise of usingcomputer-based systems to support DM has existedthroughout the years and the issue of how computer-basedsystems can be utilized to provide support to DM under theDSS nomenclature can be traced back to the later years of the1970s [30]

On the whole organizations have increased theircomplexity and stressed decentralized DM which tends tolead to using KMS with DSS to support decision-makingsuccess According to Turban et al [31] DSS covers aknowledge component that is useful for supporting DMPSuitable DSS integration with KMS will thus help the













Mji (1)



si 1113944m

i1a 1113944

m

i1b 1113944

m

i1cotimes

11113936

m11 a

1

1113936m11 b

1

1113936m11 c

1113896 (2)


a

0 if b2 le b1

1 if b1 ge c1


otherwise

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(3)


















4 Methodology






















VTotal 111394410

i1vilowast i (4)









14




Total 65













IT infrastructure






Training




Financial support







Change management













Table 4 Continued


Big data analytics


Cloud integration






Decision-making


































Training

Financial support




Adoption of KMS


Decision-making

Use

P1

P2

P3

P4

IT infrastructure


Change management


















6 Conclusion




Data Availability




References






























































































































Mji (1)



si 1113944m

i1a 1113944

m

i1b 1113944

m

i1cotimes

11113936

m11 a

1

1113936m11 b

1

1113936m11 c

1113896 (2)


a

0 if b2 le b1

1 if b1 ge c1


otherwise

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(3)


















4 Methodology






















VTotal 111394410

i1vilowast i (4)









14




Total 65













IT infrastructure






Training




Financial support







Change management













Table 4 Continued


Big data analytics


Cloud integration






Decision-making


































Training

Financial support




Adoption of KMS


Decision-making

Use

P1

P2

P3

P4

IT infrastructure


Change management


















6 Conclusion




Data Availability




References































































































































4 Methodology






















VTotal 111394410

i1vilowast i (4)









14




Total 65













IT infrastructure






Training




Financial support







Change management













Table 4 Continued


Big data analytics


Cloud integration






Decision-making


































Training

Financial support




Adoption of KMS


Decision-making

Use

P1

P2

P3

P4

IT infrastructure


Change management


















6 Conclusion




Data Availability




References

























































































































VTotal 111394410

i1vilowast i (4)









14




Total 65













IT infrastructure






Training




Financial support







Change management













Table 4 Continued


Big data analytics


Cloud integration






Decision-making


































Training

Financial support




Adoption of KMS


Decision-making

Use

P1

P2

P3

P4

IT infrastructure


Change management


















6 Conclusion




Data Availability




References

























































































































IT infrastructure






Training




Financial support







Change management













Table 4 Continued


Big data analytics


Cloud integration






Decision-making


































Training

Financial support




Adoption of KMS


Decision-making

Use

P1

P2

P3

P4

IT infrastructure


Change management


















6 Conclusion




Data Availability




References



























































































































Table 4 Continued


Big data analytics


Cloud integration






Decision-making


































Training

Financial support




Adoption of KMS


Decision-making

Use

P1

P2

P3

P4

IT infrastructure


Change management


















6 Conclusion




Data Availability




References


















































































































































Training

Financial support




Adoption of KMS


Decision-making

Use

P1

P2

P3

P4

IT infrastructure


Change management


















6 Conclusion




Data Availability




References


































































































































6 Conclusion




Data Availability




References




















































































































Data Availability




References



















































































































Knowledge Management System Adoption to Improve Decision ...

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