Essays on Economic Development Policies

Tax increment financing (TIF) policy is the most popular economic development policy in the United States. Despite the popularity of research on TIF, only a few comprehensive reviews of previous studies on TIF policy tool have been conducted. In light of this, the purpose of this paper is to review previous TIF studies relating to the controversy surrounding TIF programs. Specifically, previous studies do not provide clear answers about the efficacy of TIF and, indeed, raise more questions than answers. At the same time, this situation begs the question: why do local governments frequently use economic development policies? This is the most urgent task in the economic development academic area because previous studies have not answered that question in detail. To analyze the effects of competition and the forms of government on the utilization of business incentives at the local government level, this study focuses on two major incentives: tax credit and tax increment financing. The statistical results show that the competition mechanisms operate differently for each of the incentives. More specifically, the council-manager system considerably constrains the overall adoption and extent of use of business incentives. These results could indicate the prevalence of a particular form of government for economic development policies. To determine why local governments often use tax-based incentives, this study focuses on five major tax-based incentives: job creation tax credits, investment tax credits, R&D credits, property tax abatements, and customized job training subsidies. The statistical results indicate that a state government’s prevailing political ideology influences the choice of economic development activities. Accordingly, a more liberal state may be more likely to discourage property tax abatements and customized job-training subsidies and encourage job creation tax credits. Additionally, the competition mechanism does not operate as a trigger for tax-based incentives. This study also finds that state economic conditions are inversely related to the use of incentives. This result could imply the prevalence of political factors in the use of incentives. Clear evidence about the effectiveness of economic development incentives is limited. To bridge this research gap, this study uses the Upjohn Institute Panel Database on Incentives and Taxes (PDIT). Unemployment and employment rates are used to analyze the effectiveness of tax-based incentives. Statistical results indicate that tax incentives have a marginal impact on employment status and limited benefits to states. Only the R&D tax credit statistically significantly increases employment rates. This result supports the interpretation of economic development policies as a zero-sum gam

Forefront Office in Service Systems: Concept and Design

Conventionally, service systems have been considered to comprise two main offices: the front office and the back office. However, a new managerial office is needed to relate customers with technology as technology-based services play a critical role for customers prior to two main offices. Nevertheless, there is no attention to management of this new area. In this sense, this paper suggests a novel concept of the “forefront office” as the new area to represent service activities. The forefront office is defined as a service facility which allows customers to be provided with services by themselves using technology-based services prior to the front office. For design of the proposed forefront office, a modified service blueprint is proposed. Following on the design structure of the forefront office, three topologies of the forefront office are also proposed based on the modified service blueprint. The forefront office is expected to reduce labour costs and improve customer satisfaction in terms of mass customization

Novelty-focussed document mapping to identify new service opportunities

Successful innovation in services is considered a key factor for organisational sustainability. However, existing customer- and expert-centric approaches are becoming time-consuming and labour-intensive as the number and complexity of services increase. To counter this, an instrument for service opportunity analysis based on quantitative data and systematic processes is proposed in line with the notions of service engineering. At the heart of the suggested approach is text mining to extract the meanings of service documents and the local outlier factor to identify novel services based on quantitative indicators. A case study of mobile services is exemplified. The suggested approach promotes consensus building for promising service opportunities and enhances the efficiency of the fuzzy front-end stages of new service development.close0

Selection of technology acquisition mode using the analytic network process

Selecting the appropriate acquisition mode for a required technology, is one of the critical strategic decisions in formulating a technology strategy. Although a number of factors were found to be influential in the choice of technology acquisition mode, it still remains a void in the literature how to make a strategic decision, based on a huge set of those factors with the help of a systematic approach. This study deals with the selection of technology acquisition mode as a multiple criteria decision making (MCDM) problem. The proposed solution to the problem in this study, is the analytic network process (ANP) approach. Since the ANP is a MCDM method that can accommodate interdependency among decision attributes, it is capable of providing priorities of alternatives with consideration of interrelationships among strategic factors. The 21 influential factors identified from the empirical studies are included as sub-criteria in the ANP model, and they are grouped into five criteria: capability, strategy, technology, market, and environment. The final decision can be made based on the resulting priorities of the alternative acquisition modes. The proposed approach is expected to effectively aid decision making on which mode is adopted for acquisition of required technologies. A case of a software company is presented for the illustration of the proposed approach. (C) 2008 Elsevier Ltd. All rights reserved.Lee H, 2010, J ENG DESIGN, V21, P75, DOI 10.1080/09544820802232517Lee H, 2009, EXPERT SYST APPL, V36, P894, DOI 10.1016/j.eswa.2007.10.026Ustun O, 2008, OMEGA-INT J MANAGE S, V36, P509, DOI 10.1016/j.omega.2006.12.004Promentilla MAB, 2008, J ENVIRON MANAGE, V88, P479, DOI 10.1016/j.jenvman.2007.03.013Dagdeviren M, 2008, SAFETY SCI, V46, P771, DOI 10.1016/j.ssci.2007.02.002Raisinghani MS, 2007, IEEE T ENG MANAGE, V54, P673, DOI 10.1109/TEM.2007.906857Gerdsri N, 2007, MATH COMPUT MODEL, V46, P1071, DOI 10.1016/j.mcm.2007.03.015Simunich B, 2007, MATH COMPUT MODEL, V46, P1130, DOI 10.1016/j.mcm.2007.03.002Yuksel I, 2007, INFORM SCIENCES, V177, P3364, DOI 10.1016/j.ins.2007.01.001Jharkharia S, 2007, OMEGA-INT J MANAGE S, V35, P274, DOI 10.1016/j.omega.2005.06.005Wu WW, 2007, EXPERT SYST APPL, V32, P841, DOI 10.1016/j.eswa.2006.01.029HUNG SW, 2007, TECHNOVATIONSaen RF, 2006, APPL MATH COMPUT, V181, P1609, DOI 10.1016/j.amc.2006.03.013Shyur HJ, 2006, MATH COMPUT MODEL, V44, P749, DOI 10.1016/j.mcm.2005.04.018Leung L, 2006, J OPER RES SOC, V57, P682, DOI 10.1057/palgrave.jors.2602040Kahraman C, 2006, EUR J OPER RES, V171, P390, DOI 10.1016/j.ejor.2004.09.016POON J, 2005, J ENG TECHNOL MANAGE, V42, P321Hemmert M, 2004, RES POLICY, V33, P1019, DOI 10.1016/j.respol.2004.04.003ALLRED BB, 2004, J INT MANAG, V10, P259Baines T, 2004, INT J OPER PROD MAN, V24, P447, DOI 10.1108/01443570410532533Davenport S, 2003, R&D MANAGE, V33, P481, DOI 10.1111/1467-9310.00312Agarwal A, 2003, SUPPLY CHAIN MANAG, V8, P324, DOI 10.1108/13598540310490080Meade LA, 2002, IEEE T ENG MANAGE, V49, P59, DOI 10.1109/17.985748CHIESA V, 2001, R D STRATEGY ORG MANCho DH, 2000, TECHNOVATION, V20, P691Steensma HK, 2000, ACAD MANAGE J, V43, P1045Lee JW, 2000, COMPUT OPER RES, V27, P367Meade LM, 1999, INT J PROD RES, V37, P241Veugelers R, 1999, RES POLICY, V28, P63CANEZ L, 1999, P PORTL INT C MAN EN, P47, DOI 10.1109/PICMET.1999.787787Lowe J, 1998, R&D MANAGE, V28, P263Croisier B, 1998, R&D MANAGE, V28, P289Chiesa V, 1998, R&D MANAGE, V28, P199Veugelers R, 1997, RES POLICY, V26, P303Kurokawa S, 1997, IEEE T ENG MANAGE, V44, P124, DOI 10.1109/17.584921Madhok A, 1997, STRATEGIC MANAGE J, V18, P39Kumar V, 1996, IEEE T ENG MANAGE, V43, P273, DOI 10.1109/17.511838SAATY TL, 1996, DECISION MAKING DEPETYLER BB, 1995, STRATEGIC MANAGE J, V16, P43LLERENA P, 1994, GLOBAL TELECOMMUNICA, P257MAHONEY JT, 1992, STRATEGIC MANAGE J, V13, P559AUSTER ER, 1992, MANAGE SCI, V38, P778CAINARCA GC, 1992, RES POLICY, V21, P45DODGSON M, 1992, TECHNOL ANAL STRATEG, V4, P227MOENAERT RK, 1990, R&D MANAGE, V20, P291PISANO GP, 1990, ADMIN SCI QUART, V35, P153SHAN WJ, 1990, STRATEGIC MANAGE J, V11, P129BAUGHN CC, 1990, J HIGH TECHNOLOGY MA, V1, P181PERRINO AC, 1989, RES TECHNOL MANAGE, V32, P12HAMEL G, 1989, HARVARD BUS REV, V67, P133FORD D, 1988, LONG RANGE PLANN, V21, P85KOGUT B, 1988, STRATEGIC MANAGE J, V9, P319WALKER G, 1987, ACAD MANAGE J, V30, P589ROSENBLOOM RS, 1987, CALIF MANAGE REV, V29, P51TEECE DJ, 1986, RES POLICY, V15, P285ROBERTS EB, 1985, SLOAN MANAGE REV, V26, P3SPENCE AM, 1984, ECONOMETRICA, V52, P101NELSON R, 1982, EVOLUTIONARY THEORYSAATY TL, 1980, ANAL HIERARCHY PROCE

Analysis and visualisation of structure of smartphone application services using text mining and the set-covering algorithm: a case of App Store

This paper analyses and visualises the structure of smartphone application services to identify which smartphone application services are being developed and provided in detail. Using text information contained in web documents, the structure of smartphone application services is quantitatively and systematically analysed through two steps. First, representative services are identified and characterised to find distinctive features of current smartphone application services in the App Store by using text-mining and the set-covering algorithm. Second, based on representative services, three visual forms - grid, tree, and network - are illustrated to illuminate the structure of smartphone application services.N

Implementing technology roadmapping with supplier selection for semiconductor manufacturing companies

The rapid pace of technological innovation in the semiconductor manufacturing industry has necessitated the acquisition of competitive advantage from strategic technology planning. The vital requisite for this is well-timed investment including the replacement of old equipment with advanced new equipment. In such investment, selecting the appropriate semiconductor manufacturing equipment from the appropriate supplier is a key factor for successful technology planning. Therefore, equipment supplier selection should be taken into account in the technology planning of semiconductor manufacturing companies. One of the most widely used tools for technology planning is the technology roadmap (TRM). However, conventional TRMs have not considered the task of supplier selection. To address this limitation, this study proposes an extended, four-layered TRM that adds the layer of equipment supplier to the conventional layers of market, product, and technology. The equipment suppliers to be included in the new layer are selected from the supplier portfolio matrix composed of two performance axes: supplier performance and equipment performance. The candidates of equipment suppliers are placed on the supplier portfolio matrix according to the values of two axes determined by evaluation using the analytic hierarchy process (AHP). The proposed TRM is expected to be useful for technology planning by adding a consideration for equipment supplier selection in semiconductor manufacturing companies.Gerdsri N, 2010, TECHNOL ANAL STRATEG, V22, P229, DOI 10.1080/09537320903498553Yoon B, 2010, TECHNOL ANAL STRATEG, V22, P377, DOI 10.1080/09537321003647438Daim TU, 2008, TECHNOL FORECAST SOC, V75, P687, DOI 10.1016/j.techfore.2007.04.006Lee S, 2008, R&D MANAGE, V38, P169Oh J, 2008, INT J OPER PROD MAN, V28, P490, DOI 10.1108/01443570810875331Lee J, 2007, J INT MANAG, V13, P241, DOI 10.1016/j.intman.2007.05.003Chou YC, 2007, INT J PROD ECON, V105, P591, DOI 10.1016/j.ijpe.2006.05.006KWAK KH, 2007, ANAL EC EFFECT USINGArden W, 2006, MAT SCI ENG B-SOLID, V134, P104, DOI 10.1016/j.mseb.2006.07.004Stray J, 2006, IEEE T SEMICONDUCT M, V19, P259, DOI 10.1109/TSM.2006.873399HUNG SW, 2006, TECHNOL SOC, V28, P349KIM B, 2006, IEEE INT C MAN INN TPark SJ, 2005, IEEE T SEMICONDUCT M, V18, P605, DOI 10.1109/TSM.2005.858530Liu FHF, 2005, INT J PROD ECON, V97, P308, DOI 10.1016/j.ijpe.2004.09.005Aizcorbe A, 2005, SCAND J ECON, V107, P603, DOI 10.1111/j.1467-9442.2005.00429.xKatsikeas CS, 2004, IND MARKET MANAG, V33, P755, DOI 10.1016/j.indmarman.2004.01.002Petrick IJ, 2004, TECHNOL FORECAST SOC, V71, P81, DOI 10.1016/S0040-1625(03)00064-7Phaal R, 2004, TECHNOL FORECAST SOC, V71, P5, DOI 10.1016/S0040-1625(03)00072-6Rinne M, 2004, TECHNOL FORECAST SOC, V71, P67, DOI 10.1016/j.techfore.2003.10.002Vojak BA, 2004, TECHNOL FORECAST SOC, V71, P121, DOI 10.1016/S0040-1625(03)00047-7Chan FTS, 2003, INT J PROD RES, V41, P3549, DOI 10.1080/0020754031000138358Hood SJ, 2003, IEEE T SEMICONDUCT M, V16, P273, DOI 10.1109/TSM.2003.811894Winebrake JJ, 2003, TECHNOL FORECAST SOC, V70, P359, DOI 10.1016/S0040-1625(01)00189-5Choy KL, 2003, EXPERT SYST APPL, V24, P225ARDEN W, 2003, MAT SCI SEMICON PROC, V5, P313DULMIN R, 2003, J PURCH SUPPLY MANAG, V9, P177, DOI DOI 10.1016/S1478-4092(03)00032-3LIANG YY, 2003, IEEE INT S SEM MAN 3PHAAL R, 2003, P PICMET 03 20 24 JUChou YC, 2002, IEEE T SEMICONDUCT M, V15, P447, DOI 10.1109/TSM.2002.804885Das P, 2002, P IEEE, V90, P1637, DOI 10.1109/JPROC.2002.803665Cakanyildirim M, 2002, IEEE T SEMICONDUCT M, V15, P331, DOI 10.1109/TSM.2002.801386Fay B, 2002, MICROELECTRON ENG, V61-2, P11BHUTTA KS, 2002, SUPPLY CHAIN MANAG, V7, P126KUMAR S, 2002, J SCI EDUC TECHNOL, V11, P229SARKIS J, 2002, J SUPPLY CHAIN MANAG, V38, P18Scott G, 2001, TECHNOL ANAL STRATEG, V13, P343Kostoff RN, 2001, IEEE T ENG MANAGE, V48, P132, DOI 10.1109/17.922473Maydan D, 2001, MAT SCI ENG A-STRUCT, V302, P1Kappel TA, 2001, J PROD INNOVAT MANAG, V18, P39LEVINSON H, 2001, PRINCIPLES LITHOGRAPMCCARTHY JJ, 2001, P PICMET 01 29 JUL 2NARASIMHAN R, 2001, J SUPPLY CHAIN MANAG, V37, P28GARRAFFO FM, 2000, P 2000 IEEE INT C NO, P234, DOI 10.1109/ICMIT.2000.917339Masella C, 2000, INT J OPER PROD MAN, V20, P70Stulen RH, 1999, IEEE J QUANTUM ELECT, V35, P694, DOI 10.1109/3.760315Weber CA, 1998, EUR J OPER RES, V108, P208Chon S, 1997, REG STUD, V31, P25Roodhooft F, 1997, EUR J OPER RES, V96, P97BARBAROSOGLU G, 1997, PRODUCTION INVENTORY, V38, P14BRAY OH, 1997, P PORTL INT C MAN EN, P25, DOI DOI 10.1109/PICMET.1997.653238COMPTON WD, 1997, MANAGEMENT WORLD CLAGARCIA ML, 1997, SAND970665 SAND NAT*SEM IND ASS, 1997, NAT TECHN ROADM SEMSMITH B, 1997, MICROLITHOGRAPHY SCI, P171CHOI TY, 1996, J OPERATIONS MANAGEM, V14, P333ELLRAM LM, 1995, INT J PHYS DISTRIB, V25, P4, DOI DOI 10.1108/09600039510099928MOORE GE, 1995, P SOC PHOTO-OPT INS, V2440, P2GROSS C, 1994, P ADV SEM MAN C WORK, P14WEBER CA, 1993, EUR J OPER RES, V68, P173HILL RP, 1992, INT J PURCHASING MAT, V28, P31WEBER CA, 1991, EUR J OPER RES, V50, P2PAN AC, 1989, J PURCHASING MATERIA, V25, P36TURNER I, 1988, J OPER RES SOC, V39, P551TIMMERMAN E, 1986, J PURCHASING MAT MAN, V22, P2NARASIMHAN R, 1983, J PURCHASING MAT MAN, V19, P27PORTER ME, 1980, COMPETITIVE STRATEGYSAATY TL, 1980, ANAL HIERARCHY PROCELAMBERSON LR, 1976, J PURCHASING MAT MAN, V12, P19DICKSON GW, 1966, J PURCHASING, V2, P5

Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach

The strategic importance of performance evaluation of national R&D programs is highlighted as the resource allocation draws more attention in R&D policy agenda. Due to the heterogeneity of national R&D programs'''''''' objectives, however, it is intractably difficult to relatively evaluate multiple programs and. consequently, few studies have been conducted on the performance comparison of the R&D programs. This study measures and compares the performance of national R&D programs using data envelopment analysis (DEA). Since DEA allows each DMU to choose the optimal weights of inputs and outputs which maximize its efficiency, it can mirror R&D programs'''''''' unique characteristics by assigning relatively high weights to the variables in which each program has strength. Every project in every R&D program is evaluated together based on the DEA model for comparison of efficiency among different systems. Kruskal-Wallis test with a post hoc Mann-Whitney U test is then run to compare performance of R&D programs. Two alternative approaches to incorporating the importance of variables, the AR model and output integration, are also introduced. The results are expected to provide policy implications for effectively formulating and implementing national R&D programs. (C) 2008 Elsevier B.V. All rights reserved.Cherchye L, 2007, SOC INDIC RES, V82, P111, DOI 10.1007/s11205-006-9029-7Wang EC, 2007, RES POLICY, V36, P260, DOI 10.1016/j.respol.2006.11.004Swink M, 2006, J OPER MANAG, V24, P542, DOI 10.1016/j.jom.2005.09.004Eilat H, 2006, EUR J OPER RES, V172, P1018, DOI 10.1016/j.ejor.2004.12.001KOCHER MG, 2006, SOCIOECONOMIC PLANNI, V40, P314Garg KC, 2005, SCIENTOMETRICS, V65, P151, DOI 10.1007/s11192-005-0264-5LEE H, 2005, ASIAN J TECHNOL INNO, V13, P207FENG YJ, 2004, INT T OPERATIONAL RE, V11, P181, DOI 10.1111/j.1475-3995.2004.00450.xZhang AM, 2003, J COMP ECON, V31, P444, DOI 10.1016/S0147-5967(03)00055-6TAKAMURA Y, 2003, SOCIOECONOMIC PLANNI, V37, P85Paradi JC, 2002, IEEE T ENG MANAGE, V49, P161, DOI 10.1109/TEM.2002.1010884Korhonen P, 2001, EUR J OPER RES, V130, P121*OECD, 2001, OECD SCI TECHN IND SCOOPER WW, 2000, DATA ENVELOPMENT ANAADAMS JD, 2000, EC ECONOMETRICS INNORosen D, 1998, J PROD ANAL, V9, P205Allen R, 1997, ANN OPER RES, V73, P13Graves SB, 1996, TECHNOL FORECAST SOC, V53, P125Lee MS, 1996, RES POLICY, V25, P805BRINN T, 1996, ACCOUNT BUSINESS RES, V26, P265SHANG J, 1995, EUR J OPER RES, V85, P297NEDERHOF AJ, 1993, RES POLICY, V22, P353BOUSSOFIANE A, 1991, EUR J OPER RES, V52, P1THOMPSON RC, 1990, MEAS SCI TECHNOL, V1, P93BANKER R, 1989, RES GOVT NONPROFIT A, V5, P125DYSON RG, 1988, J OPER RES SOC, V39, P563NEDERHOF AJ, 1987, SCIENTOMETRICS, V11, P330BANKER RD, 1984, MANAGE SCI, V30, P1078BOUND J, 1984, R D PATENT PRODUCTIVSCHERER FM, 1983, INT J IND ORGAN, V1, P107SAATY TL, 1980, ANAL HIERARCHY PROCECHARNES A, 1978, EUR J OPER RES, V2, P429

Administrative and Technological Innovation: The Indirect Effects of Organizational Culture and Leadership

Administrative innovation is defined as changes to the rules and structures that characterize the communication methods and work of employees within an organization, and technological innovation refers to the implementation of programs and services. This study examines the relationship between administrative and technological innovation using two environmental factors, namely leadership and organizational culture, as indirect variables.Using structural equation modeling on a 2015 Korea Institute of Public Administration survey, this study finds that there are no direct effects between administrative innovation and technological innovation. However, results indicate that a strong organizational culture positively affects the relationship between administrative and technological innovation, and leadership in an organization plays a similar role to that of organizational culture. These findings suggest that changing the rules of are organization alone is not enough to lead to technological innovation, which must be supported by a strong culture and leadership.</p

Toward integration of products and services: Taxonomy and typology

Integration of products and services has been receiving increased attention from both practice and academia, but there is no common systematic framework that can accommodate various concepts. In response, this paper first defines an umbrella term, &quot;integrated product-service&quot; (IPS), that encompasses all related concepts. An extensive literature review is conducted, allowing the production of a taxonomy of IPS, called the IPS dichotomy. As a typology of IPS, the IPS cube, comprising eight cells, is also proposed along with practical examples. This paper is expected to lay a foundation for further advances in the field of integration of products and services. (C) 2012 Elsevier B.V. All rights reserved.N

Identifying core technologies based on technological cross-impacts: An association rule mining (ARM) and analytic network process (ANP) approach

This study proposes a new approach to identifying core technologies from a perspective of technological cross-impacts based on patent co-classification information with consideration of the overall interrelationships among technologies. The proposed approach is comprised of two methods: association rule mining (ARM) and the analytic network process (ANP). Firstly association rule mining (ARM) is employed to calculate the technological cross-impact indexes. Since the confidence measure in ARM is defined as a conditional probability between two technologies, it is adopted as an index for evaluating technological cross-impacts. The technological cross-impact matrix is then constructed with all calculated cross-impact indexes. Secondly. the ANP, which is a generalization of the analytic hierarchy process (AHP), is conducted to produce priorities of technologies with consideration of their direct and indirect impacts. The proposed approach can be utilized for technology monitoring for both technology planning of firms and innovation policy making of governments. A case of telecommunication technology is presented to illustrate the proposed approach.close