Linking Business Modelling to Socio-Technical System Design

The SETsquared Model of University Technology
Transfer
作者： 郭东波
作者机构： 威海火炬高新区科技局,山东威海264209
出版物刊名： 全球科技经济瞭望
页码： 14-18页
年卷期： 2013年 第4期
主题词： 英国 南安普顿大学 SETsquared模式 技术转移
摘要：英国南安普敦大学关注学术研究的社会与经济影响，其在产学研结合方面的实践颇具特色。

通过简要介绍南安普敦大学研发与技术转移工作的特点，着重介绍南安普敦大学如何通过加入SETsquared联盟为衍生公司和技术型公司的成长提供扶持，以及如何利用大学优势与企业开展合作等，在此基础上分析了SETsqu＆red模式对我国的借鉴意义，以期推动大学科研成果有效商业化，逐步建立起符合我国国情的校企共赢的技术转移模式。

Technology acceptance:a meta-analysis of the TAM:Part 2Shumaila Y.Yousafzai,Gordon R.Foxall and John G.PallisterCardiff Business School,Cardiff University,Cardiff,UKAbstractPurpose –This paper is the second of two concerned with a meta-analysis of the technology acceptance model (TAM).This part aims to present a rigorous and quantitative meta-analytic review of 569findings from 95TAM studies as a basis for identifying gaps and providing guidelines for implementation management and conduct of future research.The paper also seeks to investigate the potential impact of methodological characteristics on the meta-analytic findings.Design/methodology/approach –The approach consists of meta-analysis following Hedges and Olkin’s procedures,moderator-analysis using homogeneity Q -values,analogue to ANOVA and weighted regression method.Findings –The dominant focus in empirical investigations of the TAM has been on modelling intention for its effect on self-reported usage behaviour,while the attitudinal construct has been neglected.This raises three questions:whether the exclusion of attitude from the TAM is beneficial for understanding of technology usage behaviour in mandatory settings;whether the revised TAM holds equally for mandatory and voluntary settings;and whether the emphasis on measuring intentions and self-report use rather than actual usage is warranted.An additional question answered in the meta-analysis is about the relative importance of PU and PEOU.Originality/value –The paper provides a rigorous meta-analysis to progress towards a unified view of the TAM.Keywords Technology-led strategy,Research,User studies,Modelling Paper type General reviewMeta-analysis is a technique that allows quantitative accumulation and analysis of descriptive statistics across studies without acquiring access to the original data.Meta-analysis has been criticized as “mixing oranges and apples”(Hunt,1997,p.61).Such a criticism assumes that meta-analysis aggregates findings of different phenomena.However,Cooper (1989)notes that a convergence or “triangulation”of findings from methodologically varying studies lends credence to the validity of an effect.When a relationship remains constant,although tested under a variety of circumstances,it is clearly robust.Coined and first applied by Glass (1976),meta-analysis received its impetus from Rosenthal (1984),Hunter et al.(1982)and Hedges and Olkin (1985).Although the techniques used may vary,all meta-analyses aim to derive a quantitative measure,the effect size (ES),of the relationship under study.Our meta-analyses were conducted using Hedges and Olkin’s (1985)procedures.The framework guiding our discussion and empirical investigation focuses on two types of empirically tested relationship.First are the relationships proposed in Davis et al.’s (1989)original model;second,the relationships that have been tested by later research.Subsequent discussion centres on examining the sign of the association thatThe current issue and full text archive of this journal is available at /1746-5664.htmTechnology acceptance281Received May 2007Revised June 2007Accepted July 2007Journal of Modelling in ManagementVol.2No.3,2007pp.281-304q Emerald Group Publishing Limited1746-5664DOI 10.1108/17465660710834462will emerge on average.Our work attempts to determine not only the overall ES for the technology acceptance model’s (TAM)relationships;but it also provides insight into methodological issues that are useful in developing guidelines for directing future research in terms of choice of subjects,research settings,measurement approaches,and the type of technology to be tested.Moreover,the research findings are corrected for sampling error to provide a more accurate picture of the relationships among the TAM’s variables.Of more important foci in the meta-analysis are the estimates of the magnitude of the particular relationships and the identification of factors accounting for the variance in reported ESs.Literature searchFollowing Hedges and Olkin (1985),we defined our task as combining the results from different studies to form a database to assess the existence of the relationship between the TAM’s constructs.The first step in this process was to delineate a criterion for including studies in review.Candidates for inclusion were published journal articles that empirically tested at least one relationship embedded in the original Davis et al.(1989)model.Rosenthal (1994)suggested that an effort must be made to identify all the potential candidate articles to avoid the biased retrieval of searching only main journals,which may selectively publish only the results characterized by lower p -values and larger ESs.We therefore did not limit our search to specific journals;instead all potential published journal articles were reviewed.The exclusion of unpublished studies and dissertations may also bias the analyses (Rosenthal,1994).We address this concern later in the paper by reporting the fail safe N ,which predicts how many unpublished studies with non-significant ESs would be required to reduce the observed average ES to zero.A computer search was made through nine databases including ABI Inform,Academic Search Premier,Business Source Premier,Computer and Information Systems Abstracts,ERIC,Lexis-Nexis’Academic Universe,PsycINFO,Social Science Abstract,and SocioAbs.Furthermore,a manual search was carried out through likely MIS,psychology,marketing and management journals.Keywords including but not limited to “TAM”“technology acceptance”“perceived ease of use”“perceived usefulness”“usage behaviour”“behavioural intentions”and “Davis et al.(1989)”were used to identify potentially relevant articles.Once the articles were acquired their references were reviewed to assist in locating additional articles.At this stage of the literature search,the decision rules were intentionally biased in a Type-I direction.That is,we were much more likely to include an article that was not codable than to fail to include an article that was relevant and codable.Table I provides a list of 57journals that contributed 145articles published on the TAM.Meta-analytic methodsAfter collecting the studies on the TAM,the next step was to select a measure of association between the TAM constructs that would permit the greatest number of effects to be included in the meta-analysis.The Pearson correlation coefficient,r ,was used as the primary ES estimator because most of TAM studies reported the value of r ,and it is the metric to which many the TAM findings can be converted (Wolf,1986,p.35).Moreover,Pearson’s r is more simply interpreted in terms of practical importance than are the usual d -type indices such as Hedges’s g and Cohen’s dJM22,3282(Glass et al.,1981).Not all of the empirical studies,however,reported r or other measures that could be converted to r .We therefore corresponded with the authors to obtain correlation matrices for TAM constructs.Where studies have used multiple independent samples (Igbaria,1993),were repeated over time (Davis et al.,1989),or were testing multiple technologies (Subramanian,1994),multiple correlations were considered only if each correlation represented a unique combination of sample,technology and time.Otherwise,we acted in accordance with Hunter and Schmidt’s (1990)recommendations for conceptual replication;specifically,the multiple measures were used when they were highly correlated and seemed individually to exhibit construct validity (Hunter and Schmidt,1990,p.457).When several articles were based on one data set and measured the same variables (Chau and Hu,2002a,b),only one publication was included.From the pool of 145studies,eventually 95were used in the meta-analysis,contributing a total of 569correlations.To ensure the quality of the correlation values and the methods and measures associated with each correlation,the studies were independently read and coded by four researchers (three authors and a research assistant)on pre-designed coding sheets.Disagreements were resolved through discussion after a review of each paper by the group with initial unanimity occurring for 97per cent of the papers.After coding r ,the methods,and measures for each study,the third step was to average the r that tested the same hypothesis.Rosenthal (1984)notes that as theAustralian Journal of Information Systems 1Journal of Operation Management1Automation in Construction 1Journal of puting and E-Commerce 1Accounting Management &IT1Journal of Travel Research 1Behaviour &Information Technology 5Journal of Retailing1Computers &Education1Journal of Retailing &Consumer Services 1Computers in Human Behaviour 2IEEE Software1The Database for Advances in IS 4IEEE Transactions on Software Engineering 1Decision Sciences9IEEE Transactions on Engg.Management 3Decision Support Systems4Industrial Management &Data Systems 1Educational Technology &Society1Information &Management15Electronic Commerce Research &Applications 1Information Resources Management Journal 1European Journal of Information Systems 3Information Systems Journal 1European Journal of Operational Research 1Information Systems Research9Expert Systems with Applications 1Information &Software Technology 2Group Decisions &Negotiations 1Information Technology &People 1Human System Management1Interacting with Computers1Journal of Academy of Marketing Science 1Int’l Journal of Electronic Commerce3Journal of Accounting and Computers 1Int’l Journal of Human-Computer Studies 8Journal of Applied Social Psychology 1Int’l Journal of Information Management 4Journal of the Association for the IS1Int’l Journal of Man-Machine Studies1Journal of Business &Industrial Marketing 1Int’l Journal of Service Industry Management 1Journal of Computer Assisted Learning 1Internet Research 1Journal of Computer Information Systems 2Management Science 4Journal of End-User Computing 2MIS Quarterly 13Journal of Euromarketing1OMEGA5Journal of Global Information Management 1Psychology &Marketing 1Journal of Information Technology 3Small Group Research 1Journal of IT Theory &Application1Telematics and Infomatics 1Journal of Management Information Systems 8Table I.Journal sources for TAMstudiesTechnology acceptance283population value of r gets further and further from zero the distribution of r ’s sampled from that population becomes more and more skewed,thus complicating the comparison and combination of r ’s.To avoid this complication,we followed the procedure suggested by Hedges and Olkin (1985)according to whose procedure each r was first transformed to its corresponding Z-statistic Z i ,using Fisher’s r to Z r transformation and a weighted average of Z r -scores then calculated.The weighted mean Z -transformed r is reported as ES in the tables.The 95per cent confidence intervals for each correlation were generated using standard errors of the weighted mean ES.Confidence intervals reflect the “extent to which sampling error remains in the estimate of a mean effect size”(Whitener,1990,p.316).Once the confidence interval was established,the corresponding r -indexes for the weighted average ES were retrieved back using the inverse of Z r -transformation (Hedges and Olkin,1985).Cohen (1977),in describing ES as the degree to which the null hypothesis of no relationship between the independent and dependent variable is false,suggests that an ES of r ¼0.10constitutes a small effect,an ES of r ¼0.30,a medium effect,and an ES r ¼0.50,a large effect.The formulas used for the above calculations are reported in Appendix 1.Meta-analytic findingsThe analysis of the data and the reporting of findings proceed in three phases.First,we describe the correlations between each pair-wise relationship in terms of their range,direction,statistical significance,and number of studies testing the relationship.These data highlight the diversity and nature of the findings on the TAM.Second,we present the findings from the univariate analysis of the correlations.The purpose here is to offer insight into the central tendencies of the TAM’s relationships.The final stage of data-analysis centres on assessing whether the moderators identified in this study can account for the preponderance of variance in the relationships studied.Descriptive analysisFor each pair-wise relationship,Table II reports the total number of studies,number of correlations r derived from those studies,the cumulative sample size for reported correlations,range of r ,and the percentage of positive,negative and non-significant correlations.The data in Table II make apparent the diversity in the TAM’s relationships reported in the literature.The data reveal that the range of reported values can be quite broad for certain correlates in the model.As examples,the correlations for intentions-usage range from 20.197to 0.73,for PU-usage from 20.41to 0.91,for PEOU-usage from 20.197to 0.98,and for PEOU-intentions the range was from 20.54to 0.78.In addition,the correlations reported for the same correlate often contain positive and negative correlations as well as correlations that are statistically significant in the face of other correlations that are not significant.Although most of the correlations have signs that are consistent with prevailing expectations,we do find instances where the disparity in direction and statistical significance is notable.For example,for the PEOU-usage link,we find that 59per cent of the correlations are positive and statistically significant,another 5per cent of the correlations are negative,and the remaining 36per cent of the correlations are not statistically significant in either direction.Table I in Part 1of this study also suggest that PU is mostly found toJM22,3284R e s u l t s a c c u m u l a t e d f r o m T a b l e I (P a r t 1o f t h i s s t u d y )R e s u l t s a c c u m u l a t e d f r o m m e t a -a n a l y s i s P o s i t i v e c o r r e l a t i o n s N e g a t i v e c o r r e l a t i o n s N o n -s i g n i fic a n t c o r r e l a t i o n sT A M ’s r e l a t i o n s h i pT o t a l n u m b e r o f c o r r e l a t i o n s C u m u l a t i v e (N )R a n g e o f r (v a l u e s )T o t a l n o o fs t u d i e s N u m b e r P e r c e n t a g e N u m b e r P e r c e n t a g e N u m b e r P e r c e n t a g eI n t e n t i o n s !u s a g e286,05920.19t o 0.7325239201040104A t t i t u d e !u s a g e 154,81620.42t o 0.5314117901070214P U !u s a g e 7214,38720.41t o 0.9172598202031115P E O U !u s a g e 6111,45620.197t o 0.9859355903052136A t t i t u d e !i n t e n t i o n s 308,2400.06t o 0.87444091––0409P U !i n t e n t i o n s 8717,8950.05t o 0.9189809001010809P E O U !i n t e n t i o n s 7716,51820.543t o 0.7860406701011932P U !a t t i t u d e 409,96220.23t o 0.7554529601020102P E O U !a t t i t u d e 369,0480.08t o 0.73351428201020816P E O U !P U12324,11020.26t o 0.811371158402012015Table II.Descriptive information on TAM relationshipsTechnology acceptance285be a significant determinant of usage (82per cent of studies),intentions (90per cent of studies),and attitude (96per cent of studies).On the contrary,the findings on PEOU are mixed.About 59per cent of the studies in Table I of Part 1found PEOU to be a significant determinant of usage,67per cent for intentions,and 82per cent for attitude.The data in Table II further indicate that far less attention in the empirical literature has been devoted to understanding the antecedents of attitude and its role in predicting intentions and usage behaviour.Only 14studies tested the attitude-usage relationship,in contrast with 72studies for the PU-usage,59for the PEOU-usage,and 25studies for the intention-usage link.Only 13per cent (76)of the correlations in our database pertain to attitude,as compared to 34per cent (194)for intentions,31per cent (176)for usage,and 22per cent (123)for PU.Moreover,the relationship between PEOU and PU is the most tested relationship (137studies)in the TAM.In all,these data bear witness to both the supremacy of the TAM and the mixed evidence on the drivers of behaviour towards technology use.Simultaneously,the data raise questions regarding the central tendency of the relationships and the statistical significance of these associations.They also raise questions as to whether the apparent variance in the magnitude and statistician significance of the reported correlations results from chance,sampling error,or differences in measures or methods.These questions are addressed below.Analysis of direct effectThe Fisher’s Z -transformed sample size weighted mean (ES)is the focus throughout the meta-analysis under the assumption that,all else being equal,correlations from larger sample (central limit theorem)produce a mean correlation closer to the population mean (Hunter and Schmidt,1990).The data in Table III suggests that the weighted mean ESs for the pair-wise relationships are all positive and statistically significant as suggested in the original Davis et al.(1989)model.Furthermore,the mean ES differs significantly from zero to the extent that hundreds to thousand of null effects would have to reside in the file drawers of the researchers to bring the respective mean ES estimate down to a level not considered statistically significant (see fail safe N in Table III).The only negative fail safe N fs.05(210)is reported for the relationship between attitude-usage,concurring with the discussion in the previous section according to which very little attention has been given to the understanding of the attitude construct in the TAM.The confidence intervals reported in Table III do not include zero,thereby validating the relative precision of the estimate of mean ESs of the population of studies from which they are drawn.Table IV presents the corresponding r -indexes for the weighted average ES that were retrieved back using the inverse of Z r transformation (Hedges and Olkin,1985).In Table IV,intention has the strongest correlation with usage,0.43,followed by a PU-usage correlation of 0.38.In contrast,the mean correlations between PEOU-usage and attitude-usage are 0.28,and 0.25,respectively.Regarding the mean correlation between PU-usage,215null effects would have to be hidden in researchers’file drawers for the mean correlation to be non-significant in a statistical sense.However,caution is advised when interpreting the statistical significance of the mean correlation between attitude-usage and intentions-usage.Few correlations are available in the literature toJM22,3286T A M ’s r e l a t i o n s h i pW e i g h t e d m e a n E SF a i l s a f e N aN f s .05S t a n d a r d e r r o r o f t h e m e a n E S S E E S (z -t e s t )C o n fid e n c e i n t e r v a l (95%)H o m o g e n e i t y Q -v a l u e b (d f )T o t a l v a r i a n c e S 2rV a r i a n c e d u e t o s a m p l i n g e r r o r S 2e r(p e r c e n t a g e o f S 2r )B i a s e d p o p u l a t i o n v a r i a n c e S 2b ¼S 2r 2S 2e r (p e r c e n t a g e o f S 2r )I n t e n t i o n s !u s a g e 0.46*190.013(35.38)0.49,m .0.44315.95**(27)0.050.003(5.8)0.047(94)A t t i t u d e !u s a g e 0.26*2100.014(18.57)0.29,m .0.23167.07**(14)0.040.003(7.5)0.037(92.5)P U !u s a g e 0.40*2150.008(50.00)0.42,m .0.38608.45**(71)0.040.004(10)0.036(90)P E O U !u s a g e 0.29*760.009(30.80)0.31,m .0.27586.10**(61)0.050.004(8)0.046(80)A t t i t u d e !i n t e n t i o n s 0.56*870.011(50.91)0.58,m .0.54572.78**(29)0.070.002(2.8)0.068(97)P U !i n t e n t i o n s 0.55*9240.008(72.63)0.56,m .0.541,192.04**(86)0.070.002(2.8)0.068(97)P E O U !i n t e n t i o n s 0.34*1750.008(42.50)0.36,m .0.32843.95**(76)0.050.004(8)0.046(92)P U !a t t i t u d e 0.53*1120.010(53.00)0.55,m .0.51401.90**(39)0.040.002(5)0.038(95)P E O U !a t t i t u d e 0.45*450.010(45.00)0.47,m .0.43361.92**(35)0.040.003(7.5)0.037(92.5)P E O U !P U0.44*10440.006(67.80)0.45,m .0.431,840.64**(122)0.080.003(3.8)0.077(96.25)N o t e s :*S t a t i s t i c a l l y s i g n i fic a n t a t p ¼0.05a s t h e v a l u e o f z -t e s t e x c e e d s t h e c r i t i c a l v a l u e o f 1.96a t p ¼0.05;**s t a t i s t i c a l l y s i g n i fic a n t a t a ¼0.05;a f a i l s a f e N r e p r e s e n t s t h e n u m b e r o f u n l o c a t e d s t u d i e s a v e r a g i n g n u l l r e s u l t s (r ¼0)t h a t w o u l d h a v e t o e x i s t t o b r i n g t h e a d j u s t e d m e a n d o w n t o t h e j u s t s i g n i fic a n t l e v e l (p ¼0.5);b t h e Q -v a l u e h a s a X 2d i s t r i b u t i o n w i t h k 21d e g r e e o f f r e e d o m (d f ),w h e r e k i s t h e n u m b e r o f E S sTable III.Meta-analytic results forTAM relationshipsTechnology acceptance287report on these association and so a few studies reporting different ESs in the future could alter the conclusions.The correlational data in Table IV support positive relationships of attitude,PU,and PEOU with intentions.In fact,the mean correlations between attitude-intention (r ¼0.51)and PU-intentions (r ¼0.50)are the strongest correlations reported.The data in Table IV further reveal that the perceptions of usefulness and ease of use plays a great role in developing positive attitude towards the technology.The mean correlation between PU-attitude is 0.48,and the mean correlation between PEOU-attitude is 0.42.More than 1,000presently unknown null effects would have to exist for the mean correlation between PEOU-PU (0.41)to be statistically non-significant.Analysis of moderator effectsBesides,documenting the distributions,central tendencies,and the magnitude of the TAM correlations,this meta-analysis explores whether the variations in the magnitude of the correlations is due to chance or the measurement and method factors discussed previously.We conducted two procedures to test the hypothesis of homogeneity.First,the homogeneity Q -values reported in Column 6of Table III are greater than the critical value for a chi-square (x 2)distribution with k 21degrees of freedom (k is the total number of ESs)at a ¼0.05,and thus are statistically significant.A significant Q rejects the null hypothesis of homogeneity and indicates that the variability among the ESs is greater than what is likely to have resulted from subject-level sampling error alone,and therefore,each ES does not estimate a common population mean (Lipsey and Wilson,2001,p.117).Second,we applied the rule of thumb provided by Hunter and Schmidt (1990)in which the total variance for the weighted mean ES S 2r and the associated error variance S 2er were compared.All the error variances reported in Column 8of Table III account for less than 75per cent of the total uncorrected variance,suggesting that there are important differences in variance among studies that have some source other than subject-level sampling error,likely due to moderating variables associated with different study characteristics (Hunter and Schmidt,1990).Other artifacts (i.e.variations in measurement error,range restrictions,computational errors,and so on)will account for some of the remaining variance.Therefore,we conclude that the excess variability can be explained by possible moderating variables that systematically differentiate studies with larger or smaller ESs.To assess the relationship between ES and the proposed moderators we assumed a fixed effect model and two methods were used:Hedges’(1982)analogue to the analysis of variance (ANOVA)and Hedges and Olkin’s (1985)modified weighted multiple regression.Usage Intention AttitudePerceived usefulness Perceived ease of useUsage 1.00Intention 0.43 1.00Attitude0.250.51 1.00Perceived usefulness 0.380.500.48 1.00Perceived ease of use0.280.330.420.411.00Note:*The corresponding r -indexes for the weighted average ES are retrieved back using the inverse of Z r transformation (Hedges and Olkin,1985)Table IV.Correlation matrix for the constructs of TAM *JM22,3288The analogue to ANOVA provides a method of testing the ability of a single categorical variable,such as subject type,to explain variability in a distribution of ESs. This procedure partitions the total variability into the portion explained by the categorical variable(Q B)and the residual pooled within groups portion(Q W).If significant variability is explained by the categorical variable(a significant Q B)then the mean ESs across categories differ by more than sampling error,i.e.show a statistically significant difference.If Q W is not statistically significant,the categorical variable represented in Q B is sufficient to account for excess variability in the ES distribution(Lipsey and Wilson,2001).The data in Table V indicate that all moderator variables discriminate between subclasses(except the values in italics)for the TAM’s relationships,i.e.significant Q B values.However,the fact that all Q w values reported in Table V are statistically significant suggests the need for additional moderator variables.We were also interested in further exploring the relationship between ESs and moderator variables and to test several moderator variables simultaneously.This was done using a modified weighted least square regression by specifying the inverse variance weight as the weight,Fisher Z-transformed values of the correlations as the dependent variable and the dummy-coded methods and measurement factors as independent variables(Hedges and Olkin,1985).We estimated separate regression models for each pair-wise relationship having15or more correlations.Thefindings from the regression analysis reported in Table VI indicate that the regression models are relatively free of collinearity.The maximum variance inflation factor(max VIF) values are well below the threshold value of ten(except for the attitude-usage model), suggesting that collinearity is unduly influencing the estimates of the regression coefficients(Neter et al.,1989).The data in Table VI further reveal that the proposed moderators account for a significant proportion of the variance in the correlations for the following models: attitude-usage,attitude-intentions,PU-intentions,PEOU-intentions,PEOU-attitude, and PEOU-PU.However,the proposed moderators fail to account for a significant proportion of the variance in the correlations for the intentions-usage,PU-usage, PEOU-usage,and for the PU-attitude model.The respective models are not statistically significant(see Model p level in Table VI,i.e.p.0.05).A focus on the coefficients in the statistically significant models reveals that subject type,method type,technology type,and type of usage measured are statistically significant(p.0.05)moderators of the relationships in the TAM.Subject type.Ourfindings concerning the impact of subject type on the relationships between the TAM’s constructs indicate that subject type does moderate(Table V: significant Q B)the estimates of intentions(except for PEOU-intention;D ES¼0.00) and attitude but fail to discriminate(non-significant Q B)for determinants of usage (except for attitude-usage)and PU.Tables V and VI further indicate that using student samples results in higher correlation on average when attitude is correlated with intentions(b¼0.03;D ES s-ns¼0.33);PU is correlated with intentions(b¼0.24;D ES s-ns¼0.10);and PEOU is correlated with attitude(b¼0.18;D ES s-ns¼0.14).The results also suggest that student samples yield lower correlation on average when attitude is correlated with usage(b¼20.06;D ES s-ns¼20.12)and PU is correlated with attitude (b¼20.03;D ES s-ns¼20.11).Technology acceptance289T A M ’s r e l a t i o n s h i p sM o d e r a t o r U s a g e B e h a v i o u r a l i n t e n t i o n s A t t i t u d e P e r c e i v e d u s e f u l n e s s V a r i a b l e B I !UA !U P U !U P E O U !UA !B I P U !B I P E O U !B I P U !A P E O U !A P E O U !P US u b j e c t t y p e Q :s t u d e n t (d f ),n o n -s t u d e n t (d f )313.05(12),257.58(14)99.96(7),50.31(6)387.27(41),221.02(29)102.70(24),481.92(35)66.64(9),410.193(19)449.94(36),685.57(49)636.83(34),207.09(41)115.47(16),267.48(22)60.13(15),270.15(19)1,076.60(54),755.89(67)Q W (d f )313.05*(26)150.27*(13)608.31*(70)584.62*(59)476.83*(29)1,135.51*(86)843.92*(76)382.95*(39)330.28*(35)1,832.49*(122)Q B (d f )2.89(1)16.8*(1)0.14(1)1.48(1)95.95*(1)56.54*(1)0.005(1)18.9*(1)31.64*(1)8.15(1)E S :s t u d e n t ,n o n -s t u d e n t 0.50,0.450.19,.310.39,.400.31,0.280.84,.510.62,0.520.34,0.340.45,0.560.56,0.420.42,0.46M e t h o d t y p e Q :l a b s t u d y (d f ),fie l d s t u d y (d f )49.56(10),255.193(16)46.02(3),61.90(10)185.93(19),416.15(51)29.13(18),529.88(41)14.95(5),416.62(23)172.82(23),1,002.30(62)366.83(23),420.06(52)95.39(9),290.810(29)27.20(9),311.79(25)748.48(35),1,017.996(86)Q W (d f )304.75*(26)107.92*(13)602.08*(70)559.01*(60)431.58*(29)1,175.42*(86)786.89*(76)386.18*(39)338.99*(35)1,766.47*(122)Q B (d f )11.197*(1)59.15*(1)6.37(1)27.09*(1)141.20*(1)16.92*(1)57.06(1)15.72*(1)22.93*(1)74.17*(1)E S :l a b s t u d y ,fie l d s t u d y 0.53,0.430.05N S ,0.310.35,0.400.19,0.310.99,0.520.49,0.560.24,0.370.42,0.540.58,0.430.33,0.47T e c h n o l o g y t y p e Q :t y p e -1(d f ),t y p e -2(d f ),7.68(4),251.15(14)18.73(2),14.86(7)103.88(11),251.60(35)12.41(11),160.78(24)n /a ,244.1(12)18.94(3),430.93(34)2.25(3),670.42(32)37.83(4),129.67(13)40.26(4),113.21(10)1,142.32(45),62(10)t y p e -3(d f ),t y p e -4(d f )19.82(4),23.44(2)10.11(1),3.22(1)158.84(40),88.88(12)23.82(9),371.18(13).45(5),196.81(9)66.97(14),557.33(32)45.39(11),74.40(27)16.42(7),111.28(12)18.26(6),47.89(12)227.15(24),272.65(40)Q W (d f )302.09*(25)180.61*(13)603.19*(68)568.20*(57)441.36*(29)1,074.17*(83)792.40*(73)295.2*(36)219.63*(32)1,704.13*(119)Q B (d f )13.86(3)213.54(3)5.26(3)17.9*(3)131.42*(1)117.87*(3)51.49*(3)106.7*(3)142.29*(3)136.51*(3)(c o n t i n u e d )Table V.Analog to ANOVA results for moderator analysis (fixed effects model)JM22,3290。

Additive manufacturing:A framework for implementationStephen Mellor n,Liang Hao,David ZhangCollege of Engineering,Mathematics and Physical Science,University of Exeter,Exeter EX44QF,United Kingdoma r t i c l e i n f oArticle history:Received15April2012Accepted9July2013Available online23July2013Keywords:Additive manufacturingRapid manufacturingTechnology implementationCase studya b s t r a c tAs mass production has migrated to developing countries,European and US companies are forced to rapidlyswitch towards low volume production of more innovative,customised and sustainable products with highadded value.To compete in this turbulent environment,manufacturers have sought new fabricationtechniques to provide the necessary tools to support the need for increasedflexibility and enable economiclow volume production.One such emerging technique is Additive Manufacturing(AM).AM is a method ofmanufacture which involves the joining of materials,usually layer-upon-layer,to create objects from3Dmodel data.The benefits of this methodology include new design freedom,removal of tooling requirements,and economic low volumes.AM consists of various technologies to process versatile materials,and for manyyears its dominant application has been the manufacture of prototypes,or Rapid Prototyping.However,therecent growth in applications for direct part manufacture,or Rapid Manufacturing,has resulted in muchresearch effort focusing on development of new processes and materials.This study focuses on theimplementation process of AM and is motivated by the lack of socio-technical studies in this area.It addresses the need for existing and potential future AM project managers to have an implementationframework to guide their efforts in adopting this new and potentially disruptive technology class to producehigh value products and generate new business opportunities.Based on a review of prior works and throughqualitative case study analysis,we construct and test a normative structural model of implementation factorsrelated to AM technology,supply chain,organisation,operations and strategy.&2013Elsevier B.V.All rights reserved.1.IntroductionAdditive Manufacturing(AM)is defined as“the process ofjoining materials to make objects from3D model data,usuallylayer upon layer,as opposed to subtractive manufacturing meth-odologies,such as traditional machining”(ASTM Standard).Syno-nyms found in the literature include additive processes,additivetechniques,additive layer manufacturing,layered manufacturing,and freeform fabrication.There is now a large number of technol-ogies which employ this method of manufacture,some of themore widely used include stereolithography(SL),fused depositionmodelling(FDM),selective laser sintering(SLS)and3D printing(3DP).Since the development of many of these technologies hasoccurred simultaneously,there are various similarities as well asdistinct differences between each one(Kulkarni et al.,2000).Reviews of the numerous AM technologies have been performedin previous works(Gibson et al.,2010;Groover,2007;Hopkinsonet al.,2006).With over20years of history,in its early years AM was mostlyapplied for the fabrication of conceptual and functional prototypes,also known as Rapid Prototyping(RP).These prototypes were mostcommonly used as communication and inspection tools,producingseveral physical models in short time directly from computer solidmodels helped to shorten the production development steps(Santos et al.,2006).RP remains the dominant application ofpolymer AM processes and is well established in the market.Manyof the aforementioned technologies are limited to Rapid Prototyp-ing as they do not allow common engineering materials to beprocessed with sufficient mechanical properties(polymers,metals,ceramics,and composites thereof)(Kruth et al.,2007).The conceptof Rapid Manufacturing(RM)–“the production of end-use partsfrom additive manufacturing systems”(Hague et al.,2004)–isemerging today;though its economic impact remains modest(Levyet al.,2003).There are few-large scale applications of RM,many ofwhich are for producing personalised products in the medicalfield(Strategic Directions,2008).Ruffo et al.(2007)provided a summaryof the pitfalls which exist for companies looking at the use of RM asa solution for current manufacturing problems or wishing to takeadvantage of this emergent technology,suggesting they are con-centrated in three specific areas:manufacturing processes and materials,design,management,organisation and implementation.These issues are inter-related and this study centres on thethird of these areas,specifically focusing implementation of AMtechnologies for production applications.It is inevitable that someContents lists available at ScienceDirectjournal homepage:/locate/ijpeInt.J.Production Economics0925-5273/$-see front matter&2013Elsevier B.V.All rights reserved./10.1016/j.ijpe.2013.07.008n Corresponding author.Tel.:+447709994113.E-mail address:sm270@(S.Mellor).Int.J.Production Economics149(2014)194–201of the factors critical to the implementation of AM technologies are also important to the adoption of other manufacturing tech-nologies.However,it is not the aim of this study to rediscover these issues,rather this paper seeks to build on this,adding insights into factors that are specific,or of particular importance to AM technologies due to their unique characteristics,resource requirements,benefits and tradeoffs and so on.The remainder of the paper is organised as follows.In the next section,a short overview of AM technology and applications is presented along with an introduction technology implementation theory.Then the research framework is presented with a detailed description of the constructs and supporting literature.The data collection process is then described and the results of the framework test are described. Finally,the paper closes with conclusions,limitations of the study and suggestions for future research.2.Background2.1.AM technologies and applicationsAs previously stated the numerous AM systems share some similarities but have a number of distinctions.Thefirst AM system to be commercialised was SL,whereby a concentrated beam of ultraviolet lamp is used to solidify a liquid photopolymer by tracing a two dimensional(2D)layer in the form of a contour and then an infill.Once the beam has completed a single layer the build platform will then move downward in the z-axis,a new layer of photopolymer is distributed and the process is repeated until thefinal layer is ser sintering and laser melting processes work in a similar manner,whereby polymer or metallic powders are selectively melted in2D layers,through high power lasers until a solid part is complete.Another popular process, particularly with hobbyists,is the FDM process.In this method, materials,usually polymerfilaments are extruded through a heated nozzle to“print”2D layers successively,one on-top of another,until the part is complete.Whether through melting of metallic powders or through extrusion of polymerfilaments, all AM process share the additive principle of building compo-nents.It is possible to identify a number of key steps in the AM process sequence.Gibson et al.(2010)defines eight key steps in the generic process of CAD to part:Conceptualization and CADConversion to STLTransfer and manipulation of STLfile on AM machineMachine setupBuildPart removal and cleanupPost-processing of partApplicationHolmström et al.(2010)suggest the unique characteristics of AM production lead to the following benefits:No tooling is needed significantly reducing production ramp-up time and expense.Small production batches are feasible and economical.Possibility to quickly change design.Allows product to be optimised for function(for exampleoptimised cooling channels).Allows economical custom products(batch of one).Possibility to reduce waste.Potential for simpler supply chains;shorter lead times,lowerinventories.Design customization.These benefits have been captured in a variety of applications spanning a number of industries,and different stages of the product development life cycle.Examples include titanium aero-space parts where only10%of the raw material is required when compared to the original machined part(The Economist,2012). Atzeni and Salmi(2012)showed the economics of additive manufacturing for end-use parts through comparing the produc-tion of landing gear aircraft assemblies,through high pressure die casting(HPDC)and laser sintering.The authors showed the cost benefit at low to medium production volumes,illustrated in the breakeven analysis shown in Fig.1.The benefits of AM have been captured in the production of race car gearboxes(The Economist, 2012).AM facilitates the manufacture of smooth internal path ways,providing faster gear changes and reducing component weight by30%.Similarly,Cooper et al.(2012)illustrate the potential for improved functionality in their study on formula one technology,applying AM to hydraulic component manufac-ture gaining efficiency offluidflow of250%.As previously stated the current dominant application for AM processes remains RP.Rapid Tooling(RT)also makes up some of the current AM activity which involves the fabrication of moulds and dies.Regarding manufacturing applications of AM processes (RM),notable areas of success include the production of medical devices such as dental crowns and hearing aids,driven by customer requirements for individualised products and AM pro-cesses having the benefit of design customization.RM has also been applied to the production of consumer products,including high value lighting goods and electronics.The aerospace sector has also found a number of applications,often driven by the possibility of improving buy-to-fly ratios(as some AM processes have high material utilisation,most notable metal-based process)and redu-cing the weight of components through design optimisation (Petrovic et al.,2011).Other areas include,automotive,jewellery, architecture and defence applications.2.2.New technology implementation:theoretical backgroundSkinner(1984)was one of thefirst to propose that innovation in production technology can be used strategically as a powerful competitive weapon,suggesting that it can bring to bear many other strategic factors besides achieving low costs including, superior quality,shorter delivery cycles,lower inventories,lower investments in equipment,shorter new product development cycles and new production economics.In Porters(1985)influential work on competitive strategy he suggested technology is perhaps the most important single source of major market sharechanges Fig.1.Breakeven analysis performed by Atzeni and Salmi(2012)comparing HPDC and SLS processes.S.Mellor et al./Int.J.Production Economics149(2014)194–201195among competitors and is the prominent cause of the demise of an entrenched dominantfirm.Voss(1988)provided seminal work on proposing implementa-tion as a distinctive area of study of process innovations.The focus on AMT implementation in1990s was a direct result of many systems failing to meet their initial promise,with project man-agers unhappy with the system performance.In more recent years, the advent of ERP systems and RFID technology has generated a plethora of research articles on implementation as academics have sort to create process models and frameworks to assist managers in implementing new innovations successfully.Though AM as a manufacturing technology remains in comparatively low levels of exploitation,with AM production representing only a very small percentage of global manufacturing,some authors suggest the breakthrough is eminent.In order to take a pro-active approach to research we focus on developing an implementation framework based on current innovators in this area in order to facilitate this breakthrough.3.The research frameworkThe conceptual framework for AM implementations put for-ward by the authors is illustrated in Fig.2.The framework proposes that both external forces and internal strategy drive the consideration of AM as a method of manufacture and the approach to AM implementation will be influenced by factors which may be grouped in tofive constructs.These constructs are laid out described in the following sections:3.1.Strategic factorsThe decision to invest in Additive Manufacturing technologies must be linked to the market and product characteristics.High utilisation underpins any technology investment(Hill,2000),if the process will not be highly utilised on one product it must meet the manufacturing and business needs of other products.Authors in thefield of AM management have proposed a number of product characteristics which affect the types of products suitable to AM production.Generally,the product characteristics are as follows: Products with a degree of customisation. Products with increased functionality through design optimisation.Products of low volume.The implementation of AM must be preceded by strategic alignment of the business,manufacturing and R&D strategy.The technology benefits must be linked to the capabilities required of the manufacturing unit,capabilities derived from the business strategy,viewed as the market-pull strategy to AM implementa-tion.However,it is also proposed in line with the current resource-based view of thefirm that investment in AM may be seen as a structural investment which will build new manufactur-ing capabilities,creating new business opportunities for the enterprise,the technology-push strategy.3.2.Technological factorsIn order for the adopting organisation to gain competitive advantage from the implementation of AM its ability to link the technology benefits to the business strategy has been emphasised. The technology benefits associated with AM technologies possess a number of benefits,as previously stated in Section2.However,as shown in previous work by Sonntag(2003)it is equally important that the adoptingfirm also understands the trade-offs in using new manufacturing technology,for AM process(in general) material range remains low,machine and material costs remain high and process speeds are relatively slow.The lack of technical standards also presents a major barrier to adoption.Some of these characteristics of AM are likely due to their relative immaturity and managers should be aware of this when deciding whether to adopt.There is also an inherent RP legacy with AM system which may result in a psychological barrier to adoption,as management only see the technology-class as being suitable for RP applications.The adopting organisations ability to present benefits of AM as a manufacturing process in a clear and balanced way will determine the success of implementation.anisational factorsThe size of an organisation has been identified to be critical to the understanding of the process of implementation ofnewFig.2.The proposed framework of AM implementation.S.Mellor et al./Int.J.Production Economics149(2014)194–201196manufacturing technology.A number of scholars have suggested small business cannot be considered scaled-down larger ones,and the theories proved in large enterprises might not be suitable for small business(Federici,2009;Schubert et al.,2007;Thong et al., 1996;Welsh and White,1981).Therefore,the approach to imple-mentation for an SME is likely to be different to that in a large multinational company.Linked to size,previous study into new manufacturing technology implementation suggests that the structure of an organisation is the key factor to successfully implementing manufacturing technology(Dalton et al.,1980; Dean et al.,1992;Belassi and Fadlalla,1998;Abdul Ghani et al., 2002;Sun and Tian Cui,2007;Saberi et al.,2010),and that companies that adopt withoutfirst re-designing organisational structures and processes encounter high difficulties(Saberi et al., 2010;Millen and Sohal,1998).Therefore,it is proposed for successful implementation of AM technologies the decision to adopt will be accompanied by a change in jobs and tasks,and thus a change in work practices and structure.Many authors have advocated that new technologies challenge established norms and strategic options.Linked to structure, organisational culture defines the complex set of knowledge structures which organisation members use to perform tasks and generate social behaviour(Saberi et al.,2010).Hopkinson et al.(2006)suggest possibly the largest but unknown impact could be on company culture and how it changes to accommodate rapid manufacturing(RM).Using AM processes as a manufacturing technology requires designers and engineers to re-think design for manufacturing(DFM).DFM is any aspect of the design process in which the issues involved in manufacturing the designed object are considered explicitly with a view to influencing the design.AM requires users to match product with process and to understand new technology process capabilities.Therefore the workforce experience and skill is also proposed to be a key factor in AM implementation.3.4.Operational factorsAs proposed by Bailey(1993)a change in an organisations technology will influence both its operational and administrative structures to change.One area of operations which has been proposed by many authors to be significantly changed with the adoption of AM is product design.A number of authors have commented on the impact of Additive Manufacturing on the design of products and designers themselves(Hague et al.,2003, 2004;Mansour and Hague,2003).The additive nature of AM processes means that this type of manufacture is unconstrained by many of the limitations of conventional(subtractive or formative) processes(Hao et al.,2010).The unique characteristics of AM systems require new design tools and practices to be developed, contrary to early promise made by some researchers there is not total geometric freedom and many consideration have been taken into account when designing products for AM processes.It is proposed that the designers understanding of the new design for “additive”manufacturing constraints will be an influential factor in AM implementation approach.Another area of operations which is likely to change signifi-cantly with the adoption of AM is production planning and quality control.Research on AM process planning is still lacking,though Ciurana and Riba(Munguia et al.,2008)investigated processing planning strategies employed at36Additive Manufacturing cen-tres in Northern Spain.The authors used survey analysis,sup-ported by personal interviews with technicians,to identify strategies used AM process planning including;part orientation strategies,build volume strategies,layering strategies,support generation and minimisation.However,as the authors conceded a limitation of this work is that the majority of the activity in this study was RP and RT.Hayes and Jaikumar(1991)suggested the use of unsuitable cost accounting methods,where implementation of new technologies results in costs incurred shifting from direct labour costs to essentiallyfixed costs,often renders traditional methods focusing on less important factors unsuitable.A number of authors (Munguia et al.,2008;Grimm,2004;Hopkinson and Dickens, 2003)have identified the substantial information gap regarding the true cost of AM system,implementation and operation.In previous studies on AM costing four key cost factors have been identified for additive processes:operation times,machine costs, labour costs and material costs.Ruffo et al.(2006)provided seminal work on the cost estimation of AM processes and extended this research for simultaneous production of mixed components using laser sintering(Ruffo and Hague,2007).How-ever,for other systems such as metal based processes there remains a significant gap in this knowledge.In the context of AM no technology is currently capable of creating net shape parts,thus post-processing(such as support removal,heat treatment,etc.)are required,therefore the integra-tion of AM within a supportive production system is proposed to be key to implementation success.3.5.Supply chain factorsAdditive Manufacturing implementation lies at the intersection of two supply chains;first it involves a supply chain from the machine vendors to the purchaser of the technology.Second,the purchaser will then embed the technology in their respective supply chain and hence influence their customers and suppliers. On the subject of new process technologies implementation, Bessant(1994)has argued that for the technology to deliver its full potential significant organisational changes are required such as the restructuring of relationships with suppliers towards more collaborative forms.It is proposed that the implementation of AM technologies manufacture will require increased collaboration with suppliers and customers.Vendor support during the imple-mentation process has long been recognised as a critical factor of implementation success.It has been shown that the level of complexity of the technology innovation is directly related to the level of intensity of the user-supplier interaction processes(Zairi, 1998).Therefore vendor support is proposed to be a key factor in AM implementation.A characteristic of current AM industry worthy of note is the tendency for machine suppliers to be material suppliers(such as the powders used)following implementation.This characteristic is partly due to the immaturity of the technology(with a shortage of material suppliers)and also likely a strategy on the machine supplier's part to protect future business.There will also be decisions on where to locate manufacturing as the removal of tooling requirement may result in the possibility of distributing manufacturing according to demand locations as in theory the only inputs required for production are CAD data and raw material.4.Research methodologyDue to the exploratory nature of this research area,case studies are used to investigate the AM implementation process and test the research framework.Implementation research provides gui-dance to identify the most suitable interviewee for this research area,indentifying the four most critical constituencies as technol-ogy vendors,upper management,project engineers and plant operating and maintenance personnel(Zmud,1984;Cooper andS.Mellor et al./Int.J.Production Economics149(2014)194–201197Zmud,1990;Majchrzak,1988;Hattrup and Kozlowski,1993;Noori, 1990).A single case was chosen for this study in order to get an in-depth view of AM implementation.The case company was selected as it represents a rare case of a successful RM implemen-tation(in terms of business success),as it is a national leader in supplying both plastic and metal RM parts.pany backgroundCompany A is a leading supplier of SLS and Direct Metal Laser Sintering(DMLS)products in Europe specialising in the production of complex and functional metal rapid prototypes,aesthetic models and low volume production components to industries including Aerospace,Automotive,Dental,Medical,FMCG,Marine, Defence and Pharmaceutical.The company began thefirst steps in developing a DMLS capability four years ago.The informant at Company A was the company Chief Executive Officer(CEO)the enquiry was also supported through interviews with the machine vendor.The company was started as a specialist in polymer AM processes and focusing on producing prototypes for external customers.Following implementation of metal-based processes the focus has been on production applications due to the process costs and the reduction in overhead costs for higher volumes.The focus on the implementation of the DMLS systems was made for following reasons:the interviewee was directly involved in the implementation ofDMLS technology and may be viewed as the project champion, the interviewee regarded the metals based processes as key tofuture success,andfinally,when discussing production applications DMLS was the pro-cesses generally referred to as providing the major benefits. 5.Results and discussion5.1.AM strategyThe company has a clear mission of becoming Europe's leading supplier of SLS and DMLS parts.A number of factors have led to the implementation of DMLS at Company A.One of the main contributing factors to implementation may be viewed as the CEOs perception of technology benefits,at a stage where the technology was very much in its infancy,the CEO recognised the potential for competitive advantage through innovation and technology investment:“It was a belief as much as anything;there was no precedent to base it on….I could see that metals was going to be massive in the future….I wanted to make sure that I set us up ready for when it happened so that we were in a position of strength rather trying to catch up.”Originally a specialist in purely SLS,another factor contributing to the decision to invest in DMLS at Company A may be viewed as the changes in the RP sector.As the systems have matured,many companies have taken this capability in-house which has reduced the amount companies request from specialist suppliers,such as Company A.Though this change has had drastic effects in certain markets for the company,in many it has been less severe and although demand may have decreased,it has not disappeared.Though the company has achieved significant success the challenge for case company is that in order to maintain the business benefits of the technology it needs to prove its produc-tion capability:“….the only way we are going to make any money on this ultimately is by doing production because the overheads forproduction work are radically lower than they are for prototyp-ing……so then you have the problem,you don't want to do prototyping,but that's the only business around,you want to do production,but to do production and be good at it you've got to show you're good at doing production.So you've got a real dichotomy there.”The strategy at Company A is therefore significantly influenced by its organisational antecedents,coming from a RP background rather than a production background.The informant suggests that “it would have been better to be a machining company that adds it in rather than a rapid prototyping company that is trying to become a serious aerospace or whatever supplier”There are a number of explanations for this reasoning which influence the approach to AM implementation at Company A, including,the culture within RP(discussed in Section5.3),the company not having an established customer base(as discussed in Section5.5)and lacking production capability(particularly regard-ing the post-processing requirements in DMLS,discussed in Section5.4).Their response to this apparent inefficiency has been to become experts in“design and application development in a particular way”.Though the company has built up a workshop facility to support the DMLS system,through deliberatelyfinding and designing parts to suit the process they have been able to get round this inadequacy and as the informant suggests:“it is much more profitable to design the part where you don't do anything to the part after you havefinished,so you design it so that it doesn't need anything doing to it and if you can do that then its far more profitable….and far quicker”.Therefore,this represents the company's main in-house cap-ability.However,an important implication of this is that this does require a lot of design thought,therefore regarding productivity, downstream process may be reduced,but upstream process is increased.Regarding the current market for DMLS products,the informant suggests that it is difficult to know where the next job will come from and how the market for DMLS products will change over time.Therefore it remains a particularly turbulent environment,where both the technologies and the markets are relatively new.The informant also states that with a few exceptions,industry knowledge of the process has significantly lagged behind that of the users.Among these exceptions is the aerospace industry,with requirements for lighter aircraft and more efficient processes often influenced by environmental pany A is targeting these manufacturers as they require less education on process benefits and the components are more suited to AM production.5.2.AM technologyThe CEOs perception of AM benefit is that they must be considered over the life cycle of the product,which itself presents a challenge when attempting to educate customers on these potential opportunities.This is reflected in the company's focus on Aerospace applications.In aircraft component manufacturer, the potential to reduce part weight through the design freedom unlocked when using AM processes can create mass savings over the product life cycle.This is also reflected in the company main in-house capability,design and application development for AM process.The CEO suggests:“….in terms of material usage….particularly with the metals it's very good but you have to go through a high energy process of turning it into powder in thefirst place,do you really gain a benefit there?It's marginal to be fair.The actual machines are theyS.Mellor et al./Int.J.Production Economics149(2014)194–201 198。

PRESENTATION ATGASTECH 2015, SINGAPOREWHAT MAKES NEARSHORE MIDSCALE FLNG SO ATTRACTIVE?O c t o b e r 30, 2015Javid TalibVice President & FLNG Program DirectorBob GerminderSr. Vice President & Sr. Director, FLNGErnest Black & Nathan Veatch Building a World of Difference®i n •Founded in 1915•Global workforce of more than 10,000 •Employee-owned corporation•$3.0 billion in annual revenues in 2014 •More than 110 offices worldwide •Completed projects in more than 100 countries •Proprietary gas and oil technologiesBLACK & VEATCH – 50+ YEARS OF LNG ™Continued Development / ImprovementLate 1950s •Methane PioneerDevelopment Work1960s •Cascade Technology1970s•Development of PRICO® SMR •3 Trains in Algeria•U.S. Peakshaving Market, Numerous1990s•Development of Improved PRICO® SMR Process •Wrenshall, Alagasco, Brazil, ScotlandEarly 2000s •China - 21 Projects2008•Development ofFloating LNG Solutions2013•Developmentof Transportation LNG32014•Development of PRICO-DMR ™AGENDANearshore midscale FLNG solutionsNearshore FLNG developmentNearshore FLNG configurations developedWhat makes nearshore midscale FLNG attractive4NEARSHORE FLNG5Black & Veatch | •Export onshore pipeline gas •Alternate to building fixed assets – onshore base load plants•Shorter schedule, faster commercial operation•Small to midscale solutions •Lower CAPEX•Combine with onshore infrastructure •Solutions for jetty moored or sea island mooring•Easy redeployment •Consistent quality•Reduced risk labor availability & productivityOctober 2015NEARSHORE FLNG APPLICATIONS6Black & Veatch | Category Size HullOnboard LNG Containment System Location Offshore Near-shore with Sea-Island Dockside / Jetty /Wharf FLNG orLNG FPSO*Mid ScaleShip shapeor BargeIntegral to Hull or Conversion of Existing LNGC YESPrimarilyTurret MooredYES YESBarge LNG** Small to Mid ScaleBargeNone or IMO Type C Containers withAlongside FSUNO YES Primarily Jetty Moored * FLNG/LNG FPSO means LNG production facility with LNG integral containment system (cargo tanks) in the hull. This could include Barge with SPB tanks, LNGC conversions –Moss or membrane and Bulk Carrier ** Barge LNG means a simple barge production facility with no integral LNG containment system, but could have IMO Type C storage tanks located under the barge deck for small storage.October 2015NEARSHORE FLNG SOLUTIONSBlack & Veatch | 7•Barge•Ship Shape •ConversionOctober 2015NEARSHORE FLNG DEVELOPMENT8NEARSHORE FLNG COMPONENTS99 •Gas pipeline•Marine infrastructure (jetty, gas transfer system – loading arms etc) • Port development - dredging •Gas compression where necessary •Gas treating facility •FLNG production unit•Floating LNG storage unit (FSU) •LNG offloading•Boil-off gas (BOG) handling •Flare, utilities •Deckhouse• Power generation •AccommodationsNEARSHORE FLNG CONFIGURATION OPTION10 10NEARSHORE FLNG FLNG DEVELOPMENTEARLY CHALLENGES IN FLNG PROJECT DEVELOPMENTComplexityTechnologyProven PerformanceSafetyMarinizationNEARSHORE FLNGDEVELOPMENTFLNG PRODUCTION CAPACITY AND TRAIN SIZE •Nearshore reservoir size & life•Pipeline gas - GSA•LNG offloading strategy•Single large train vs. small multiple trains1 tcf reservoir can support 1 MTPA plant for 20 years•Economies of scales •Reduced footprint•No operational flexibility •Complex startup, turndown issues•Shutdown of any of the system or for annual maintenance will result in entire production loss •Modularization challengeLARGE SINGLE TRAIN VS SMALL MULTIPLE TRAINSLarge Single TrainsLARGE SINGLE TRAIN VS SMALL MULTIPLE TRAINSSmall Multiple Trains•May overcome loss of economies of scale with simple technology •Wider selection of fabrication yard•Increased overall footprint but easily modularized to make it space efficient •Operational flexibility – easier startup and improved turndown •Shutdown of one train still allows production from other trains•Higher availability - annual maintenance can be staggered by trains •Facilitates progressive development of the project•Allows dedicated trains for offtakersTECHNOLOGY SELECTIONFuel GasPRICO® SMR LNG PROCESSSuction DrumLNG StorageInlet TreatingFeed GasTurbine / Motor Discharge DrumMain ExchangerNGL / CondensateMixed Refrigerant Compressor Heavies Recovery Dehydration Hg Removal Interstage DrumPRICO® DRIVER VS. CAPACITYPRICO® LIQUEFACTION TRAINSMARINIZATIONProcess OperabilityMechanicalintegrity SafetyMaterialsof constructionMaterials Handling & maintenance•Vapor dispersion •Flare radiation •Depressurization •Explosion / blastanalysis•Safety gaps •Drop object analysis •LNG Spill containment •Escape routes •ESD•HAZID•HAZOP•QRASAFETYDEVELOPMENTNEARSHORE FLNGCOMMISSIONING AND STARTUP •Shipyard pre-commissioning•In yard / near yard commissioning •Availability of gas•Sail away – transit to site•Hookup at site•Commissioning at site•Turnover to operations•Roles and responsibilities•Care-custody-control“It’s never too early to start on start-up planning”DEVELOPMENTNEARSHORE FLNGDEVELOPMENTNEARSHORE FLNGFLNG STAKEHOLDERSBlack & Veatch | DEVELOPMENT OF SUCCESSFUL MIDSCALE FLNG FACILITYCommercialFrameworkBankabilityFinancingRisk AllocationDEVELOPMENTSeveral models for commercial engagement of FLNG stakeholders NEARSHORE FLNG October 2015DEVELOPMENTBlack & Veatch | NEARSHORE FLNGOctober 2015NEARSHORE FLNG CONFIGURATIONS DEVELOPED AT BLACK & VEATCHFLNG CONFIGURATIONS DEVELOPED•Barge – Small-Mid Scale•Ship shape – Mid Scale•Conversion Small to Mid ScaleBlack & Veatch is the only Technology provider and TopsidesBARGE CONFIGURATIONS•Capacity 0.5 to 2.1 MTPA•Onboard Storage 17K m3 to 36K m3•No. of Trains 1 to 4•Refrigerant Driver GT or Electric Motor •Interstage Cooling Water or Air•Power Generation Onboard or Onshore •Feed Gas Pre-treatment Onboard or OnshoreWISON – EXMAR CARIBBEAN FLNG PROJECTUNDOCKING OF CARIBBEAN FLNG NOVEMBER 19, 2014Black & Veatch | October 2015•4 X 1.1 MTPA PRICO® liquefaction modules•RR Trent 60 WLE GT driver •Fractionation moduleEXCELERATE ENERGY – 4.4 MMTPA FLSO ™PORT LAVACA BAY, TEXAS• BOG module• Refrigerant make-up skid • Power generation module • Gas treatment onshore • 340m X 62m hullGOLAR GOFLNG – MOSS LNGC CONVERSIONSHILLI, GIMI & GANDRIA•4 x 0.5 MTPA trains•Pre-treatment, liquefaction,BOG, HRSG•Pipeline gas•GE LM2500+G4turbine/compressor•BAHX•Topsides built to Class - DNVUnique moss LNGC conversionIN CONCLUSION•Export onshore pipeline gas •Monetize nearshore reserves •More options on configuration•Cost competitive solutions – low CAPEX•Option to other large onshore plants for early production/export•Bankability – “Bite size”•Flexibility in phased development while using smaller multiple trains•Mitigate lengthy permitting required for land-based facilities Black & Veatch | SO, WHY NEARSHORE MIDSCALE FLNG SO ATTRACTIVE?October 2015。