Technological Forecasting & Social Change 103 (2016) 191–202

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Technological Forecasting & Social Change

A proactive technology selection model for new technology: The case of3D IC TSV

Chih-Young Hung ⁎, Wen-Yi LeeInstitute of Management of Technology, National Chiao-Tung University, Taiwan

⁎ Corresponding author at: A709, No. 1001, Ta-Hsueh RE-mail address: cyhung@nctu.edu.tw (C.-Y. Hung).

http://dx.doi.org/10.1016/j.techfore.2015.11.0090040-1625/© 2015 Elsevier Inc. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 11 November 2014Received in revised form 8 July 2015Accepted 9 November 2015Available online xxxx

Many studies have addressed the issues of the evaluation and the selection of new technology. In this paper, weadd to this literature by formulating amore general model that incorporates the evaluation, the selection and theimprovement of new technology. The improvement of new technology refers to themodification of the develop-ment of the underlying technology.We accomplish this by combining an Importance-Score Report for each of thefactors of the new technology to the adopting firm and a Performance-Score Report of the alternative new tech-nologies' performance on each of the factors into a two-dimensional graph. This graph helps to reveal the specificfactors of the new technology for the adopting firm towork on improving. Processes such as this will be an infor-mative and powerful planning tool for decision maker when choosing among competing new technologies. Wethen use a case of selecting a 3D IC TSV technology for a Taiwanese ICmanufacturer to illustrate the application ofthe PTSM. Three critical factors of the 3D IC TSV technology were then identified for improvement.

© 2015 Elsevier Inc. All rights reserved.

Keywords:Proactive technology selectionNew technology3D IC TSV

1. Introduction

Numerous exciting new technologies in various fields have emergedin recent years, such as in nanotechnologies, biotechnologies, greenenergy, photonics, IOT, and 5Gwireless communications. New technol-ogies oftentimes have great potential to create value. However, theprocess of innovating new technologies is characterized by high degreesof uncertainties and late return, especially when the market forces are notsufficient to come to a more sustainable technological regime (Alkemadeand Suurs, 2012; Porter et al., 2002). In addition, the advance of new tech-nologies is often characterized by long development times, large initial in-vestments, and heavy involvement of entrepreneurial, government, andmarket actors. Orman (2013) stated that “a fast changing technological en-vironment introduces additional risk because of the numerous social, eco-nomic, and political opportunities it creates, and threats it engenders.”

Bakker et al. (2011) described technological development as an evo-lutionary process of variation, selection and retention. In the process,different technologies are evaluated and selected by the market andother institutions. The accompanying high uncertainties and potentialgreat benefits in the applications of new technologies entail the needfor the evaluation and selection of these technologies. Given that thefactors to be considered in the decision are getting more complicatedand harder to identify, especially, when the underlined technology isat its early-stage, selection of new technology has become one of themost challenging decisions that the management of a technology firm

d., Hsinchu 300, Taiwan.

comes across. Whether to invest in a new technology oftentimes is acritical decision that firms have to make for their survival.

The literature on R&D project evaluation and selection is abundant.The earliest study we can trace is the one by Baker and Pound (1964).The title of their study is “R&D project selection: Where we stand.”After this milestone paper, hundreds of studies in this area were pub-lished (Henriksen and Traynor, 1999). Henriksen and Traynor (1999)provided a relatively comprehensive review of fifty eight studies thatwere published between 1969 and 1995. According to their study,R&D project selection methods can usually be placed into one of eightcategories, as shown in Table 1. From unstructured peer review toformal quantitative modeling, these methods all shared one commonand difficult element in technology selection: forecasting the future.Using assessments by a group of experts has thus become the mainapproach to deal with uncertainties in most of these methods.

Since 2000, researchers shifted their focuses to obtaining a deeperunderstanding of the key factors for selecting new technologies and de-veloping algorithms that would measure the different technology op-tions against these factors so that they can be compared to facilitatethe decision-making process. A typical methodology along this line ofreasoning can be exemplified by Chan et al. (2000), Tavana (2003),andHousemanet al. (2004). Usually, themethodology of technology se-lection in this line consists of a series of activities: First, an analysis of thesituation; Second, the setting of clear, measurable targets; Third, theidentification and consultation of key stakeholders, sometimes knownas experts, for each factors (economics, social environmental, techni-cal); Lastly, available technologies are assessed against each of the fac-tors and decision made according to the assessment. The challenge ofthis methodology underlies the difficulty of reaching a consensus

Table 1Overview of the R&D project selection literature.Source: “APractical R&DProject-Selection ScoringTool,”AnneDePianteHenriksen andAnnJensen Traynor, IEEE Transaction on Engineering Management, Vol. 46, No. 2, May 1999.

No. Category Methods

1 Unstructured peerreview

2 Scoring3 Mathematical

programmingInteger programming, linear programming, nonlinearprogramming, goal programming, dynamicprogramming

4 Economic models Internal rate of return, net present value, return oninvestment, cost-benefit analysis, option pricing theory

5 Decision analysis Multi-attribute utility theory, decision trees, riskanalysis, analytical hierarchy process

6 Interactive methods Delphi, sorting, behavioral decision aids,decentralized hierarchical modeling

7 Artificial intelligence Expert systems, fuzzy sets8 Portfolio optimization

Fig. 1. The structure of the proactive technology selection methodology (PTSM).

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given an array of divergent assessments from the various experts. Thus,researchers resort to a discipline called multi-criteria-decision-making(MCDM) for methods to converge the diverse assessments. Chan et al.(2000)) presents a technology selection algorithmtoquantify both tangibleand intangible benefits in fuzzy environments. It applied the theoryof fuzzysets to hierarchical structural analysis and economic evaluations. Decisionmakers are asked to express their opinions on comparative importance ofvarious factors in linguistic terms rather than exact numerical values. By ag-gregating the hierarchy, the preferentialweight of each alternative technol-ogy is found,which is called fuzzy appropriate index. The fuzzy appropriateindices of different technologies are then ranked and preferential rankingorders of technologies are found.

The Consensus Ranking Organizational Support System (CROSS)presented by Tavana (2003) is a multi-criteria group decision-makingmodel that has been implemented successfully at Kennedy SpaceCenterin the USA. This system aims to capture the decision makers' beliefsthrough sequential, rational and analytical processes. It uses the Analyt-ical Hierarchy Process (AHP) to enhance the decision-makers' intuitionin evaluating sets of advanced technology projects. Houseman et al.(2004) constructed a methodology that consolidated several studies,especially that of Chan et al. (2000) and Tavana (2003) to the specificrequirements of the aviation subsidiary company.

In recent years, various modifications or extensions of the AHP havebeen proposed to address the issue of technology selection. Chen et al.(2006) mitigated the vagueness problems in human judgment by usinga fuzzy AHP model. Chen et al. (2009) added an element of sensitivityanalysis to the use of AHP. They emphasized that the hierarchical modellinks an organization's competitive goals and strategies in evaluating thetechnology alternatives' overall contributions to business success; whilethe sensitivity analysis helps to forecast and implement possible futurechanges in the economic environment, industry policies, and organizationstrategies. Kim et al. (2010) developed a dual AHP to prioritize new tech-nologies. Shen et al. (2010) proposed a technology selection process inte-grating fuzzyDelphimethod, AHP, andpatent co-citation approach (PCA).

Although various studies have focused on the evaluation and the se-lection of new technologies, none have addressed the issues of improv-ing the selected new technology for the adopting firm at the same time.Since the adopting firm has its own specific strengths and weaknessesin terms of its capabilities in absorbing the new technology, it is crucialfor thedecisionmaker to assess not just the importance of the new tech-nology but also the fit between the new technology and the adoptingfirm. The improvement of new technology refers to either themodifica-tion of the direction of development or the removal of inadequate prop-erties of the underlying new technology in order to satisfymarket needs(Shin et al., 2013; Grunske, 2007). Therefore, devising a more generaland proactive technology selection model that integrates the evalua-tion, the selection and the improvement of new technology wouldadvance the field of technology selection. Such model will be an

informative and powerful tool for decision makers when choosingamong competing new technologies.

2. Proactive technology selectionmethodology for new technologies

In this section, we advance and discuss the proactive technology se-lection model (PTSM) used in this study. The term ‘proactive’ is chosento highlight that it not only evaluates the relative attractiveness andrankings of each of the competing new technologies but also identifiesspecific areas (factors) for the adopting firm to improve the overall per-formance of the new technology.

Themodel basically consists of three stages, as shown in Fig. 1. In Stage1, qualitativemethods of Delphi and brainstorming are applied to identifythe crucial factors that affect the development of new technologies. InStage 2, the DANP (DEMATEL-based ANP) and the VIKOR methods areadopted to generate consensus in the forms of three relevant reports.These three reports are then utilized as inputs to Stage 3. In Stage 3, qual-itative methods of focused group and panel discussion are then used tosynthesize the results from Stage 2 and make the final decision. Usingthis straightforward yet comprehensive three-stage process of thePTSM, the final decision regarding the selection and the directions for im-provement of the new technology chosen can then be reached.

2.1. Stage 1

The goal of the PTSM is to select themost appropriate technology froma list of new technology alternatives for a firm. To achieve this goal, themission of Stage 1 in thismodel is for the researcher to identify those cru-cial factors that affect or ‘could affect’ the goal. In the language of researchmethods (Cooper and Schindler, 2003), the mission of this stage is toclearly depict themanagement dilemma and then translate this dilemmainto management questions, research questions, investigative questionsand finally to measurement questions. The guiding principle at thisstage is to have a list of factors that are comprehensive enough yet nottrivial. Previous studies in theMCDM literature seldom addressed the de-tails regarding the pool of participants and the procedures actually takenin soliciting participants' assessments. Since the results from all MCDMmodels are highly sensitive to the composition of the participants, it iscritical that the participants invited are knowledgeable enough to coverboth the macro and the micro aspects of the underlying technology.Consequently, we adopt a modified Delphi method (Hung et al., 2013)and modify it further to serve as a model for factors identification.

For comparison, as shown in Fig. 2, the right hand side displays theactual procedures of Stage 1 in the PTSM; while the left-hand sideshows the modified Delphi procedures proposed in the previousstudy. Two groups of participants ((A1,…Am) and (B1,…Bn)) are se-lected and invited to the project. In the first step, participants of GroupA are brought in to propose an imminent management dilemma

Fig. 2. Contrasting the Stage 1 in the PTSM to that of a similar model.

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regarding the underlying technology using a traditional brainstormingmethod. In step 2, another groupof participants, Group B, is then formedand asked to express their viewpoints in translating the managementdilemma into some plausible management questions. The mission ofGroup B is to propose a list of factors and the hierarchy among these fac-tors that critically affect the goal in resolving the dilemma. In both steps3 and 4, a conventional Delphimethod is used to congregate the diverseopinions from these experts of Group B. In step 5, Group A is againbrought back to examine the factors and the hierarchy proposed byGroup B. In other words, the final choices regarding these factors andtheir hierarchy are to be approved by Group A.

Regarding the qualifications for participants in Group A and B, oursuggestion is that Group A ought to be consisted of persons in the tophierarchical level of that institute and seasoned with strategic mattersin the relevant industry; while Group B ought to be consisted of personsin the middle or operating level of the underlying institute who are fa-miliar with the new technologies. This arrangement helps to avoid thepossible bias that might be encountered if participants are all fromone level only. This arrangement also carries an additional benefit ofaddressing not only the strategic-level but also the operating-levelaspects of the new technology selection issues that an institute is facingwith. It must be stressed that this arrangement, though trivial, is vital tooperationalizing a technology selection process.

2.2. Stage 2

After the relevant factors are identified in Stage 1, the relative impor-tance for each of the factors with the new technology needs to be

determined. A natural choice of method will be the popular AHP.To use AHP properly, it is required that the factors in the same levelof the hierarchy being independent (Saaty, 1990). However, it is doc-umented that many factors may interact with each other with feed-back (Saaty and Takizawa, 1986). Thus, a straight utilization of AHPwould bemostly improper. To unravel this limitation, a more generalform of the analytic AHP, known as the analytic network process(ANP) was developed (Saaty, 1996). ANP does not require indepen-dence among factors, so it can be used as an effective tool in ourstudy.

Before applying the ANP, it is still necessary to investigate the inter-relationship structure among the factors. The DEMATEL technique, asproposed by the Battelle research center in 1972, analyzes complicatedproblems in the real world by building a network interrelation (Gabusand Fontela, 1972). This method is effective for solving complicatedproblems between groups (Tzeng et al., 2007; Peng and Tzeng, 2013;Lu et al., 2013; Chiu et al., 2006). To inquire the structure of cause-and-effect among factors, we decided to use a DEMATEL-based ANP(DANP) technique put forward in several studies (Yang et al., 2013;Liu et al., 2012; Tzeng and Huang, 2012). The detailed procedure ofthe DANP technique is described in the Appendix A.

In the first part of Stage 2, i.e., Fig. 3, the main tasks are for us, theresearcher, to prepare the DANP questionnaires instrument and thensolicit the responses from participants in Group C. In the second part,a method called VIKOR is applied to provide a more viable evaluationof the performance of each of the new technology alternatives foreach of the factors given the underlying new technology is adopted.VIKOR is best known for its versatility in solving decision problems

Fig. 3. The framework of Stage 2.

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with conflicting and non-commensurable (different units) factors, as-suming that compromise is acceptable for conflict (Opricovic, 1998).

VIKORwas originally developed by Duckstein and Opricovic, (1980)to solve decision problems with conflicting and different-units criteria.It assumed that compromise is acceptable for conflict resolution andthe decision maker wants a solution that is the closest to the ideal.

Fig. 4. Three reports ge

Those alternatives are evaluated according to all established criteria.The VIKOR has become prevalent in recent years among researchers(Yoon and Hwang, 1995). The detail procedure of VIKOR method isgiven in the Appendix B.

As shown in Fig. 4, three informative reports engendered in the endof Stage 2, include the Cause-and-Effect Report, the Importance-ScoreReport, and the Performance-Score Report. These three reports arethen used as the inputs to Stage 3.

2.3. Stage 3

The purpose of Stage 3 is for the decisionmaker to reach the final de-cision. As illustrated in Fig. 5, this mission is accomplished through aprocess of focused group and panel discussion. This third group of par-ticipants, i.e., Group C, should be made up of the project's leader andtop ranking personnel. It is suggested that the researcher should pre-pare a presentation on the results of the DANP and VIKOR analyses forthis group of participants. The presentation should discuss three things:

First, the cause-and-effect interrelationship among factors; Second,the importance score for each of the factors; And lastly, the performancescore for each of the alternative technologies on each of the factors.

2.4. Areas of improvement for the selected new technology

To identify areas of the new technology selected for the adoptingfirm to work on so that the overall performance of the new technologycan be improved, we followed the practice of Eisenhower Matrix (Baer,2014; Eisenhower, 1954) and formulated a two dimensional graph. Itintegrated the results from the Importance-Score Report and thePerformance-Score Report.

As shown in Fig. 6, this graph depicts the scatterplot of factors basedon their scores of ‘Importance’ and ‘Performance’. Those factors that fallunto the fourth quadrant are the potential factors (targets) of the new

nerated in Stage 2.

Fig. 5. The framework of Stage 3.

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technology for the adopting firm to improve on. However, as pointedout earlier, these factors are mostly interrelated. Thus, it is importantfor the researchers to further investigate the cause-and-effect interrela-tionships for each of these potential factors.

As an illustration, on the upper right-hand side of Fig. 6, a cause-and-effect graph was added to indicate that the researcher has decided tofurther investigate the cause-and-effect relationship for oneof those po-tential factors, supposed to be Factor A. Factor A is located in the fourthquadrant, thus considered as a potential target for improvement. How-ever, after tracing its cause-and-effect, it is found that Factor A is mainlyinfluenced by Factors B and C. Further, Factor C is also strongly influ-enced by Factor B. Thus, it might be more feasible for the adoptingfirm to work on the B factor of the new technology instead of on FactorA directly.

This step is neglected in the previous studies in the decision-makingliterature (Gabus and Fontela, 1972; Lu et al., 2013; Yang et al., 2013;Tzeng and Huang, 2012). It is suggested that this additional step betakenwhen the researcherwants to devise a proper strategy for improv-ing the overall performance of thenew technology for the adopting firm.

The model developed in this section will later be applied for an ICmanufacturing firm in Taiwan. The task is for the IC manufacturingfirm to select one particular 3D-TSV technology from three alternatives.These three new technologies are considered competing since that theybasically solve the same problem but with their own set of pros andcons.

Fig. 6. Identification of factors for improvement.

3. 3D IC TSV technology

As consumer electronic devices are increasingly converted to hand-held and mobile units, semiconductor components are expected to de-liver more functionality at greater speed and in smaller dimensions. Inthe past, miniaturization of circuits is the main approach used to meetthe challenge. The continuous miniaturization of circuits has been de-scribed as a phenomenon called Moore's Law. Moore's law states that,over the history of computing hardware, the number of transistors ina dense integrated circuit doubles approximately every two years(Moore, 1965). Blessed with breakthroughs in material science and li-thography technologies, global semiconductor industries have success-fully extended the lifespan of Moore's Law until now.

However, there is a rising concern that the physical limitation ofmaterials will one day kick in when Moore's Law ceases to be valid. Inresponse, several technologies have emerged to continue Moore's Lawby increasing chip density at the two-dimensional (2D) level, such asSoC (System-on-a-Chip), and even at the three-dimensional (3D)level, such as SiP (System-in-Package). Recently, 3D integration usingmultiple vertical layers of transistors emerges as a way to increasesdevices' performance. The most prevalent solutions in 3D integrationappear to be 3D-packaging. 3D-packaging saves space by stacking sepa-rate chips or packages in a single package. However, this packaging,known as ‘Stacked Package-on-Package (PoP)’, does not integrate thevarious chips into a single circuit. The chips in thepackage communicateusing off-chip signaling, much as if they were mounted in separatepackages on a normal circuit board.

In contrast, a 3D IC is a single chip on which all components on thelayers communicate using on-chip signaling. Despite there are variouspractices for 3D integration, TSV-enabled 3D silicon solutions (TSVi.e., through-silicon via, where via stands for vertical interconnection ac-cess) have apparently become the mainstream technologies in the antic-ipatable future. A true 3D IC TSV technology, using a copper via fillprocess, improves device speed and enables silicon efficiency gained bystacking devices directly on top of one another. Alternatives such as2.5D TSI (Through-via Interposer) technology, using interposers and ad-vanced wire-bonding techniques to enable the redistribution of circuitryto connect one layer of active ICs with another may be viable in theshort term tomidterm, but it will become less attractive as device perfor-mance requirements become a more dominant issue in the latter half ofthis decade. A comparison among the 2D, 2.5D and 3D IC is given in Fig. 7.

3.1. Via creation

To implement 3D integration, a branch of TSV technologies called Viashas emerged as an important method of 3D integration. Themajor differ-encewithin this family of Vias originates from the different process of Viaformation. What Vias are made before CMOS is referred as “Via-first”;while Vias made between CMOS and BEOL (back-end-of-line) are re-ferred as “Via-middle” andViasmade after BEOL are referred as “Via-last.”

4. Evaluation, selection and improvement of 3D IC TSV technologies

A major IC manufacturer in Taiwan is aware of the potential opportu-nities of and the threats from the new 3D IC TSV technologies. It is immi-nent for the manufacturer to build up its 3D IC capability. However,selecting a specific TSV technology is known to entail different investmentschemes andproduct developmentplan that are generally irrevocable. ThePTSMwas applied to assist the decisionmaker in selecting amore suitable3D IC TSV technology for the manufacturing firm and identifying areasfor improvement given that a particular technology has been selected.

4.1. Results from Stage 1

In Stage 1, ten participants from the IC manufacturing and designfields were invited to participate; three in Group A and seven in

Fig. 7. A symbolic drawing comparison among 2D, 2.5D and 3D IC.Source: Tzu-Kun Ku, “3D TSV Stacking IC Technologies, Challenges & Opportunities,” Industrial Technology Research Institute(ITRI), 2011.

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Group B. In the beginning, Group A proposed the issue which exploredthe development of 3D IC TSV technology on the current state and pos-sible future alternatives through a panel discussion. Then, Group B cameup with twenty-six factors that they considered crucial in selecting 3DIC TSV technologies. Later, Group A decided to reduce the number offactors to eighteen for the proposed issue, as shown in Table 2.

4.2. Results from Stage 2

The eighteen factors were approved and assigned to one of fivedimensions by Group A as shown in Table 3. We then devised the ques-tionnaire and used it to survey the participants in Group C. The form ofthe questionnaire is given in Appendix C.

We found it necessary to conduct the survey on a face-to-face inter-view basis. Since the questionnaire instrument in a DANP study is pecu-liarly odd to most participants, thus the presence of researcher helpsignificantly to clarify many of the questions. The researcher's interactionwith the participant is an indispensable part in a typical technology selec-tion study. It safeguards the results derived from the survey to be valid.

Table 2Factors relevant to 3D IC TSV technologies.

No. Factors proposed by Group B Factors approved by Group A

1 Technology foresight Technology patentability2 Technology importance Technology application3 Technology patentability Technical continuity4 Technical continuity Technology extension5 Technology application Return on investment6 Technology extension Market share7 R&D cost Cost reduction8 Cost reduction Product performance9 Market share Adverse effect10 Potential Market scale Integration of heterogeneous wafers11 Entry timing Access to technology related resources12 Design tolerance Technology standardization13 Adverse effect Supply chain collaboration14 Access to technology related resources Rivalry among existing competitors15 Related equipment support Technology risk16 Technology standardization Technology commercialization risk17 Supply chain collaboration Collaboration risk18 Rivalry among existing competitors Funding risk19 Integration of heterogeneous wafers20 Performance enhancement21 Heat management22 Technology risk23 Accomplishment risk24 Technology commercialization risk25 Collaboration risk26 Funding risk

Table 4 shows the total relation cause-and-effect among these eigh-teen factors. The number in each cell of the table is interpreted as thestrength of influence of a factor in row i on another factor in column j.For example, the factor ‘Technology continuity’ is causing the factor‘market share’ and the strength of this causal relation is calculated tobe 0.484. Table 4 reveals some interesting phenomena: First, all theseeighteen factors exhibit complicated cause-and-effect relationships.This is consistentwith the criticism in the literature against the assump-tion of AHP regarding the independence among factors (Saaty, 1990;Saaty and Takizawa, 1986). Second, the column sum indicates themagnitude of the column factor being affected by all other factors.

Rivalry-Among-Existing-Competitors factor is found to have thelargest value at 8.374. This indicates that factor 14 is the most affectedone than any other factors are. Third, the row sum indicates on theother hand the total strength of that row factor affecting on other fac-tors. For example, the row sum for Supply-Chain-Collaboration factoris 8.334, which is the largest among all factors. This suggests thatSupply-Chain-Collaboration factor is the most influential factor amongall the eighteen. Fourth, it is also worth of pointing out that the differ-ence between the row sum and the column sum of a same factor canbe regarded as the net effect of that factor. As an example, the rowsum of factor ‘Supply chain collaboration’ is 8.334 while the columnsum is 7.856. The positive difference of .478 suggests that Supply-Chain-Collaboration factor is much more a cause than an effect. As acontrast, the difference for Rivalry-Among-Existing-Competitors factoris −0.733 (7.641–8.374), an indication that it is more an effect than acause.

Table 5 shows the perceived importance of each of the factors. Theimportance score is the weight that we will apply when evaluatingthe attractiveness of each of the 3D IC TSV technology choices. Thescore is derived through using DANP algorithm. The results show thatthe dimension of ‘Risk’ received the highest score (.211). The dimensionof ‘Product benefits’ received the lowest score (.183). Apparently, risk isthe foremost concern among participants when they were confrontedwith the task of evaluating the likeliness of the technology. Further,the factor of ‘Return on investment’ received the highest score at .106and the factor of ‘Market share’ followed at .103. This phenomenonreveals that the adoption of a 3D IC TSV technology is ultimately morea business decision than a technology decision.

The third report, Table 6, shows the performance, as perceived byparticipants, of the underlying technology on each of the factors.These scores, calculated from using the algorithm of VIKOR, show thedistance between the performance of the technology and the ideal per-formance. The narrower the distance, the better it performs, and thehigher the performance score is. Thus, the sum of the score is a practicalmeasure for making selection decision.

Table 3The hierarchy of dimension/factor and definition for factors.

Dimension Factor Definition

Technology benefits Technology patentability The emerging technology help to generate patents for the instituteTechnology application The emerging technology is applicable to various productsTechnology continuity The emerging technology is compatible with existing technologies so that lower R&D and less

new equipment are needed during the developing process, thus cutting short development timeTechnology extension The emerging technology is essential for extending into other technologies in the future

Business benefits Return on investment The emerging technology can provide excessive returnMarket share The emerging technology can significantly increase the market share of related products

Product benefits Cost reduction The emerging technology can effectively decrease the cost of related productsProduct performance The emerging technology can significantly increase the performance of related productsAdverse effect The emerging technology will not cause adverse effect on related productsIntegration of heterogeneous wafers The emerging technology can effectively and efficiently integrate wafers/dies into 3D IC

Supply chain benefits Access to technology related resources The required resources for the emerging technology such as patents, equipment and manpowerare accessible

Technology standardization The emerging technology has clear standards under developing processSupply chain collaboration In applying the emerging technology, there is strong support from the upstream, midstream, and

downstream companiesRivalry among existing competitors There is no or few competitors developing the underlying emerging technology

Risk Technology risk The emerging technology can be developed successfully in timeTechnology commercialization risk The emerging technology can be practically applied to the underlying institute's product linesCollaboration risk The upstream, midstream, and downstream companies related to the emerging technology are

actively cooperate with each otherFunding risk The fund available is enough to complete the development of the emerging technology

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4.3. Results from Stage 3

The above three reports were then compiled and presented to theparticipants of GroupD.We explained the detail algorithmand functionof the DANP and the VIKOR to the participants. We found it vital to ex-plain the algorithms of these twomethods so as to win the participants'trust. This helped the participants to feel comfortable inmaking thefinaldecision based on the findings of these reports. As shown in Table 6, thetechnology choice of Via-Middle received the highest total score at .565.It is sensible then for the underlying manufacturer to select the Via-Middle process for its 3D IC development. After several rounds ofdiscussion, the participants in Group D finally agreed on the Via-Middle process as the choice for the underlying manufacturer.

After the selection decision is made, there is one important issueremained to be addressed. Although Via-Middle in this case is the bestchoice among the three alternatives, it is still not good enough. A naturalcontinuation question is finding what this IC manufacturing firm coulddo to improve the performance of the new technology they have

Table 4The Cause-and-Effect Report.

Note: This cause-and-effect report is derived from themathematical procedure of A1 to A5 as defactor on another factor. As an example, the value of .322 in the upper left diagonal cell (1–1) inThis long-run effect came from the cumulative indirect effects of all other factors on the Factorstrength of effect between two factors does depend on the direction of the effect.

decided to adopt. Stated differently, it would be ideal to identify thosefactors of the technology for the manufacturer to focus on for improve-ment. This question can be answered with the help of. The figure isderived by integrating the importance scores on Table 5 and the perfor-mance scores on Table 6 under the column of Via-Middle.

As shown in Fig. 8, three factors fall on the first quadrant, three onthe fourth quadrant and the rest on the third quadrant. The focus shouldturn to factors located in the fourth quadrantwhich include ‘Technologyrisk’, ‘Technology commercialization risk, and ‘Supply chain collabora-tion.’ If the underlyingmanufacturer has limited resources that preventthem from improving all the factors simultaneously, then these threefactors could be the most appropriate targets for improvement at first.

However, tracing back the cause-and-effect relationship among thefactors, a very different picture from that as suggested in Fig. 9 emerged.Several interesting results are revealed: First, the top three causal fac-tors for ‘Technology risk’ are ‘Supply chain collaboration’, ‘Integrationof heterogeneous wafer’ and ‘Technology continuity,’ respectively. Sec-ond, the top three causal factors for ‘Technology commercialization

scribed inAppendixA. It shows the long-run equilibriumeffect, i.e., the total relation, of onedicated that the strength of the long-run effect of Technology Patentability on itself is .322.of Technology Patentability. It is to be noted that the matrix is not symmetric because the

Table 5The Importance Score Report.

Dimension Factors

Importance

score for

dimension

Importance

score for

factors

Technology benefits Technology patentability 0.194 0.044

Technology application 0.050

Technology continuity 0.051

Technology extension 0.050

Business benefits Return on investment 0.209 0.106

Market share 0.103

Product benefits Cost reduction 0.183 0.052

Product performance 0.045

Adverse effect 0.040

Integration of

heterogeneous wafers0.047

Supply chain benefitsAccess to technology

related resources0.202 0.048

Technology

standardization0.046

Supply chain collaboration 0.053

Rivalry among existing

competitors0.056

Risk Technology risk 0.211 0.054

Technology

commercialization risk0.054

Collaboration risk 0.052

Funding risk 0.052

Sum 1.000 1.000

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risk’ are “Technology continuity’, ‘Supply chain collaboration’ and ‘Inte-gration of heterogeneous wafer.’ Third, the top three causal factors for‘Supply chain collaboration’ are ‘Technology continuity’, ‘Integration ofheterogeneouswafer’ and ‘Technology application’ respectively. Fourth,while the factor ‘Supply chain collaboration’ is both an important targetand a significant cause to the other two targets, both factors of ‘Technol-ogy risk’ and ‘Technology commercialization risk’ are actually more ‘ef-fect’ than ‘cause’.

Table 6The Performance Score Report.

Via-first Via middle Via last

1. Technology patentability 0.025 0.027 0.026

2. Technology application 0.023 0.033 0.033

3. Technology continuity 0.026 0.031 0.029

4. Technology extension 0.026 0.031 0.028

5. Return on investment 0.041 0.057 0.059

6. Market share 0.058 0.061 0.054

7. Cost reduction 0.022 0.028 0.027

8. Product performance 0.030 0.030 0.025

9. Adverse effect 0.024 0.023 0.022

10. Integration of heterogeneous wafers 0.027 0.029 0.028

11. Access to technology related resources 0.020 0.025 0.028

12. Technology standardization 0.015 0.018 0.020

13. Supply chain collaboration 0.015 0.023 0.025

14. Rivalry among existing competitors 0.034 0.036 0.031

15. Technological risk 0.024 0.028 0.033

16. Technology comercialization risk 0.022 0.027 0.029

17. Collaboration risk 0.026 0.030 0.030

18. Funding risk 0.023 0.027 0.028

Total score 0.482 0.565 0.556

Performance scoreFactors

This seemingly complicated relationship can best be described by agraph. As shown in Fig. 9, the factors as target for improvement areplaced in the rectangular boxes while the top three causal factors foreach of the targets are shown in the oval boxes. The arrow shows the di-rection from cause to effect and the number on the arrow indicates theranking of influence of a particular causal factor among all other factorson the target factor.

Thus, to improve the performance of the new technology moreeffectively and efficiently, the manufacturer should actually work onfactors that are of the nature of causes, instead of on the factors ofeffects. In other words, to improve the overall performance of 3D ICcapability when the Via-Middle process is adopted by the underlyingIC manufacturer, the top three factors they should focus on are thethree factors shown in the oval boxes, instead of the factors originallyidentified as the target (in rectangular boxes). This is an interestingand important finding derived from the integration of the DANP andthe VIKOR methods. As far as we know, the PTSM is the first effort inthe literature to propose this algorithm in identifying the target factorsfor further improvement in enhancing the overall performance of thenew technology selected.

5. Conclusion and discussion

In this study, we add to the technology selection literature by formu-lating a more general and proactive model that integrates the evalua-tion, the selection and the improvement of new technology. Themodel consists of three stages that encompass: First, asking participantsin Group A to identify an appropriate management question for resolv-ing the management dilemma caused by the new technology. It thenasks participants in Group B to determine a list of factors relevant to an-swering that specific management question; Second, given the list offactors screened and approved by participants in Group A, the research-er then devises the questionnaires instrument and conduct personalinterview to obtain the evaluation opinion of participants in Group Con each of the measurement questions in the instrument. Based onthe evaluation responses from participants in Group C, the researcherthen conducts an analysis by using the algorithms of DANP andVIKOR. Three reports were generated that reveal the cause-and-effectrelationship among all the factors, the importance score of each of thefactors and the performance of each of the technology choice on eachof the factors; Third, a different group of participants (Group D) thengathered to discuss intensively regarding the results of these reportsand the implications for the question raised in Stage 1. The decisionon the selection of and improvement on the final choice of new technol-ogy will then be reached by participants in Group D.

The PTSMwas applied to a case of selecting 3D IC TSV technology foran IC manufacturer in Taiwan. Since the case is used for illustrationpurpose only, we did not cover the 3D IC TSV technologies in detailand also reduce the number of alternative TSV technology to three.The analysis showed that the Via-Middle process should be selected.However, we do notice that the scores for the Via-Middle and the Via-Last were actually very close. As shown in Table 6, the total score forthe Via-Last was .556 which was less than that of the Via-Middle by.009. This is an indication that the decision making should not rely onthis one number only. The results should be checked for possible errorsor biases inherent in the process.

This study contributed to the literature by extending the conven-tional MCDM analysis further in integrating the three reports fromDANP and VIKOR to identify the factors for improvement. We arguedthat the target factors (high in importance but low in performance)are not necessary the most appropriate factors for improvement ifthere is time and/or cost constraint in developing new technology. Wedemonstrated that two target factors in the fourth quadrant of Fig. 8are actually more of effect in nature. They are strongly affected bythree other factors. Even though the third factor ‘Supply chain collabo-ration’ is both of cause and effect in nature, it is also influenced heavily

Fig. 8. Two-dimensional graph of factors with VIA-middle technology. Note: This diagram followed a popular time management scheme called the Eisenhower Matrix (Baer, 2014;Eisenhower, 1954). In utilizing the scheme, every task is evaluated on two axes: important/unimportant and urgent/not urgent. In the same spirit, we ask participants in Group C todetermine a vertical line and a horizontal line to divide the factors into four quadrants based on their subjective assessment on the Importance and Performance scores of the factors.

Fig. 9. The cause-and-effect relationship among the improvement target factors and their causal factors.

199C.-Y. Hung, W.-Y. Lee / Technological Forecasting & Social Change 103 (2016) 191–202

by the same three factors. Thus, we reasoned that it will be much moreefficient and effective for the underlying manufacturer to focus onthose three causal factors of ‘Integration of heterogeneous wafers’,‘Technology continuity’ and ‘Technology application’ in their effort toimprove the performance of their 3D IC investment if they have decidedto adopt the Via-Middle process.

The PTSM is an integration and extension of various methods pro-posed in the area of multiple criteria decisionmaking (MCDM). Howev-er, we noticed that earlier applications ofMCDMmethods in technologyselection were used typically in a mechanical manner. The decisionswere reached mechanically from the results of the application ofmodels. We differ to these practices common in previous studies. Weaccentuate that the technology selection decision is a critical matter. Of-tentimes, once the technology selection decision is made and actionsconducted, it is irrevocable. Thus, even though the logic and procedureof PTSM is straightforward, we recommend the model be used in an it-erativemanner. For example, Stage 2 can be repeated for several runs todetermine the sensitivity of each of the factors to the compositionchange of the participants. The role of decision models should not bemystified to an extent that it replaces the responsibility of the decisionmaker in synthesizing all the inputs available. Wemaintain that models

of this kind are best employed as a framework for discussion and exam-ination. The model developed in this study offers a sequence of trans-parent steps to provide clarity and consistency in the general processof evaluation, selection, and improvement of new technology. Such pro-cess will be an informative and powerful planning tool for decisionmakers when choosing among competing new technologies on behalfof the adopting firm.

Appendix A. Procedures of the DANP

The DANP combines the original DEMATEL with the basic conceptsof ANP. The procedures of the DANP adopted in this study are summa-rized as follow:

Step 1: Estimate the direct relation matrix

A direct relationship matrix R is produced for each participant asshown in Eq. (A1).

R ¼ rhij ðA1Þ

200 C.-Y. Hung, W.-Y. Lee / Technological Forecasting & Social Change 103 (2016) 191–202

where h denotes the hth participant in Group A. The number of partici-pants in Group A is denoted as H in the following equations. An averagematrix A is then derived from themean of the same factor in the variousdirect matrices for all participants. The average matrix A is as follows:

A ¼ aij� �

nxn ¼ 1H

XH

h¼1rhijh i

nxnðA2Þ

Step 2: Estimate the normalized direct influence matrix

The normalized direct influencematrix X is obtained by normalizingthe average matrix A.

X ¼ A=m ðA3Þ

where

m ¼ max max1≤ i≤nXn

j¼1aij;max1≤ j≤n

Xn

i¼1aij

h iðA4Þ

Step 3: Develop the total influence matrix

Assuming a continuous decrease of the indirect effects of factorsalong the powers of X, e.g., X2, X3,…, Xg and limg−N∞ Xg = [0]nxn,where X = [xij]nxn; 0 b = xij b 1; 0 b =∑ixij≤1; 0 b =∑ jxij≤1 andat least one column sum ∑ixij or one row sum ∑ jxij equals 1. Then,the total influence matrix T is derived as

T ¼ X þ X2 þ…þ Xg ¼ X I−Xð Þ−1;when limg→∞

Xg ¼ 0½ �n�n ðA5Þ

Step 4: Explore the influence weights within dimensions

Each element of matrix T, tij, discloses the intensity of influence thatfactor i has on factor j, and the influential network relationship map(INRM) can thus be obtained. The influence matrix T can be displayedin two levels, namely the “dimension” level as TD and the “factor”level as Tc, respectively. Tc can be described as the matrix in below:

ðA6Þ

Step 5: Normalized the total influence matrix for criteria (factor)level.

(A7)

The total influence matrix for criteria level, Tc, is then normalizedand denoted as Tcα

where

TC

α11 ¼

t11

C11=d111 ⋯ t

11

C1 j=d111 ⋯ t

11

C1m1=d111

⋮ ⋮ ⋮t11

Ci1=d11i ⋯ t

11

Cij=d11i ⋯ t

11

Cim1=d11i

⋮ ⋮ ⋮t11

Cm11=d11m1

⋯ t11

Cm1 j=d11m1

⋯ t11

Cm1m1 =d11m1

2666664

3777775

ðA8Þ

and

d11i ¼Xm1

j¼1t11Cij ðA9Þ

where di11 denotes the element of normalized influence for the elementtcij11

Step 6: Find the ‘supermatrix’Wby transposing the normalized totalmatrix Tcα:

ðA10Þ

where

ðA11Þ

Step 7: Obtain the weighted normalized supermatrix Wα by multi-plying the normalized total influence matrix TDα with thesupermatrix W.

Wα ¼ TαD �W

¼

tα11D �W11 ⋯ tαi1D �W i1 ⋯ tαn1D �Wn1

⋮ ⋮ ⋮tα1 jD �W1 j ⋯ tαijD �W ij ⋯ tαnjD �Wnj

⋮ ⋮ ⋮tα1nD �W1n ⋯ tαinD �W in ⋯ tαnnD �Wnn

266664

377775

ðA12Þ

The normalized total influence matrix for dimension level TDα can bederived by normalizing the total influencematrix for dimension level TDas shown below:

TD ¼

t11D ⋯ t1 jD ⋯ t1nD⋮ ⋮ ⋮ti1D ⋯ tijD ⋯ tinD⋮ ⋮ ⋮

tn1D ⋯ tnjD ⋯ tnnD

266664

377775

ðA13Þ

201C.-Y. Hung, W.-Y. Lee / Technological Forecasting & Social Change 103 (2016) 191–202

TαD ¼

t11D =d1 ⋯ t1 jD =d1 ⋯ t1nD =d1⋮ ⋮ ⋮

ti1D =d2 ⋯ tijD=d2 ⋯ tinD =d2⋮ ⋮ ⋮

tn1D =dn ⋯ tnjD =dn ⋯ tnnD =dn

266664

377775

¼

tα11D ⋯ tα1 jD ⋯ tα1nD⋮ ⋮ ⋮

tαi1D ⋯ tαijD ⋯ tαinD⋮ ⋮ ⋮

tαn1D ⋯ tαnjD ⋯ tαnnD

266664

377775

ðA14Þ

where

di ¼Xn

j¼1tijD i ¼ 1;2; :::;n: ðA15Þ

Step8: The normalized total influence matrix for the dimensions TDα

is then multiplied with the supermatrix W to obtain the newweighted supermatrix Wα:

Wα ¼ TαD �W

¼

tα11D �W11 ⋯ tαi1D �W i1 ⋯ tαn1D �Wn1

⋮ ⋮ ⋮tα1 jD �W1 j ⋯ tαijD �W ij ⋯ tαnjD �Wnj

⋮ ⋮ ⋮tα1nD �W1n ⋯ tαinD �W in ⋯ tαnnD �Wnn

266664

377775

ðA16Þ

Step 9: Lastly, the limit of theweighted supermatrix is found by rais-ing Wα to the power of g where g is sufficiently large.

This is the equilibrium state of the supermatrixWα and called, in thelanguage of DANP, the global influential weights.

Wα ¼ limz→∞

Wα� �z ðA17Þ

Appendix B. Procedures of the VIKOR

VIKOR is a compromise ranking method that utilizes the Lp-metric.The step of VIKOR procedures as adopted in this study are as follow:

Step1: Decide on a best f j⁎ value and a worst fj− value:

Let the alternatives be represented by A1, A2,…, Ak,…, Am, the perfor-mance score of alternative Ak in factor (criterion) j be denoted by fkj(k = 1, 2,…, m; j = 1, 2,…, n); wj is the influence weight (importancescore from DANP) of the jth factor. We identify the best f j⁎ values andthe worst fj− values for all of the factors. Where.

f j�¼ maxk fkj; j ¼ 1;2;…;n ðB1Þ

f−j ¼ mink fkj; j ¼ 1;2;…;n ðB2Þ

Step 2: Calculate the means of group utility (Sk) and utmost regret(Qk).

mink Sk stresses themaximumgrouputility for themajority; howev-er, mink Qk stresses selecting theminimum from themaximum individ-ual regrets/gaps.

Sk ¼Xn

j¼1

wjrkj ¼Xn

j¼1

wj f �j− f kj���

���� �

= f �j− f−j���

���� �

ðB3Þ

Qk ¼ maxj

rkj j ¼ 1;2;…;nj ðB4Þ

Step 3: Calculate the performance score.

This value (performance score of alternative k) can be measured bythe following equation:

R ¼ v Sk−S�ð Þ= S−−S�ð Þ þ 1−vð Þ Qk−Q�ð Þ= Q−−Q �ð Þ

where

S⁎ = mink Sk and S−=maxk Sk.Q⁎ = mink Qk and Q−=maxk Qk

Appendix C. The interrelationship between criterion i and criterion j

Using the scales 0, 1, 2, 3, and 4 to represent “no influence (0)” to “very high influence (4)”, please insert the direct influence score of factor i exertson each of other factors j.

202 C.-Y. Hung, W.-Y. Lee / Technological Forecasting & Social Change 103 (2016) 191–202

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Chih-Young Hung received a B.S. degree in electrical and control engineering fromNCTU,Taiwan. He served in the ITRI for 2 years as an assistant R&D engineer involved with thedesign of industrial robot arms. He earned his Ph.D. in business administration, with a fo-cus on finance, from the Texas Tech University, Texas, USA. Currently, he is an associateprofessorwith the Institute ofManagement of Technology at theNational Chiao-TungUni-versity in Taiwan. His current research interests include technology selection, valuationand commercialization of technology, and IPR policy.

Wen-Yi Lee is currently a Ph. D. candidate in the Institute of Technology Management atthe National Chiao-Tung University in Taiwan. His research areas include operationalresearch, technology forecasting and time-series analysis.