Modeling the technology transfer process in the petroleum industry: Evidence from Libya

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Modeling the technology transfer process in the petroleum industry:Evidence from LibyaA.S. Mohamed a,∗, S.M. Sapuan b, M.M.H. Megat Ahmad c, A.M.S. Hamouda d,B.T. Hang Tuah Bin Baharudin b

a Institute of Advanced Technology, University Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysiab Faculty of Engineering, University Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysiac Faculty of Engineering, University Pertahanan Nasional Malaysia, 57000 UPNM, Kem Sungai Besi, Kuala Lumpur, Malaysiad Mechanical and Industry Systems Engineering Qatar University, P.O. Box 2713, Doha, Qatar

a r t i c l e i n f o

Article history:Received 30 October 2009Received in revised form 9 August 2011Accepted 9 August 2011

Keywords:Technology transferPetroleum industryLibyaStructural equation model

a b s t r a c t

The purpose of this study was to propose a conceptual model for technology transfer(TT) that houses several factors. These factors are believed to influence the processes’effectiveness and guide the TT performance. In addition, this study aimed to exploreTT performance and the relationship between TT government support, infrastructure,TT environment, and TT learning capability. Oil production in Libya is dependent onforeign technology transferred into the country by foreign multinational petroleumcompanies. During the 1980s, the Libyan government launched a program of developmentknown as ‘‘Libyanization’’ in the Libyan petroleum industry in an effort to create anabsorptive capacity to acquire petroleum technology dominated by foreign companies.This study evaluates the level of technical change because of TT programs and the impacton knowledge and competitiveness performance of the Libyan petroleum industry. Aquestionnaire survey was administered to companies in the Libyan petroleum industry.There were 201 responses from industry professionals in the Libyan petroleum industrythat were analyzed using structural equation modeling (SEM), exploratory factor analysis(EFA), and confirmatory factor analysis (CFA). In addition, the significance of direct andindirect interrelationships between model factors was determined through SEM. A pathmodel was estimated and specified to include three process enablers, namely governmentsupport, host characteristics, and learning technology capability, and one outcomefactor named TT performance. The results suggested that government support factor(government support, laws and regulations, petroleum industry strategy, internationalquality standards, and information technology) and technology learning capability factor(i.e., supervision, adoption, teamwork, absorption, training, technology complexity, andindustry knowledge) were determined to be the key predictors of TT performance to thehost petroleum industry.

© 2011 Elsevier Ltd. All rights reserved.

1. Introduction

In developing countries, technology transfer (TT) is a solution for the improvement of industrial and economic sectors.However, the success of any transfer depends on the proper choice of the proper technology from the right provider as well

∗ Corresponding author. Tel.: +218923705994.E-mail address: [email protected] (A.S. Mohamed).

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Fig. 1. Conceptual model for technology transfer in the Libyan oil industry.

as the absorptive capacity of the technology. Evaluating the TT effect in the petroleum industry is crucial to any petroleumcompany. This is more important than ever as the price of petroleum products and the cost of oil production have increaseddramatically in recent years. Most petroleum-producing countries are committed to developing their petroleum industriesto become competitive, compatible, and reliable. This often involves setting up the TT infrastructure considered necessaryfor the petroleum industry. Nevertheless, most of these countries lack the managerial and technical expertise to managesuch large projects. On the other hand, the petroleum industry was slow to accept and adopt new technologies. When oilwas discovered in Libya in 1959, and oil exports began in 1961, the country then had very few human resources to manageand operate a sizable modern petroleum industry. The foreign oil companies, not to mention a very weak government,then played the major role in establishing the conditions for the establishment of linkage and minor change capabilities,especially in developing human resources for the petroleum industry. Technology transfer continues to be a key energizerto industrialization and economic expansion in developing countries, mostly in the fast-growing oil-producing countriessuch as Libya, Algeria, and Nigeria.

2. Literature review

In the study of TT, numerousmodels have beendeveloped to analyze TT process [1–4]. None of these studies concentratedon the petroleum industry. Nevertheless, measuring the impact of transferred technology changes both according toresearchers and evaluators. In addition, not all TT study models were backed by strong observed data analysis. In manymodels, finding out themeaning of TT effectivenesswas intimidating. The researchers tried to define the TT term in differentways because of their individual areas of study. Chacko [5] cameupwith a definition of TT in a scientificmanner as convertingphysical or mental matter or energy into a direct usable alternate form. Williams and Gibson [6] defined TT as the sharedresponsibility between the source and the destination by ensuring the technology was accepted, or at least understood, bya user who has the required knowledge and the resources to apply the technology. In the construction sector, Simkoko [7]tried to use this definition by identifying individual construction resources as either materials or permanent equipment(e.g., steel beam, elevators, material) or construction-applied resources (e.g., information, skill). According to Waroonkunand Stewart [8], TT has been defined as when all types of knowledge about the construction field (e.g., design, constructionprocess,material use, equipment utilization, etc.) are transferred froma foreign party (transferor) to a host party (transferee)that arranges to receive it. For the purpose of this study, the TT process in the petroleum industry has been defined as whensome form of knowledge, material, or equipment is transferred from one foreign party such as a person or organizationto another local party such as a person or organization that arranges to receive it. Explicitly, the host industry refers onlyto Libya petroleum companies fully owned by the National Oil Corporation (NOC) or joint ventures, and foreign refers to aforeign company or organization working with Libyan petroleum companies to acquire projects or perform TT process. Asexpected, most foreign companies had their origins in developed nations such as the United States, United Kingdom, Italy,Germany, France, Australia, etc.

In this study, we developed and empirically tested a model that related to several antecedents factors for the transferof foreign developed technology by petroleum companies in Libya. The model, shown in Fig. 1, was based on extensiveliterature in TT processes and literature on TT. The TT model was specifically designed to be applied to the study of TT fromdeveloped countries to the Libyan petroleum industry. The TT process has not been tried before, and we seek evidence

A.S. Mohamed et al. / Mathematical and Computer Modelling ( ) – 3

supporting the relationship identified in our model via exploratory and confirmatory factor analysis. We believe in theimportance and the uniqueness of the Libyan petroleum industry TT processes. The exploratory test of our model wasconducted considering the factors adopted from previous studies. Libya is arguably one of the most prominent petroleum-producing countries in theworld today. Libya’s petroleum industry is currently undergoing rapid expansion, and technologytransfer for the petroleum industry development is likely to be an important engine for economic development in Libya.We used a convenience sampling of respondent representing over 30 petroleum companies across several petroleumindustries in Libya: oil production, oil exploration, petroleum technology, oil refining, and petroleum marketing. Thespecific behavior being modeled was the transfer of foreign developed technology by Libyan petroleum employees in theindustry.

Themodel of Calantone et al. [9] consistedmainly of five components that capture the TT process, whichwas constructedbased on Boddewyn’s [10] study on comparativemarketing research.Measuring TT process feedbackwas themain objectiveof the model. However, the model failed to include TT process performance indicators. Furthermore, the complex designmodel has not been empirically verified. Elements of the model would be suitable to be adapted to the petroleum industryTT model.

Simkoko [7] focused on TT in the construction industry of developing countries. The study was based on case studies of12 international construction projects in developing countries of Africa, South America, and Asia in 1987 and 1988. Datacollection was conducted in two schedules. One involved the examination of project files and the other involved site visitsand further interviews with project participants. The objective of this study was to examine the impact of TT programs andother internal and external environment factors on construction project performance. This study is now old, consideringthe development of more advanced TT mechanisms. This study only investigated the development of technological andmanagement, rather than attempting to model the TT process.

Kumar et al. [11] identified key elements that affect the ability of firms in developing countries. The paper studied theIndonesian manufacturing sector and its developments in recent decades; the sector has become the largest segment of theeconomy and is growing by nearly 10% annually. Kumar et al. [11] concentrated on small-scalemanufacturing industries, andhence this is considered a major hinder as the petroleum industry in most cases involves large-scale technological changes.Nevertheless, some of the learning capability model is suitable to be incorporated in the proposed petroleum industry TTmodel; these factors namely are the role of government and subfactors in the learning capability that include training andresearch and development (R&D).

Lin and Berg [2] carried out an exploratory study into the effects of cultural difference on TT projects. The aim of thisstudy was to provide empirical evidence that confirms the conceptual models developed by other researchers in the field ofTT. The Lin and Berg study focused on TT projects involving Taiwanese manufacturing companies. Three groups of factors,previously examined in conceptual studies were investigated: nature of technology; previous international experience; andthe cultural difference between the technology provider and receiver. An important conclusion made in their study wasthat TT study investigations should not be limited to only examining the direct effects of identified factors and associatedvariables. It was also important to examine causal interactions between factors to achieve an accurate representation ofthe TT process. Many of the factors and associated variables identified in this investigation were utilized to develop theconceptual model for TT in the petroleum industry described later. However, important influences such as governmentpolicy and mode of transfer, to name just two, have been neglected. The study does not adequately address all aspects ofcultural difference, leaving the framework somewhat incomplete.

Another model, developed by Malik [3], concentrated on intra-firm TT. Because the model was tested only on onemanufacturing company, the supporting empirical evidencemight have some biased testing. It should be noted that Malik’smodel was useful in developing the conceptual model for TT in petroleum industry projects in terms of identifying theinteractive nature of the communication process.

The study ofWang et al. [12] followed fromextensive previous research in the field of TT. His paper is primarily concernedwith the transfer of knowledge from a multinational company to a subsidiary. The Wang et al. [12] model was developedfrom semi-structured interviewswith 62multinational companies operating in China. However, themodel was also limitedby the scope of the TT process that was examined. This scope was confined to the amount of knowledge that a subsidiary ofa multinational company acquires because of the transferor and transferee characteristics. The model was not successful inexamining other influential factors such as government influence; technology characteristics, economic advancement, andcompetitiveness are factors to be adopted in the model for TT for the petroleum industry.

Waroonkun and Stewart [8] attempted to estimate performance rates of TT in developing countries; the study proposeda conceptual model for TT that accommodates several factors thought to impact on the processes’ effectiveness and derivedoutcomes. In their study, the transferee refers only to Thai architectural, engineering, and construction (AEC) firms, and thetransferor refers to the foreign AEC firms working with Thai firms to secure projects. The model depended on the maturitylevel of technology of the host nation. Justifiably, the model was designed for the construction industry and it may not beaccurate for other industries. However, some variables of their model were adopted in this study.

3. Conceptual model of technology transfer in petroleum industry

The development of a conceptualmodel for TT in the petroleum industry has been aimed at capturing all of the significantfactors that influence the effectiveness of the TT process and the resulting performance. These relevant factors have been


adapted from the examined leading studies into the TT phenomenon with the objective of developing a model that explainsthe TT process in the petroleum industry. Through a process of categorizing variables taken from previous studies andconceptualizing their relationship with one another in the petroleum industry context, a number of factors were identified.The factors identified were classified as enabling and TT outcome factors. The classification of variables into their relevantfactors, namely, TT support, TT infrastructure, TT environment, TT learning capability, and TT performance, has not beensolely based on other studies but rather is a conceptualization based on understanding of TT and the petroleum industry. Thestructure and links between the model constructs have also been conceptualized based on some empirical understanding,and they therefore require testing to confirm their appropriateness and validity. Fig. 1 illustrates the conceptual modelon how the developed enabling factors interact to create value for the host petroleum industry. The four main TT enablingfactors are shown at the left andmiddle of themodel as the constructs TT support, TT infrastructure, TT environment, and TTlearning capability. The outcome factor, TT performance, has been presented at the right of themodel. The arrows representthe hypothesized causal paths between each enabling and the outcome factor. Each of these causal paths is described in thefollowing paragraphs.

The variables contained in the TT support factor were found to have direct impact on those variables contained withinthe TT infrastructure factor. Therefore, the link from TT support to TT infrastructure was constructed in Fig. 1. Similarly, theliterature provided some evidence that the following causal relationships also existed: TT support – TT environment; TTsupport – petroleum industry learning capability; TT infrastructure – TT environment; TT infrastructure – TT performance;TT infrastructure – petroleum industry learning capability; TT environment – petroleum industry learning capability; TTenvironment – TT performance; and petroleum industry learning capability – TT performance. Although these links weresaid to have been described in past literature, this does not mean that every variable contained in each factor impacts onevery variable in another, rather that the factor when considered as a whole has impact on another. The three links from TTsupport to TT infrastructure, TT environment, and petroleum industry learning capability were insufficiently supported inthe literature, and therefore their validity was closely examined. Many enablers have been identified as having the potentialto impact on the effectiveness of the TT process, and they have been divided into four main categories in this study: transfersupport, transfer infrastructure, transfer environment, and petroleum industry learning capability. The following sectionsdescribe the reason for including each variable in the aforementioned enabling and outcome factors.

3.1. TT support

This factor is predominately concerned with the impact of government-related influence on the TT process [13].According to Kwon and Zmud [14], the availability of financial resources for the petroleum manufacturing industrytechnology must be considered during the TT process. The financial subfactor has been recognized in several studies as aninfluential factor that impacts on the effectiveness of the TT process [15]. The organization’s strategy towards the technologyto be transferred affects the efficiency and interaction pattern between the holding company and its subcompanies duringthe TT process [2]. The business strategy is concerned with the overall purpose and long-term direction of the parentorganization and its financial viability [16]. A TT process supported by government can decrease the technological gapbetween local and foreign companies by establishing innovation systems and policies that encourage technology researchand development (R&D) [17]. There is also a direct link between the level of government support to the industry R&D andtraining to apply a TT process. The government must plan ahead of time in the global petroleum industry to make theindustry competitive and investor friendly in the world [18]. Government support of petroleum industry technology hasbeen identified as an important consideration in the success of a TT process as it has an impact on several other influentialfactors. The study adopted four variables for this factor (laws and regulations, government TT plan, NOC strategy, and NOCreward system) from previous studies, which has been incorporated into the conceptual model.

3.2. TT infrastructure

Information technology (IT) and its impact on the TT process is unquestionably a major concern when managing a TTprocess [19]. The innovative use of a variety of IT tools may provide benefits to facilitate the TT process [20]. Accordingto Nazmun et al. [21], IT can increase the capacity as well as decrease the expenditure of information handling whichwill in turn enhance the success of the TT process. Local industry should have an interaction with local R&D centers anduniversities [22,23]. However, TT may not materialize if the technology gap between the foreign company and the localcompany is too large: it is generally believed that local participationwith foreign firms reveals the proprietary knowledge in away that facilitates TT to the domestic industry [24]. Notwithstanding, training is an important component of any TT process.TT through training could be in the form of practical training, where local employees are exposed to working methods andrequired to work in a highly developed industry environment to adopt new skills and techniques [25]. Growing attentionhas been paid to the possible role of TT agreements as part of the architecture of the TT process [26]. The foreign companyshould take all viable steps to promote, facilitate, and finance as appropriate the transfer or access of sound technologies andknow-how to the local industry [27]. Management as an important subfactor can take actions to develop an infrastructurethat is supportive of the TT process. The management approach would significantly contribute to low or high TT processperformance [28]. The six variables of this factor (IT, R&D, subcontractor, training, standards and quality, and managementpractice) have been incorporated in the conceptual model from previous studies.


3.3. TT environment

A major concerns of managing TT is the environment in which the interaction between the foreign technologyprovider and the host industry takes place, and its effect on the success of TT process performance [2,29]. Williams andGibson [6] suggested that TT should be conceptualized as a communication process where gaps between foreign and localenvironments will affect the efficiency of inter-firm communication and the overall effectiveness of the TT process.Wei [30]reported that prior international technology experience in international operations is helpful for the host to gather relevantinformation during the TT process. In contrast, Lin and Berg [2] suggested that previous foreign experience of the hostcan increase its capability to preserve core technology from the foreigner, eventually resulting in the host becoming aserious competitor of the foreign company. Actually, among all the resources of a firm, knowledge is the most strategicallyimportant resource [31]. Knowledge provides the capacity for organizational action and new knowledge provides thecapacity for organizational renewal [32]. Nonaka [33], for instance, argues that tacit knowledge accounts for three quartersof all knowledge used by firms. A complex system or technology may need a longer time, more technical people, andhigher capital investment to be transferred. Madeuf [34] therefore suggests that the nature of the technology will affectTT effectiveness, and this needs to be carefully investigated in managing a TT process. Most technologies are very difficult totransfer because they include a large portion of tacit knowledge. According to Nanoka [16], tacit knowledge is not easilyvisible, not easily expressible, highly personal, hard to formalize, and difficult to communicate. Several dimensions areproposed to characterize the nature of a technology to describe its transferability [35]. Robinson [36] proposes that theskill and education level required to adopt a technology by the technology receiving team is an indicator of the complexityof a technology. Therefore, a TT process to transfer a complex technology is likely to have a lower success rate. Achievingsuccess in a TT processwill require the information to be conveyed clearly and effectively in a total error-free communicationsetting. A successful TT process requiresmany factors, in particular a high level of commitment to shared goals. Saunders [37]provided a model that is characterized by frequent communication both formally and informally, namely, open sharingof information. Carolynn et al. [38] indicated that effective communication was given a very high rating by all categoriesand by both organizations involved in the process. Zeller [39] describes the introduction of cross-functional teams aspart of the reorganizing of R&D activities within pharmaceutical companies in response to the increasing globalizationof R&D. More recently, Michie and Sheehan [40] considered firms with high levels of participation in teamwork as partof their examination of the impact of an alternative system TT process. The five factor variables (experience, knowledgebase, technology complexity, communications, and teamwork) have been adopted from several studies to be used for theconceptual model.

3.4. TT learning capability

TT learning capability is concernedwith the affects of the subfactors that facilitate the technology that is being transferredbetween the host and local companies. The issue of culture in a TT process has been studied by Kedia and Bahgat [41],and they concluded that if the foreign and host companies did not emphasize the issue of culture, the result may be anunproductive TT process. The importance of recognizing the apparent and hidden components of the host country cultureinvolved in the TT process depends on several factors such as attitude towards foreigners and the company’s reputation [42].The cultural traits of the two parties can have a significant impact on the effectiveness and hence the success of any TTprocess [43]. A TT process in which the cultural gap between the host and the foreigner is high is expected to result in anunsuccessful TT process [2]. Adoption of new technology requires some modification to fit with changes in the workingenvironment by controlling the working environment variables or making an adjustment to synchronize the host andforeign company policies [44]. The importance of adoption is crucial because perhaps the inputs from the host are notthe same as for those for whom the equipment was designed. The ability of any company to absorb advanced technologydepends on the organizational and technical capabilities of the company [45,46]. Structural systems should adopt measuresof quality and performance, and promote learning within the company [47]. The company’s current absorption capacitywill be determined by the extent of their ability to participate in the transfer of technology [30,48,49]. Recent researchby Escribano et al. [50] suggested that the capacity for absorption is in fact a source of competitiveness. In a TT process,exposure occurs when employees become informed and educated about the technical and manufacturing systems andtheir applications that were not diffused or applied in their industry environment previously. In their research, Arboseand Bickerstaffe [51] argue that many of the users who have an earlier exposure to technology have a greater capacity toaccept a TT process. Recently, Liu [52] suggested that employees with technological knowledge who had not been exposedto external expertise will need to be exposed to foreign knowledge. Respectively, a company which has been exposed toforeign expertise will develop methods for local R&D; meanwhile, a company which has not been similarly exposed has todepend on more foreign expertise. More recently, Feldman and Bercovitz [53] studied university technology transfer usingdata on individual researchers from the medical schools of Duke University and Johns Hopkins University. They found thata high percentage of supporters of a TT process and new organizational strategic initiatives were more recently trained staffmembers, more likely encountered in an environment supportive of commercialization activity. There is little informationon the role and impact of the supervisor in a TT process. However, Miles [54] suggested that one of the reasons for the failureof a TT process is incautious choice of the supervisors for the TT process, without considering the required knowledge for thatTT process. Susan and Cromwell [55] suggested that supervisors who had the proper setup and management support and


had participated previously in TT processes would excel in future TT processes. In contradiction, supervisors who reportedless participation and did not have time and enough support in previous TT processes would be considered a real barrier toa TT process. Five variables of this construct (culture, attitude, capability, exposure, and supervision) were combined basedon several studies and applied in the conceptual model.

3.5. TT performance

The performance of a TT process could be examined from numerous attributes in the literature. From the viewpoint ofefficiency, Teece [56] tried to measure the effectiveness of a TT process through the calculation of the cost of TT. Viewingfrom the perspective of a TT process within the organization, Schwarz [57] defined the effectiveness of TT as adequateR&D in the local organization. Similar findings are evident in the work of Alam and Langrish [58]. Zakaria [59] discussedthe transfer of technology to the petroleum industry on the bearing capacity of the country to purchase or lease the besttechnological equipment. Manson [60] determined that the effective transfer of technologymust bemeasured if it facilitatesdeveloping the methods and the acquisition of new skills. Mytelka [49] suggested that the incorporation of technologyis the best and only way to possibly modify, improve, and extend it later. One of the leading motivations for developingcountries to adopt and apply TT programs is the expectation that TT would enhance the standard of living [61,62]. Theeconomic development subfactor is concerned with the extent of competitiveness between the Libyan petroleum industrycompanies in local markets and global markets. In addition to the economic benefits expected to be obtained by the transferof technology, the local petroleum industry could also benefit from cognitive development at the level of individual users,as well as at the enterprise level [63,64].

Preliminary results of a TT process are the transfer to the individual employee of tacit knowledge to explicitknowledge [65]. The knowledge gain subfactor is concerned with the improvement caused by the TT process in theknowledge of local industry employees, improvements causedby the TTprocess, and its impact on themethods and technicalskills of the employees. The TT process has a number of results at each stage and between stages and at the final stage, whichare sometimes delayed because of the length of the development process [66]. According to Devapriya and Ganesan [67], thekeymotivations of the TT process in any industry are highly effective financial performance, efficient schedule performance,and significant quality operating performance. Enhanced commendable performance must be the key outcome of any TTprocess. The project performance factor’s main objective is to measure the impact of TT effectiveness on the industry [68].In particular, from an evaluation perspective of performance this would be in terms of progress of financial, schedule, andquality assessments. The output of the TT process is measured in comparison with the objectives identified in advance withmost emphasis on time, cost, and quality [7]. The variables (competitiveness, performance, working practice, skill base,financial performance, and schedule performance) of the TT performance construct were adopted from previous studies.

4. Research methods

4.1. Data collection

Data collection for this study was undertaken with Libyan petroleum professionals in the fourth quarter of 2008. Thetarget group of respondents are technical’s, engineers, supervisors, managers of departments of the Libyan petroleumindustry and have relationship to TT process in their companies. This study only solicited the perceptions of local petroleumprofessionals (Libyans) since TT initiatives are ultimately undertaken for the purpose of improving knowledge levels andenhancing the industry capacity of local participants. Accordingly, individuals from the host nation were considered thebest respondents to evaluate the importance and effectiveness of variables pertaining to the TT process and the outcomesit can potentially generate. As expected, it was difficult to determine the adequate number of sample participants for thisstudy. However, this process made use of available statistics on the NOC and statistics of the Libyan petroleum companiesavailable [69]. Furthermore, counselingwith professional academics in the relevant statistical researchers. The total numberof petroleum employees in Libya, according to NOC statistics, is 45,000. The approximate number of employees in TT-related past and present petroleumprocesses is about 5000. In total 300 questionnaireswere distributed and 205 completedresponses were received, i.e., 68%. The statistical methods used to analyze the data are shown below. The statistical packageSPSS ver. 11 was utilized in this research due to its accuracy and effectiveness; it is suitable for quantitative analysis. Thequestionnaire for the study consisted mainly of three parts and included 63 questions in total: 26 questions were used foranalysis, 26 were utilized for descriptive analysis, and 11 questions were for the background of the respondents. To confirmthat the data was obtained from a reliable source, the background section contained questions about the participant’s yearsof work experience, position held, amount of education, sex, and number of TT projects involved with. The definition ofeach part and factor of the questionnaire survey was offered. In addition, a translated copy of the questionnaire into theArabic language was prepared in order for participants to understand the questionnaire correctly. The questionnaire surveywas prepared based on two key indicators. First, by picking out the answer, participants would provide their opinion on theeffectiveness and success of a factor in the TT process. Subsequently, the participants would rate the impact of this factor onthe TT process. The choices of the questionnaire answer were designed on a five-point Likert scale, ranging from ‘‘stronglyagree’’ to ‘‘strongly disagree’’ in the first part, while the impact assessment of the answer ranges from ‘‘strongly positive’’to ‘‘strongly negative’’. Moreover, they enabled causal links between variables to be established. In summary, the data set


Fig. 2. Respondents’ petroleum experience.

obtainedwas utilized to ensure that the variableswere perceived to be sufficiently important to be considered as essential TTenabler and outcome variables, examining the successfulness effectiveness of such TT variables, and it was also utilized forevaluation purposes, formulating TT constructs and determining causal paths. Statistical techniques including exploratoryfactor analysis (EFA), confirmatory factor analysis (CFA), and structural equationmodeling (SEM)were implemented for thispurpose. A complete description of each of these stages is provided.

4.2. Data screening

Data screening is a vital precaution before proceeding with data analysis to ensure that the data accurately reflect theresponses made by participants of the study. It is undertaken to check if some of the data is missing and if there is a patternto the missing data, and in addition, to look for extreme responses present in data set that may distort the understandingunder study. Moreover, it is done to ensure that multivariate statistical assumptions are met, and to decide what to do ifviolations are there. Data cleaning was performed using SPSS ver. 11, based on visual inspection of the box plot, distributiondiagnosis, frequency tables, histograms, bar graphs, scatter matrices, and outlier cases. Additionally, data screening wasapplied to detect multivariate outliers and validate multivariate assumptions (normality, linearity, homoscedasticity). Datascreening of data set indicated four unusual cases attributes code violation that was eliminated after inspecting each case.On the other hand, two variables that violated the multivariate assumptions (experience, culture) were removed from thedata set due to extreme scores that resulted in a measure of central tendency that does not really represent the majority ofthe scores. Additionally, analysis of variance (ANOVA) was performed to ensure that respondents having different positions(e.g. supervisor, superintendent, engineer, etc.) and from different specializations of petroleum companies (production,exploration, etc.) could be considered as a single sample. ANOVA confirmed a correspondence between position types atthe 0.05 level of significance. A careful examination of data suggested that variance was not widespread and only resultedin two combinations the datawas treated as one useable sample. Critically examining the quality of data collected to prepareit for data analysis resulted in retaining 201 cases after deleting four cases and 24 questions after the removal of two violatingvariables.

5. Descriptive statistics

5.1. Respondent profile

The acknowledged number of valid respondents involved in the questionnaire survey was 201. Determining theexperience of process participants was critical for ensuring the validity of the results. The greater the experience of therespondent in the petroleum industry means a greater understanding of process outcomes and influences. The highestfrequency of respondents had 11–15 years of experience, as shown in Fig. 2. This group accounted for almost 39.3% of the201 respondents; the lowest group was the 0–5 years of experience group, accounting for 1.5% of respondents. However,there was a fairly well distributed frequency of respondents in each category of experience. This spread of respondent yearsof experience should provide a balanced view on how the TT processwas perceived by the actual Libyan petroleum industry.

Respondents were requested to detail the number of processes they had been involved with where TT was incorporated(Fig. 3). Almost 65% of the questionnaire participants had been involved with at least three past processes involving TT.1.5% of the sample had been involved in more than seven TT processes. These provide a good basis for evaluating theimportance and success of individual TT processes and outcome variables. Advantageously, there was a relatively lowfrequency of respondents that had been involved with only two past TT processes (5.5%). As a result, this may not boundtheir understanding of the TT process; they may still have a good understanding of process success. As expected, very few(about 0.5%) participated in more than nine TT processes.

Table 1 gives a breakdown of respondent experience versus the number of TT process involvements. Obviously, theanalysis determined that the experience of respondents and the number of past TT processes they were involved in were tosome extent related.


Table 1Experience and number of previous TT processes.

Experience (years) Number of TT processes1–2 3–4 5–6 7–8 9+

0–5 2 0 1 0 06–10 4 36 1 0 011–15 1 56 20 1 116–20 2 31 25 1 020+ 2 6 10 1 0

Fig. 3. Respondents’ previous involvement in TT processes.

Fig. 4. Positions of respondents.

Most participants with experience on only one or two TT processes had between 6 and 10 years of experience.Respondents with experience on three or four TT processes generally had about 11–15 years of experience. Obviously,employees with more than 10 years of experience should be involved in more than 3 TT processes. Very few respondentshad more than 15 years of experience and in addition had been involved with more than 7 TT processes. These respondentsare likely to have an excellent understanding of process outcomes and influences; consequently, their opinions are of greatvalue to this study.

The position held by respondents in their respective organization is given in Fig. 4. It is essential to evaluate the positionheld by the respondents not only to give credibility to the results but also to understand the perspective from whichthe survey questions have been interpreted. This will prove to be valuable when examining the factor analysis resultsand should make grouping of factors and rating of factor importance easier. 33.7% of respondents for these studies weresupervisors, followed by engineers with 30.2%. These respondents will have an informed perspective of all daily operations.Thus, they would be able to critically evaluate all process issues, especially those concerning the enablers like teamwork,understanding, and communication. There were also relatively moderate proportions of project engineers: 21%. Otherpositions such as project manager, superintendent, technicians, and others accounted for about 16% of the respondentseach (approximately 31 employees).

Evaluating education levels was necessary to demonstrate that the respondents were sufficiently educated to develop aprofessional opinion about the petroleum industry. Obviously, the petroleum industry in Libya was dominated by the malegender (100%). As shown in Table 2, if, for example, there were a high frequency of respondents with only a high schooleducation, the results obtained may carry questionable authority and may not be accommodating as an accurate and validinterpretation of the TT process.


Table 2Respondents’ personal characteristics.

Item Personal characteristics No. of respondents Percentage

Gender Male 201 100Female 0 0

Age

<30 3 1.530–40 88 43.841–50 103 51.251+ 7 3.5

Education

Diploma 23 11.4Bachelor degree 148 73.6Master degree 27 13.4Doctorate degree 2 1.0Others 1 0.5

Table 3TT transfer mode and nationality.

Nationality Mode of transfer CountJoint venture Turnkey Management agreement Other modes

1. United Kingdom 25 25 46 10 1062. Germany 7 19 19 4 493. Italy 20 10 14 3 474. Austria 4 6 4 1 155. Others 20 21 34 10 85

Total 76 81 117 28 302

The survey targeted experienced Libyan petroleum professionals involved in TT processes. As a result, the age ofrespondents centered around 30–40 and 41–50 periods, totalling 95%. Low frequencies were reported of less than 30 years(1.5%) and more than 50 years (3.5%).

Fortunately, the highest frequency of respondents had a bachelor degree (73.6%). Masters degree qualified respondentswere also quite common (13.4%), followed by diploma qualifications (11.4%) and doctoral degree (1.0%). Unexpectedly, veryfew respondents had less than a high diploma in the petroleum field (0.5%).

5.2. TT project profile

Survey participants were requested to detail the number of processes they had been involved with where TT wasincorporated. More than 80% of the questionnaire participants had been involved with at least three past projects involvingTT. Less than 5% of the sample had been involved in just one TT project. This gave them a good basis for evaluating theimportance and success of individual TT process and outcome variables. Understandably, very few respondents (about 2%)participated inmore than eight TT projects, since planned TTwas a relatively new concept in the Libyan petroleum industry.Respondentswere requested to provide a range of information on the last three petroleumprocesses they had been involvedin where TT from a foreign partner was integrated. In total, the respondents provided detailed information on 302 processesperformed. The information collected for the processes included year completed, process description, foreign nationality,skills transferred,mode of transfer, and a general rating on the success of the process. A descriptive summary for each of theseitems was provided. The primary mode of transfer for processes in the petroleum industry where TT was implemented wasmanagement contracting (38.7%) and turn-key (26.8%), closely followed by joint venture (25.2%), followed by other modesof transfer (9.3%). This drift confirms the remarks made by Hill [70] that turn-key projects are common in petrochemicalplants and oil refineries. Also, this will reinforce other studies reported in the literature [1].

Additionally, the nationality of the technology transferor (i.e., foreigner) for each of the 302 listed processes wasrequested. The United Kingdom (UK) was involved in the highest numbers of processes (35.1%), followed by Germany(16.2%), Italy (15.6%), Austria (5%), and other countries (23.5%), Other transferor nationalities included Canadian, French,Spanish, Korean, and Irish, to name a few (see Table 3).

This result support the information published recently by Otman and Karlberg [71] that the United Kingdom is by far thelargest source of foreign direct investments in Libya. Not surprisingly, Germany and Italy, two countries which have playeda major role in Libya’s petroleum industry during the sanction period are second and third in the list [72].

Table 4 shows that therewere four groupings of skills transferred during the TT process. The skills includedmanagement,technical, new technology, and other. Technical skills were predominately transferred (44%), followed by technical and newtechnology skills, both being transferred on 27.8% of the processes.

Skill types other than those previously mentioned (e.g., research and development) were transferred on only 2% of theprocesses. Most processes examined transferred more than three types of skill.



Nationality Skill transferred Count1 2 3 Skills

Management Technical skill New technology 1+2 2+3 1+2+3

UK 5 50 11 6 0 34 106Germany 1 21 12 2 3 10 49Italy 2 14 6 8 1 16 47Austria 0 7 0 2 0 6 15Others 10 31 9 5 2 24 85Total 18 133 38 23 6 84 302


Rating Nationality Total Percentage (%)UK Germany Italy Austria Others

Very low 0 0 1 0 2 3 1Low 1 4 0 0 1 5 1.65Moderate 15 6 6 2 17 46 15.23High 80 37 35 13 59 224 74.17Very high 10 2 5 0 7 24 7.95

Table 6TT process description and year completed.

Year Technical process Training Advanced technology Petroleum project Study abroad Total

1990–1994 2 4 0 7 0 131995–1999 21 11 7 14 5 582000–2004 69 39 28 52 2 1902005–2009 12 10 5 11 3 41Total 104 64 40 84 10 302

Fig. 5. Nationality success rate.

To realize how successful the affect of TT process on single process, the respondents were requested to rate each processon a scale from very low to very high. A total of 270 process represents the majority of processes were rated as having eithermoderate or high success. However, only 2.65% indicated a low or very low success rating combined (see Table 5).

Understandably, it was difficult to indicate that a particular country transferred technology better than another countrygiven that the sample size for each country is unequal.

Notwithstanding, evaluating the mean of the rating provided insight to the TT process (Fig. 5): the United Kingdomreceived the highest score of 3.93, and Italy followed at 3.92, while the others somewhat received lower rates.

Respondents provided a list of process completion dates which grouped into 5-year categories from 1990 until 2009.Obviously, from Table 6, the period 2000–2004 has a high number of technology-related processes performed in the Libyanpetroleum industry. This highlights that most petroleum technology in the Libyan industry was transferred on a large scalerecently due to the full lifting of sanctions, along with possible changes to Libya’s 1955 hydrocarbon legislation.

Sanctions had caused delays in a number of field development and enhanced oil recovery (EOR) projects, andhaddeterredforeign capital investment to a significant extent [73]. This study found that petroleum technology was rarely transferredduring the sanction years (1990–2000) Fig. 6. The reason for this low performance was that many international companies


Fig. 6. TT process description and year completed.

Fig. 7. TT process count and nationality.

Table 7TT process planning count and year completed.

Year Yes No Don’t know Total

1990–1994 13 0 0 131995–1999 54 1 3 582000–2004 180 5 5 1902005–2009 37 3 1 41Percentage 94.04% 2.98% 2.98% 302

having advanced petroleum technologies, many of which they owned under patent, were not allowed to participate in theLibyan petroleum industry.

However, Libya is seeking foreign company help to increase the country’s oil production capacity from 1.60million bbl/dat present to 2 million bbl/d by 2008–2010, and to 3 million bbl/d by 2015 [73]. In order to achieve this goal, and to upgradeits oil infrastructure in general, Libya is seeking asmuch as $30 billion in foreign investment over that period. The last period(2005–2009) showed lower than expected because data for years 2008 and 2009 were not available.

Themain study provided some indications that the United Kingdom has been themost common transferor of technologyin Libyan petroleum processes over the past 20 years (Fig. 7). Italy and the Germany also have a strong representation in thefield of TT in Libya’s petroleum industry. Austria has had a relatively low TT involvement over the past 20 years. The ‘Other’nationality group included Canada, Korea, and Spain.

Respondents were asked whether they knew if TT was planned prior to the beginning of the process (see Table 7). Thiswill help to develop a better understanding of the respondents’ perception of the TT process and how completion plannedcould improve skills.

For the 302 listed projects where TT was integrated, almost 6% of the respondents acknowledged that TT was either notplanned or that they did not know. Thus, the majority of respondents knew whether TT was proactively planned prior tothe project execution phase over the past 20 years. During the 1990–1994 period, it had been found that technology hadbeen slowly transferred into petroleum industry in Libya. In the 1995–1999 period, technology had been transferred atalmost 17.9%; meanwhile, the percentage in the 2000–2004 period improved to 59.6% of the total processes surveyed. Thus,it appears that Libya is not only undertaking amassive upgrade of its petroleum infrastructure in general in recent years butis also utilizing what was considered a highly attractive petroleum state due to its low cost of oil recovery. Consequently,TT programs were utilized to improve the skillfulness of petroleum employees.


Table 8TT process planning count and year completed.

Code Description Mean Std. deviation

EnablersA TT support 3.90 0.622A2.1 Laws and regulations 3.87 0.688A2.2 Government TT plan 3.99 0.845A2.3 NOC strategy 3.85 0.807A2.4 NOC reward system 3.92 0.865B TT infrastructure 3.92 0.600B2.1 Information technology 3.91 0.729B2.2 Research and development 4.03 0.854B2.3 Subcontractors 3.82 0.799B2.4 Training 3.93 0.761B2.5 Standards and quality 3.86 0.913B2.6 Management practice 3.98 0.869C TT environment 4.05 0.645C2.2 Knowledge base 4.14 0.796C2.3 Technology complexity 3.96 0.896C2.4 Communications 4.05 0.756C2.5 Teamwork 4.05 0.870D TT learning capability 4.04 0.669D2.2 Attitude 4.05 0.882D2.3 Capability 4.00 0.815D2.4 Exposure 4.09 0.798D2.5 Supervision 4.02 0.827TT outcome

Economic performance 4.01 0.693A4.1 Competitiveness 3.92 0.760A4.2 Performance 4.09 0.834

Knowledge performance 4.01 0.721B4.2 Working practice 4.00 0.809B4.3 Skill base 4.01 0.803

Project performance 4.02 0.657C4.1 Financial performance 3.93 0.803C4.2 Schedule performance 4.11 0.747

6. Data analysis and results

6.1. Rating TT variables

As previously described, the questionnaire respondent rated the impact for the retained 24 items on a five-point Likertscale for the success or effectiveness of TT process. Table 8 details the mean and standard deviation value for each variablein the conceptual model. The significant outcomes of this analysis are summarized below.

• The TT environment mean (4.05) was considered the most important TT process enabler, followed by learning capability(4.04). However, TT infrastructure (3.92) and the TT support construct (3.90) were not considered as important as theothers were. Possibly, this is due to respondents’ limited understanding of the impact of macro factors on the TT process.

• Industry knowledge (4.14) was considered the most important variable. Surprisingly, involving subcontractors in the TTprocess (3.82) and the construction mode of transfer (3.86) were considered the least important enabling variables.

• Most of the TT outcome variables were deemed highly and equally important. Respondents perceived that theimplementation of TT programs was essential for improving schedule performance (4.11). Since schedule performancein developing countries is generally perceived to be quite low, this result is not surprising. However, the importance ofTT for improving financial performance (3.93) and competitiveness (3.92) of host companies was notably lower than theother outcome variables.

The variables within all constructs were considered important (mean> 3); therefore, 24 variables of impact perspectivewere used for the initial factor analysis computation.

6.2. Exploratory factor analysis

EFA using principal component analysis (PCA), with varimax rotation, was conducted to condense the informationcontained in the original 24 variables into a smaller set of factors with a minimum loss of information [74,75]. Specifically,the aim was to search for and define the fundamental constructs assumed to underlie the original variables. The ratio ofdata sample observation to variable (8.3:1) was deemed adequate for factor analysis, exceeding that recommended byHair [74]. Moreover, the value for the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.95, exceedingthe recommended threshold level of 0.5 [76]. EFA retained a 21-variable solution, removing three variables A2.4 (reward


Table 9Varimax rotated factor loading for the five-factor solution.

Factor Items (identifying questions) v (63.937%) explained Loading

1. Government supportVariance = 4.675% Government TT plan 0.814Eigenvalue = 4.675 NOC strategy 0.807Cronbach’s alpha = 0.720 Standards and quality 0.780

2. Host infrastructureVariance = 3.850% Communications 0.843Eigenvalue = 0.924 Research and development 0.814Cronbach’s alpha = 0.742 Subcontractors 0.779

3. Technology learning capabilityVariance = 46.65% Supervision 0.806Eigenvalue = 11.2 Attitude 0.797Cronbach’s alpha = 0.883 Teamwork 0.768

Capability 0.759Training 0.755Technology complexity 0.741Knowledge base 0.738

4. Local characteristicsVariance = 3.476% Information technology 0.859Eigenvalue = 0.834 Laws and regulations 0.859Cronbach’s alpha =0.625

5. TT performanceVariance = 5.291% Overall performance 0.826Eigenvalue = 0.1.270 Industry knowledge 0.818Cronbach’s alpha = 0.870 Skill base 0.799

Schedule performance 0.760Competitiveness 0.736Financial performance 0.733

Table 10Varimax rotated factor loading for the single outcome factor solution.

Factor Items (identifying questions) Loading

1. Government supportVariance = 53.341% NOC strategy 0.758Eigenvalue = 2.67 Standards and quality 0.739Cronbach’s alpha = 0.778 Laws and regulations 0.731

Government TT plan 0.721Information technology 0.701

system), B2.6 (management), and D2.4 (exposure). Two of the removed variables (i.e., management and reward system) hadvery high loadingswithin their own individual constructs. These factors could be argued as being essential enablers in the TTprocess; however, they were removed because there were factors, which consisted of only one generic variable. Five factorsbest represented the data in terms of variance explained (64%) and grouping of variables. These factors included governmentsupport, host infrastructure, technology learning capability, host characteristics, and TT performance. Table 9 details thefactor loading, explained variance, eigenvalues, andCronbach’s alpha for the five-factor solution. All factor loadings exceededthe 0.5 threshold level with loadings ranging from 0.733 to 0.859. Additionally, Cronbach’s alpha results ranged from 0.625to 0.883, indicating that the scale used was reliable [77,78]. The results underline that the technology learning capabilityfactor is the key enabler of the TT process, explaining almost half (46.65%) of the total variance in the data set (64%). Thecombined explained variance for the TT process enablers (i.e., government support, host characteristics, technology learningcapability, and local characteristics) equates to more than two thirds (58.7%) of the total variance (64%). Unquestionably,these factors need to be carefully managed to ensure that the TT process derives the most value for the host country.

Factors 1 and 4 are related to the TT support required for TT, and factor 4 contained only two variables. These two factorswould be better represented as one broader enabling factor, as originally perceived in the conceptual model (Fig. 1). Tosubstantiate this model, factor analysis was performed on these combined two factors (5 variables), as shown in Table 10.

As suspected, only one factor resulted, explaining 55.064% of the variance and all loadings exceeded 0.7, as shown inTable 11. Thus, factor analysis produced a TTmodel consisting of four factors, including three enablers, government support(GS), host characteristics (HC), and technology learning capability (TLC), and one outcome factor, TT performance (TTP). Thefollowing stage of analysis utilizes structural equation modeling (SEM) to confirm the model structure and causal pathsbetween factors.


Table 11Measurement model results.

Factor variable description Standardized regression weights t-value R2

Technology learning capabilityTraining 0.71 * 0.50Knowledge base 0.70 9.45 0.49Technology complexity 0.68 9.19 0.47Teamwork 0.73 9.84 0.54Attitude 0.75 10.13 0.57Capability 0.70 9.44 0.49Supervision 0.76 10.23 0.58Government supportLaws and regulations 0.61 * 0.37Government TT plan 0.68 7.41 0.47NOC strategy 0.68 7.75 0.47Standards and quality 0.65 7.45 0.42Information technology 0.60 7.00 0.35Host characteristicsSub contractor 0.69 8.72 0.47Research and developments 0.71 * 0.50Communications 0.71 8.99 0.50TT performanceCompetitiveness 0.64 * 0.41Overall performance 0.78 9.09 0.61Industry knowledge 0.78 9.07 0.61Skill base 0.77 9.02 0.59Financial performance 0.66 9.25 0.44Schedule performance 0.69 8.26 0.48* Fixed for estimation.

6.3. Structural equation modeling (SEM)

SEM using AMOS 4 software was performed to test the study model and interrelationships between factors. SEM isan effective technique for conceptualizing a theoretical model, confirming relationships between variables, and gaininginsight into the causal nature and strength of identified relationships [79]. However, SEM is a structural technique requiringlarge samples with a minimum sample size of 15 cases per measured variable. Since factor analysis reduced the number ofvariables to four factors, a satisfactory ratio of 50:1 cases permeasured variable was achieved [80]. Moreover, the developedmodel needs to satisfy conditions for a number of ‘‘fit’’ indices. A rule of thumb is that the comparative fit index (CFI) andother incremental indices with values greater than 0.90 may indicate a reasonably good fit of the study’s model [81]; thegoodness of fit index (GFI) is an absolute index, and it requires values to be above 0.90 as well; TLI is the Tucker–Lewiscoefficient, also called the Bentler–Bonett non-normed fit index (NNFI). TLI is not guaranteed to vary from 0 to 1; however,a TLI close to 1 indicates a good fit; and the root mean square of approximation (RMSEA) is a lack of fit index where a valueof zero indicates the best fit and higher values indicate a worse fit. The rule of thumb is that values less than 0.05 indicate aclose approximate fit, values between 0.05 and 0.08 indicate reasonable error of approximation, and values greater than 0.10indicate a poor fit [82]. For the purpose of this study, SEM was employed for the following two main tasks: confirmatoryfactor analysis (CFA) to corroborate the four constructs established through exploratory factor analysis (i.e., testing themeasurement model), and determining significant causal paths between factors.

6.4. Measurement model

Confirmatory factor analysis (CFA) was undertaken to substantiate the results determined through EFA (Fig. 8). Themeasurement model for these four constructs (latent variables) had acceptable goodness of fit indices: RMSEA = 0.042,GFI = 0.90, CFI = 0.97, TLI = 0.96, [79,83]. Moreover, the results shown in Table 11 indicated that the items used foreach factor were representative of that factor (regression weights > 0.6, significant t-value at the 0.05 level). In summary,CFA confirmed the EFA analysis and did not suggest the removal of any variable, and as a result the same constructs wereutilized for subsequent path analysis.

6.5. The path analysis of the model

Paths analysis was undertaken using the SEM technique to uncover the significant interrelationships between the factorsretained from EFA and CFA. From the analysis, it was determined that government support was the only exogenous factorin the model. The remaining enablers were considered endogenous factors (Fig. 9). In order to access what is called asaturated model in the sense that every variable was hypothesized to be related to every other variable. The model allowsexamining the direct effect of government support and hosting characteristics on TT performance. Nevertheless, it also


Fig. 8. Confirmatory factor analysis (CFA) design.

allows examining some of the indirect effects as well. Not only are government support and host characteristics said todirectly affect TT performance, they are also hypothesized to exert an effect through the technology learning capabilityvariable. The technology learning capability takes a mediator role in this model, and it could be said that some of the causalinfluence of government support and host characteristics is mediated through the technology learning capability. Thus,government support and host characteristics are said to influence TT performance in two ways: first by exerting a directeffect on TT performance and second by exerting an indirect effect on TT performance by affecting the technology learningcapability.

The model is considered to be recursive. Using the model-fitting method with AMOS allows one to quickly review theresults of the initial model. Standardized regression weights were assigned to the appropriate paths from each analysis. Thepath coefficients showed that government support and host characteristics are both significant predictors of technologylearning capability. Because they exceed the 0.3 criterion, both would be treated as having achieved practical significance aswell. However, the path coefficient leading from host characteristics to TT performance (0.09) is insignificant, indicatingthat the host characteristics variable has an indirect effect, accomplished through the mediator variable of technologylearning capability. Only two of the three predictors of TT performance yielded significant coefficients. The average of theresiduals between the observed correlation/covariance (RMSEA) from the sample and the expected model estimated fromthe population. Loehlin [84] proposes the following classifications: (1) less than 0.08 indicates good fit, (2) 0.08–0.1 indicatesa moderate fit, (3) greater than 0.1 indicates a poor fit. The RMSEA in this model is 0.65, indicating a poor fit.


0.42 (0.43) ***

0.45 (0.44) ***

0.68 (0.72) ***

0.36 (0.34) ***

0.40 (0.42) ***

Fig. 9. Path model for TT in petroleum industry.

0.46 (0.46) ***

0.45 (0.44) ***0.68 (0.72) ***

0.40 (0.39) ***

0.40 (0.42) ***

Fig. 10. Respecified path model for TT in the petroleum industry.

Table 12Standardized path coefficients and structural equations.

Paths Standardized equation Coefficients t R2

GS→ HC ZHC = 0.86*(ZGS) γ = 0.68 6.75∗∗∗ 0.46GS → TLC ZTLC = 0.40*(ZGS) + 0.45*(ZHC) γ = 0.40 6.75∗∗∗ 0.61GS → TTP ZTTP = 0.46*(ZGS) γ = 0.46 7.43∗∗∗

HC → TLC ZTLC = 0.45*(ZHC) β = 0.45 7.49∗∗∗

TLC →

TTPZTTP = 0.40*(ZTLC)+0.46*(ZGS) β = 0.40 6.55∗∗∗ 0.63

* P < 0.001.

6.6. Respecifying the path model

In the model, one of the direct effects in the original did not statistically materialize. The path coefficients from hostcharacteristics failed to achieve a statistical significance; therefore the paths were dropped from the respecified modelshown in Fig. 9. Moreover, discriminate validity analysis did not uncover any correlated endogenous perspective [85,86].Additionally, scatter plots between the four factors were conducted to ensure that a linear trend best represented (i.e.,highest R2 fit) their relationship. Fig. 10 presents the formulated path model for international TT in the petroleum industry.

This model has the following fit coefficients: CMIN/DF= 1.96, where the CMIN value shown in AMOS is equal to the chi-square value divided by the number of degrees of freedom (DF), RMSEA = 0.069, GFI = 0.99, AGFI = 0.95, NFI = 0.99, CFI= 0.99, and TLI= 0.98. In total, four structural equations explained the five causal relationships (paths)which exist betweenthe four retained TT enabling and outcome factors. A summary of the developed structural equations, path coefficients,and significance levels is provided in Table 12. The following section discusses the practical implications of each structuralequation and its associated predictor variables.

6.7. SEM discussion

SEM allows for the combining of a structural and theoretic model with a measurement model. SEM is an extension ofseveral statistical techniques that have been developed, most notably multiple regression and factor analysis [74].


Utilizing EFA, CFA, and path analysis, a four-factor structural model of TT was developed. A discussion on each of the fourstructural equations detailed in Table 12 will be provided as follows.• Host characteristics (ZHC): Government support was determined to be the only predictor of host characteristics.

Specifically, if the host government creates an environment encouraging TT processes they will be more likely to layout the regulations that stimulate local subcontractors with the necessary communication skills and organizing researchand development institutes to provide the necessary TT processes challenge setup.

• Technology learning capability (ZTLC): The results confirm that host characteristics and government support can directlypromote enhanced technology learning capability. Achieving success when transferring highly complex technology willbe more likely to occur when the host has a positive attitude toward working with foreigners, a strong knowledgebase of petroleum technology, and their technical and managerial training practices stimulate the technology transferprocess. Additionally, the transferee must have a sufficient supervision and capability to utilize available technology.The government regulations of host countries, such as laws governing the petroleum industry, tax exemptions,and cooperation agreement, could strongly encourage foreign companies to participate in a TT process. Moreover,governments may also help to promote solid relationships by encouraging only those foreign companies that have asatisfactory TT reputation.

• Technology transfer performance (ZTTP): The results confirm that government support is essential for achieving outcomesfrom the TT process. Governments in several developing countries are currently encouraging TT initiatives in an attemptto improve their industries, living standards, and economic prospects. This objective could only be achieved if hostemployees andprofessionals performat a higher level and becomemore competitive locallywithin petroleum-producingcountries and the international petroleummarket, eventually becoming a competitor of the foreign companies. Host andforeign companies with idealistic characteristics for TT need to be carefully selected to ensure that the host nation hasthe best chance for receiving the most tacit and implicit knowledge from the process. Moreover, mutual trust developedthrough effective communication and understanding between the transferee and transferorwill greatly enhance the hostfirms’ knowledge advancement, working practices, and overall performance over the long term. It should be noted thatthe developed structural equations might not be as accurate when applied in another context (i.e., path coefficients maychange when modeling TT initiatives in another country’s petroleum industry). Whilst the causal relationships shouldhold true in these different settings, the strength of the relationship will depend on the maturity of the host nationand its petroleum industry, in relation to the development scale. Nonetheless, the identified path equations can be usedas a tool by governments and petroleum companies for developing and newly oil-producing countries to monitor theTT process and its generated outcomes for the host sector. Additionally, the core constructs or factors of the modelwere generically named for easy adaptation and utilization in other industry sectors. With some minor modificationsto the derived TT enabling and outcome subfactors presented herein, the developed TT model could be applied to a widerange of industrial settings. The results confirm that appropriate host characteristics are essential for technology learningcapability. Foreign companies that have experience working with foreigners, a strong knowledge base and are willing totransfer their knowledge will create robust bonds with local workers, which are based on mutual trust, communicationand understanding.

7. Discussion and conclusions

As indicated before, two variableswith high factor loadings (i.e.,management and reward system)were removed throughthe EFA process along with two variables (culture and experience) that were removed by data screening. Moreover, CFAdid not corroborate the inclusion of variables representing the technology infrastructure and the degree of exposure totechnology itself. Future research should address the limitations of this research and include factors, with a number ofspecific variables, which focus on the impact of technology characteristics, technology infrastructure, degree of exposure,management, and reward system on the TT process and its outcomes. First, it could be argued that if the technology beingtransferred is significantly more advanced than the current working practices of the host employees they may not properlyunderstand howandwhy itwas implemented, and thus theywill be unlikely to embrace it on future technology acquisitions.Second, the technology infrastructure available for the TT process can influence the degree to which TT performs. It could beargued that joint ventures are one of the better vehicles for achieving higher rates of technology diffusion to the host sectorbecause they typically imply a shared management approach. Third, having compatible culture may play a role in achievingeffective outcomes from the TT process. Considerable cultural differences could potentially have an impact on a transferor’swillingness to implement TT initiatives, which will in turn can create barriers to achieving harmonious relationships.Moreover, culturally blind leadership, where no attention is paid to cultural differences and indigenous approaches, maycause conflicts resulting in the disintegration of teamwork. Lastly, embedding training into the project schedule could alsobe considered as a key enabler in the TT process. Implementing training sessions into TT agreements should not only fostermutual trust, communication, and information sharing between the transferor and transferee, butwillmore rapidly advancelocal employees’ knowledge at the operational, functional, andmanagement levels. Certainly, TT programs that are formallyplanned and managed (i.e., training times allocated, supervision specified, etc.) are more likely to transfer a greater degreeof knowledge to the host workers.

The implications for the petroleum industry in developing countries, government departments,and industries, suchas petroleum, manufacturing and construction, are gaining benefits and competitive advantages from the successful


implementation of TT initiatives. Encouraging such TT initiatives is the first step in efficiently and effectively transformingor re-engineering traditional petroleum business processes, and ultimately improving the productivity of the domesticpetroleum industry. However, it is not enough to only expect that TT will naturally occur. The processes that underpinTT should be continuously evaluated to ensure that knowledge and indigenous workers are seamlessly absorbing skills. Thisresearch study has implications for the petroleum industry of developing and newly oil-producing countries attemptingto develop and promote an effective TT process in the petroleum industry. The derived international TT model could beutilized to assist government officers in developing countries to enhance the evaluation of TT performance. Specifically, NOC-ownedpetroleumcompanies’managerswill be interested in the significant pathways to achieving value from the TTprocess.Understanding the dynamics of such pathways will assist them to better structure TT arrangements and concentrate on themost empowering enablers. This study provides evidence that when petroleum industries incorporating TT are establishedthere must be careful selection of both transferee and transferor companies. Companies with appropriate characteristics forTT will form solid bonds that are based on trust, understanding, and communication. Thus, it is essential that a substantialinvestment is provided for workshops and other technology learning capability activities to create these bonds as earlyas possible in the petroleum industry. In essence, speeding up the TT process is the key to rapidly enhancing industrycapacity and competitiveness. The model is especially important for publicly funded petroleum infrastructures, where thegovernment is concerned that advanced technologies are being willingly and effectively transferred to local petroleumemployees and professionals. Moreover, the model could assist national economic councils in planning; they would want tohave tools to better monitor the performance of the TT process when they set up road maps for the country to developthe necessary infrastructure for the petroleum industry. One of the primary objectives of these planning councils is toactively encourage domestic industry in developing countries to improve the knowledge levels of their employees as wellas increasing the industry capacity, ultimately leading to improved standards of living for all indigenous people. Finally, theauthors suggest that government and the petroleum industry companies in developing countries should seriously start toinvest funds into further developing acquired petroleum technology andmanagement knowledge. Such funding will ensurethat knowledge is perpetually building in domestic companies, diminishing the degree of reliance on foreign firms.

In conclusion, a path model was created to help both researchers and practitioners to understand the TT process in thepetroleum industry. Themain emphasis of the developedmodelwas to assess TT performance in the petroleum industry. Themodel provided an evident design on main variables influencing TT issues. The structural model consisted of four factorsand five paths, representing the interrelationships between the four enabling factors and one outcome factor. Positively,EFA and CFA empirically validated that factors referring to technology learning capability, technology characteristics, andtechnology support could be incorporated to evaluate the transfer performance. However, variables such as degree ofexposure, management, and culture were dropped by factor analysis from the model, and they should be investigated infuture research. With suitable assumptions, the findings of this study and the developed TT path model have essentialimplications for governments, policy makers, and Libyan petroleum companies seeking to enhance rates of transfer. Theapplication of the developed path model to TT in other industries is recommended.

Acknowledgments

The researcherswould like to thank all the respondentswho participated in the questioner survey. The authors also thankthe Institute of Advanced Technology (ITMA), University PutraMalaysia for their cooperation and the facilities provided. Thissupport is gratefully acknowledged.

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Modeling the technology transfer process in the petroleum industry: Evidence from Libya

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