Developing Evaluation Matrix for Critical Success Factors in Technology Forecasting

Abstract

The article aims to investigate the Critical Success Factors (CSFs) of technology forecasting. It also attempts to build a consensus from the previous work to develop an evaluation matrix of the same with a view to segregate these factors based on multiple perspective approach. The literature survey reveals that exiguous studies exist pertaining to the CSFs of technology forecasting, that too with a limited focus. Hence, the opportunity and need to write a research article in this domain became rather obvious. The article seeks to understand and identify the success factors of technology forecasting activities and how these factors can be put into practice to help the forecasting process. Some 44 variables contributing towards the technology forecasting success were collected and 16 critical factors were derived out of them, using intuitive judgement based on majority criterion. The analysis revealed that accuracy in forecasting followed by an understanding of the technological change, are the most critical factors in the success of technology forecasting.

Keywords

Technology forecasting technology forecasting success critical success factors intuitive judgement

Introduction

Any change, technical or non technical, is the law (rule) of nature that cannot be avoided by any means. Therefore, it becomes very much required by the organizations to adapt to such changes (Cyert and Kumar, 1994). At the same time, understanding the drivers of technology and technological change is a must, which is useful for practitioners and the researchers in almost every field including management (Raju, 2004). Forecasting plays a major role in adapting to these changes. Almost all the organizations around the globe require technology forecasting for their strategic planning. It becomes extremely important in those areas where there exist a high rate of technological change and various alternative technologies to choose from. This becomes even more significant when we talk about Information Technology (IT), telecommunications, health care, defence, media and other fields that are constantly changing and facing upheavals. It is an indispensable tool for determining future threats or exploring the possibilities for new technological capabilities.

Despite the benefits that could be derived from a successful technology forecasting, the evidences of high failure risk in forecasting future can also not be denied. Therefore, one of the major issues today is the study of the technology forecasting success.

The purpose of a good forecast is to facilitate the planning and the decision making process, therefore a forecast is simply valuable if it is informative and possibly leads to better decision making. A good forecast leads to decisions that minimizes the scope of the future surprises and inspires the organization, in turn industry, to make decisions with better outcomes in terms of reforming its investment strategies, pursuing research in a defined direction or reframing the policies to better prepare for the time to come. According to Vanston, the value and the success of a forecast rests upon the fact that the outcome of the decisions based on it must be better than that in its absence (Vanston, 2003). Of course, this necessitates taking utmost care in the process of forecasting, that can only develop confidence amongst its beneficiaries on the adopted methodologies and the implementations according to the outcomes of the forecast. Although timing is an extremely important factor while forecasting technology, it only assists in making decisions about when to start; in no way it promises success in the marketplace (Miller and Swinehart, 2010).

One of the approaches used to define and measure the technology forecasting success has been the Critical Success Factor (CSF) approach. CSFs are the few key areas of an activity in which the favourable results are absolutely necessary for a particular project to achieve its goals (Bullen and Rockart, 1981). Understanding these critical areas and managing them not only helps in reducing risk, but also provide guidance to the industry. The forecasting team has to work under the influence of these factors that in-turn affect the decisions of the planners that is responsible for the performance of the business operations and its sustainable competitive advantage.

However, relatively very few studies exist on the CSFs related to technology forecasting. Therefore, there exists an urgent need to identify amongst the various factors, the CSFs of technology forecasting.

The organization of the article is as follows. The first section of the article introduces the research problem being taken up. In the second section, success in technology forecasting along with the CSFs of it, is reviewed. The research objectives were derived in the third section, based on the gaps in the existing literature. The research methodology is being dealt in detail in the fourth section with a step-by-step approach: the first sub-section entirely deals with the identification and analysis of the CSFs of technology forecasting where as the CSFs with their respective citations is given in the second sub-section; the third sub-section presents an evaluation matrix for CSFs based on multiple perspective approach;. and subsequently the relevance of CSFs by perspective is shown in the fourth sub-section. The result and discussion of the overall analysis is summarized in the fifth section. The last section concludes the entire study.

Review of Literature

Although researchers and academicians have shared their views towards the success factors of technology forecasting activity, the numbers of success factors based studies, especially pertaining to CSFs on technology forecasting, are scanty.

The approach of CSF was popularized by Rockart and other researchers and is now increasingly being used by many, as an aid to planning an activity or project. Bullen and Rockart proposed five different sources of CSFs of any activity or project: the industry in which the organization operates, the competitive strategy pursued by the organization, the surrounding environmental factors, the temporal factors faced by the organization and the CSF that are individual centric (Bullen and Rockart, 1981). Other sources of CSFs involve analysis of the main competitors that leads the industry and their business evolution (Leidecker and Bruno, 1984).

The identification of CSFs takes place in the organization and can have remarkable impact on the organization’s success, when properly sustained and managed (Leidecker and Bruno, 1984). According to Evaristo, identification of CSFs could help in the accomplishment of competitive analysis for making strategic decisions (Evaristo, 1996). The identification of CSFs of technology forecasting assists in developing measures for these factors and seeking reports on them, defining the amount and the type of information to be collected and limits the collection of unnecessary data that is costly too (Rockart, 1979, p. 87).

The entire technology forecasting life cycle could be viewed as a socio-technical system involving organizations, academicians, researches and forecasting professionals using various techniques and methods of forecasting technology for a given time span. In his book, Linstone describes three perspectives: technical, organizational and personal, with which every socio-economic system may be viewed (Linstone, 1999). The technical perspective includes those factors that are mainly concerned with the techniques and methodologies developed and adopted. An organizational CSF is characterized by the extent to which the factor is associated with the organization or the industry it is applied to. These factors play a major role in the creation of a learning environment for the promotion of innovation (Dasgupta et al., 2011). The strategic perspective involves those success factors that play a major role in defining the firm’s strategy. On the other hand, tactical perspective describes how these goals could be met and when. Tactical CSFs require short to medium term planning often carried out at the middle management level (De Sousa, 2012).

The personal perspective relates the individuals to the entire system and is not confined to the organizational and technical perspectives. It believes that individuals can make a remarkable difference. Forecasting is a human activity usually carried by the individuals within or outside the industry (Bretschneider and Gorr, 1989). However, many studies have revealed that the main cause of failure in the new technological developments is none other than human (Eberts and Salvendy, 1986; Rasmussen, 1982). The cause of failure lies in the fact that the human elements are not given due considerations from the very initial planning phase to the execution phase.

A framework on the significance of technology forecasting and its CSFs is given by Abdullah as depicted in Figure 1 (Abdullah, 2007).

Figure 1.

Source: Adapted from the thesis of Abdullah (2007).

The study tries to fill the gap between the identification of the CSFs and the multiple perspectives with which these success factors could be viewed, to gain an insight regarding their relative importance and relevance, based on these perspectives.

Research Objectives

The review reveals that there exists an enormous literature pertaining to technology forecasting. But as far as success in technology forecasting is concerned, it is limited only to only few success factors. The article aims to identify almost all the success factors of technology forecasting and filter-out the critical ones to gain insight on their relevance, based on multiple perspectives. The article has the following objectives:

To explore the opinion of the experts towards the success factors of technology forecasting and filter out the CSFs out of it;

To present an evaluation matrix and the relevance of each of these CSFs based on multiple perspective approach; and

To see, how these CSFs are to be managed to enhance the technology forecasting activities.

Research Methodology

The entire research work is comprised of the four main phases discussed in detail in the coming sections:

Identification of CSFs.

Analysis of CSFs with their respective citations.

Development of an evaluation matrix for CSFs based on multiple perspective approach.

Relevance of CSFs based on multiple perspective approach.

Identification of CSFs

The number of studies to find the CSFs is many, as found in literature. A summary of few of the studies adopting some method to find CSFs are listed in Table 1. Every method has its own strengths and weaknesses.

As the literature review based identification of the CSFs had to be employed, a qualitative research approach is followed that provides an understanding of the problem through research instruments inclined towards determining the concerned area and have significance to the present work.

The entire research process towards the identification of CSFs was composed of the following three phases:

First Phase (Research Design Phase)

Definition of Research Subject and its Scope

The objectives of the study, that is, to identify and describe the CSFs of technology forecasting through the analysis of literatures/articles/research papers; to present an evaluation of CSFs matrix based on multiple perspective approach and to see, how these CSFs are to be managed to enhance the technology forecasting activities, are defined.

Table 1.

Most Commonly Used Methods to Find CSFs

Method	Reference
Action-research	Kock et al. (1999)
Group interviewing	Khandewal and Miller (1992)
Structured interviewing	Bullen and Rockart (1981)
Delphi technique	McCarthy and Atthirawong (2001); Brancheau et al. (1996)
Focus groups	Lawley et al. (2001)
Literature review	Esteves and Pastor (2000); Umble and Umble (2001)
Combination of methods	Khandewal and Ferguson (1999); Parr et al. (1999)
Scenario analysis	Barat (1992)
Multivariate analysis	Tishler et al. (1996)
Driver and constraint analysis	Vanston and Hodges (2009)
Case studies	Holland et al. (1999); Sumner (1999)

Source: From the thesis of José Manuel Esteves de Sousa (p. 52).

Collection and Analysis of literature

The second step involved the collection and analysis of the literatures pertaining to the success of technology forecasting. Sixty-one research papers dealing with the CSFs in general, and that of technology forecasting in particular, were made the basis of the research. The collected literature was undergone thoroughly to understand the underlying success factor(s) being discussed for the technology forecasting activities.

Second Phase (Data Collection Phase)

The collected literature was analyzed systematically to identify the common success factors being discussed among various authors. In total 44 factors were identified that were discussed by some or the other author(s). The success factors along with their citations are depicted in Table 2.

In addition to the previous classification, one more classification is given in Table 3 that gives the list of the authors along with the number of success factors that had been discussed by them in the literature. This classification laid emphasis on the number of varied success factors and the depth of the work done by the author in the field. In all, 47 authors were found during the review who talked about one or more success factors of technology forecasting.

Based on the intuitive judgement, the CSFs out of the various success factors found during the review, were identified. Using the majority criterion, the success factor discussed by three or more authors was taken as critical. Consequently, 16 factors out of 44 success factors were found to be critical (see Table 4).

Third Phase (Data Analysis Phase)

This phase consists of the conceptualization, division and organization of the data in a novel way. The technique used here is the intuitive judgement method. All the relevant literature and the research material were collected and then analyzed.

Table 2.

Success Factors with the Authors

S. No.	Success Factors	Author(s)/Year
1.	Degree of reliability and validity of the forecast	Ilonen et al. (2006); Kumar et al. (2002); Gordon and Helmer (1964); Yoo and Moon (2006)
2.	Start from a set of defined goals/Planning	Jantsch (1967); Vanston (2003)
3.	Avoidance of over optimism	Schnaars et al. (1993); Mishra et al. (2002)
4.	Careful, studied approach	Schnaars et al. (1993)
5.	Related cost	Golden et al. (1994); Fildes (2006); Jantsch (1967)
6.	Technical sophistication	Golden et al. (1994); Winebrake and Creswick (2003); Vanston (2003)
7.	Availability of accurate historical data	Golden et al. (1994); Aravantinosa and Fallah (2006); Vanston (2003); Wenrong et al. (2006); Meade and Islam (2006); Levary and Han (1995)
8.	Time horizons (forecasted period)	Golden et al. (1994); Ilonen et al. (2006); Gordon and Helmer (1964); Jantsch (1967)
9.	Amount of detail necessary	Golden et al. (1994)
10.	Accuracy in forecast	Golden et al. (1994); Eto (2003); Aravantinosa and Fallah (2006); Lin et al. (2010); Rowe and Wright (1999); Ilonen et al. (2006); Kumar et al. (2002); Yoo and Moon (2006); Meyer et al. (1999); Venkatesan and Kumar (2002); Madden and Tan (2007); Fildes (2006); Anderson et al. (2002); Barley (1998); De Gooijer and Hyndman (2006); Meade and Islam (2006); Levary and Han (1995)
11.	Timing of forecast	Golden et al. (1994); Vanston (2003); Jantsch (1967)
12.	Form. Who will use the data?	Golden et al. (1994); Ilonen et al. (2006)
13.	Whether the forecast supports decision making	Coates et al. (2001); Lemos and Porto (1998)
14.	Evaluate systematically all the related technologies	Winebrake and Creswick (2003)
15.	Good team work	Sripaipan (2006)
16.	Clear strategy	Wang and Chien (2006); Vanston (2003); Lemos and Porto (1998)
17.	Sufficient information resources	Wang and Chien (2006)
18.	Understanding the nature and evolution of technological change	Adomavicius et al. (2006); Cyert and Kumar (1994); Radojicic and Markovic (2009); Winebrake and Creswick (2003); Kayal (1999); Anderson et al. (2001); Coccia (2005); Barley (1998)
19.	Understanding the technology ecosystem as an interrelated set of technologies and ecosystem (social, economical, political, etc.)	Adomavicius et al. (2006); Aravantinosa and Fallah (2006); Winebrake and Creswick (2003); Phaal et al. (2004); Kuwahara (2001); Mishra et al. (2002); Jantsch (1967)
20.	Developing a forecasting method	Halal (2007); Durmusoglu and Dereli (2011); Ilonen et al. (2006); Kumar et al. (2002); Vanston (2003); Levary and Han (1995)
21.	Tracking the most critical trends over time	Halal (2007)
22.	Evaluating strategic alternatives	Halal (2007)
23.	Identifying present key technologies	Halal (2007); Winebrake and Creswick (2003); Levary and Han (1995)
24.	Analyzing critical issues	Halal (2007); Mishra et al. (2002)
25.	Degree of uncertainity	Cheng et al. (2008)
26.	Number of variables affecting the development of technology	Cheng et al. (2008); Martino (1999); Levary and Han (1995)
27.	Extent of data availability	Cheng et al. (2008); Ilonen et al. (2006); Vanston (2003); Levary and Han (1995)
28.	Degree of data validity	Cheng et al. (2008); Vanston (2003); Levary and Han (1995)
29.	Technological experience and insights of the forecaster	Miller and Swinehart (2010); Yoo and Moon (2006)
30.	Should support decision making	Coates et al. (2001)
31.	Collaborate with research and development institutions	Abdullah (2007)
32.	Training and education of forecasting techniques	Abdullah (2007)
33.	Practice of technology forecasting culture	Abdullah (2007)
34.	Development of range of possible outcomes/Scope	O’Connor (1978); Vanston (2003)
35.	Development of library and access system to ensure the availibity of forecast to all	Capon and Hulbert (1985)
36.	Promotion of innovation and R&D activities	Abdullah (2007)
37.	Due considerations to human elements	Rasmussen (1982); Eberts and Salvendy (1986)
38.	Maintain the current awareness of the concern area	Porter et al. (1991)
39.	Availability of identifiable number of experts in case of expert opinion	Porter et al. (1991)
40.	Use of non-perfect available information	Grubesic and Murray (2005)
41.	How well the underlying assumptions are fitted to the real data	Levary and Han (1995)
42.	Analysis of present technological capability	Yoon and Park (2004)
43.	Satisfy the objective of achieving technological competitiveness	Gerdsri and Kocaoglu (2007); Ilonen et al. (2006); Wang and Chien (2006); Lemos and Porto (1998)
44.	Accountability of the forecast	Fildes (2006)

CSFs with Their Respective Citations

Table 4 represents a list of CSFs of technology forecasting with their respective citations. Out of various CSFs listed, ‘accuracy’ in forecast is found to be the most critical having 17 citations followed by ‘understanding the nature and the evolution of technological change’ with eight citations. The other factors lie behind with less than eight citations in their names (see Table 4).

Table 3.

Success Factors Found in the Literature Review

S. No.	Author(s)	No. of Success Factors	S. No.	Author(s)	No. of Success Factors
1.	Abdullah (2007)	4	25.	Kuwahara (2001)	1
2.	Adomavicius et al. (2006)	2	26.	Lemos and Porto (1998)	3
3.	Anderson et al. (2001)	1	27.	Levary and Han (1995)	2
4.	Anderson et al. (2002)	1	28.	Lin et al. (2010)	1
5.	Aravantinosa and Fallah (2006)	3	29.	Madden and Tan (2007)	1
6.	Barley (1998)	2	30.	Martino (1999)	1
7.	Capon and Hulbert (1985)	1	31.	Meade and Islam (2006)	2
8.	Sripaipan (2006)	1	32.	Meyer et al. (1999)	1
9.	Cheng et al. (2008)	4	33.	Miller and Swinehart (2010)	1
10.	Coates et al. (2001)	2	34.	Mishra et al. (2002)	3
11.	Coccia (2005)	1	35.	O’Connor (1978)	1
12.	Cyert and Kumar (1994)	1	36.	Phaal et al. (2004)	1
13.	Durmusoglu and Dereli (2011)	1	37.	Porter et al. (1991)	2
14.	Eberts and Salvendy (1986)	1	38.	Radojicic and Markovic (2009)	1
15.	Eto (2003)	1	39.	Rasmussen (1982)	1
16.	Fildes (2006)	3	40.	Rowe and Wright (1999)	1
17.	De Gooijer and Hyndman (2006)	1	41.	Schnaars et al. (1993)	2
18.	Gordon and Helmer (1964)	2	42.	Vanston (2003)	9
19.	Grubesic and Murray (2005)	1	43.	Venkatesan and Kumar (2002)	1
20.	Halal (2007)	5	44.	Wang and Chien (2006)	3
21.	Ilonen et al. (2006)	7	45.	Winebrake and Creswick (2003)	5
22.	Jantsch (1967)	5	46.	Wenrong et al. (2006)	1
23.	Kayal (1999)	1	47.	Yoo and Moon (2006)	3
24.	Kumar et al. (2002)	3

Source: Authors’ computation.

Table 4.

The CSFs with Their Respective Citations

		Critical Success Factors
S. No.	Authors	Accuracy in Forecast	Understanding the Nature and Evolution of Technological Change	Understanding the Technology Ecosystem	Developing a Forecasting Method	Availability of Accurate Historical Data	Degree of Reliability and Validityof the Forecast	Time Horizons (Forecasted Period)	Extent of Data Availability	Satisfy the Objective of Technological Competitiveness	Related Cost	Technical Sophistication	Timing of Forecast	Identifying Present Key Technologies	Number of Variables Affecting the Development of Technology	Degree of Data Validity	Clear Strategy
1.	Aravantinosa and Fallah (2006)	✓		✓		✓
2.	Adomavicius et al. (2006)		✓	✓
3.	Anderson et al. (2001)		✓
4.	Anderson et al. (2002)	✓
5.	Barley (1998)	✓	✓
6.	Cheng et al. (2008)								✓						✓	✓
7.	Coccia (2005)		✓
8.	Cyert and Kumar (1994)		✓
9.	Durmusoglu and Dereli (2011)				✓
10.	Eto (2003)	✓
11.	Fildes (2006)	✓									✓
12.	Gerdsri and Kocaoglu (2007)									✓
13.	Golden et al. (1994)	✓				✓		✓			✓	✓	✓
14.	De Gooijer and Hyndman (2006)	✓
15.	Gordon and Helmer (1964)						✓	✓
16.	Halal (2007)				✓									✓
17.	Ilonen et al. (2006)	✓			✓		✓	✓	✓	✓
18.	Jantsch (1967)							✓			✓		✓
19.	Kayal (1999)		✓
20.	Kumar et al. (2002)	✓			✓		✓
21.	Kuwahara (2001)			✓
22.	Lemos and Porto (1998)									✓							✓
23.	Levary and Han (1995)	✓			✓	✓			✓						✓	✓	✓
24.	Lin et al. (2010)	✓
25.	Madden and Tan (2007)	✓
26.	Martino (1999)															✓
27.	Meade and Islam (2006)	✓				✓
28.	Meyer et al. (1999)	✓
29.	Mishra et al. (2002)			✓
30.	Phaal et al. (2004)			✓
31.	Radojicic and Markovic (2009)		✓
32.	Rowe and Wright (1999)	✓
33.	Vanston (2003)				✓	✓			✓			✓	✓				✓
34.	Venkatesan and Kumar (2002)
35.	Wang and Chien (2006)									✓
36.	Winebrake and Creswick (2003)		✓	✓								✓		✓
37.	Wenrong et al. (2006)					✓
38.	Yoo and Moon (2006)	✓					✓
	Total no. of citations	17	8	7	6	6	4	4	4	4	3	3	3	3	3	3	3

Source: Authors’ computation.

In a nutshell, the above distribution of citations could be seen in Table 5 and in the subsequent pie-chart (Figure 2).

Table 5.

CSFs with No. of Citations

Critical Success Factor	Name	No. of citations	% of all citations
Accuracy in forecast	CSF₁	17	42
Understanding the nature and evolution of technological change	CSF₂	8	21
Understanding the technology ecosystem	CSF₃	7	18
Developing a forecasting method	CSF₄	6	16
Availability of accurate historical data	CSF₅	6	16
Degree of reliability and validity of the forecast	CSF₆	4	11
Time horizons (forecasted period)	CSF₇	4	11
Extent of data availability	CSF₈	4	11
Satisfy the objective of technological competitiveness	CSF₉	4	11
Related cost	CSF₁₀	3	8
Technical sophistication	CSF₁₁	3	8
Timing of forecast	CSF₁₂	3	8
Identifying present key technologies	CSF₁₃	3	8
Number of variables affecting the development of technology	CSF₁₄	3	8
Degree of data validity	CSF₁₅	3	8
Clear strategy	CSF₁₆	3	8

Source: Authors’ computation.

Figure 2.

Source: Authors’ own.

Evaluation Matrix for CSFs Based on Multiple Perspective Approach

As per the second section ‘Literature Review’, it is observed that these CSFs of technology forecasting can always be viewed through a multiple perspective: technical, organizational and personal perspectives with a cross-over of strategic and tactical approaches.

Thus, the CSFs of technology forecasting that were identified during review were categorized, based on these perspectives. Depending upon the above classification, the evaluation matrix of CSFs of technology forecasting based on multiple perspective approach is presented in Table 6.

It is evident from the above matrix that the technical aspects related to forecasting that are strategic in nature, contributes more towards the success of a technological forecasting activity. On the other hand, the organizational aspects that are tactical in nature are also major contributors towards a technology forecasting success.

Relevance of CSFs by Multiple Perspectives

The CSFs were arranged in accordance with the number of citations found in the literature review with the related perspective (see Table 7). Table 7 shows that the technical aspects that are mainly concerned with the methodologies and techniques developed and adopted for the implementation of the forecasting activities, contributes significantly to the success of technology forecasting as compared to the organizational and the personal aspects, in the process of technological forecasting. Given a technology forecasting project, the extent of accuracy in forecasting appears to be the most important success factor.

Table 6.

Evaluation Matrix for CSFs Based on Multiple Perspective Approach

	Strategic	Tactical
Technical	Accuracy in forecastTechnical sophisticationDeveloping a forecasting methodTime horizons (forecasted period)	Number of variables affecting the development of technology
Organizational	Timing of forecastClear strategySatisfy the objective of achieving technological competitiveness	Related costExtent of data availabilityDegree of data validityDegree of reliability and validity of the forecastAvailability of accurate historical data
Personal	Understanding the nature and evolution of technological changeUnderstanding the technology ecosystem as an interrelated set of technologies and ecosystem (social, economical, political, etc.)	Identifying present key technologies

Source: Authors’ computation.

Table 7.

CSF Literature Relevance by Perspective

Perspective		CSFs	Relevance
Technical	Strategic	Accuracy in forecast	17
		Developing a forecasting method	6
		Time horizons (forecasted period)	4
		Technical sophistication	3
	Tactical	Number of variables affecting the development of technology	3
Organizational	Strategic	Timing of forecast	3
		Clear strategy	3
		Satisfy the objective of achieving technological competitiveness	4
	Tactical	Availability of accurate historical data	6
		Degree of data validity	3
		Related cost	3
		Degree of reliability and validity of the forecast	4
		Extent of data availability	4
Personal	Strategic	Understanding the nature and evolution of technological change	8
		Understanding the technology ecosystem as an interrelated set of technologies and ecosystem (social, economical, political, etc.)	7
	Tactical	Identifying present key technologies	3

Source: Authors’ computation.

Result and Discussion

Out of various CSFs established, accuracy in forecast stands high on the list followed by understanding the nature and evolution of technological change, understanding the technology ecosystem, developing a forecasting method and availability of accurate historical data.

The article attempts to explore the expert’s opinion towards the success factors of technology forecasting activities through an extensive literature survey, and identifies various CSFs out of it. The identified CSFs need to be given due consideration while going for any forecasting activity.

The relevance of these CSFs based on multiple perspectives is also given to understand the criticality of these factors based on various domains: technical, organizational and personal with a cross-over of strategic and tactical.

But the question arises that if one wishes to improve upon the accuracy in forecasting, the first and foremost condition is to measure that accuracy. The forecast accuracy can only be determined by evaluating the fitness of a model on the new data that was not used while fitting the model. As far as developing a method of forecasting is concerned, a combination of qualitative together with the quantitative methods helps in improving accuracy in the forecast. It is evident that doing so would again generate a complex set of problems consuming a lot of resources. One of the practical ways to improve upon the forecast reliability and avoiding large errors is to combine the forecasting results obtained by various methods. Accuracy highly depends upon the forecasting horizon—the greater the forecasting horizon, the less accurate the forecast; but it is also dependent upon the aggregation level—the greater the aggregation level, the more accurate the forecast. For short term forecasts using qualitative methods, the accuracy could be enhanced by the recalculating several times to tune the resulting model into an interactive mode for a better fit into the reality. The data mining area got well known interactive techniques for tuning the equations of the forecast. Accuracy can be improved by identifying the sustained and ongoing trends in the fundamental technologies where complex applications and declining costs are enabled due to increasing capabilities and increasing competition, respectively.

The organization must capture future focused data related to customers, strategy, finance, employees and finally technology, well on time. The credibility of a statistical data could be enhanced by assessing it against its source reliability, currency, completeness, potential biases, gathering technique and relevance. For the opinion derived by experts, qualification of experts, understanding of potential biases and a balance in their opinion is a must to increase its value. For improving completeness and avoiding missing relevant information, the forecast should be generated using a range of data and methodologies.

In our view, techniques are not confined to themselves. The successful application of various techniques of forecasting technology relies heavily upon the experience and the insights of its users. To succeed in this high-risk environment, besides setting strong strategic course, a capability to alter that course with the shifting competitive environment, is also a must. Altering the strategy ‘on the fly’ again requires imagination and creativity. Involvement of the right people in the entire forecasting life cycle who have enough useful information, intelligence and experience to input into the forecasting activity, is indispensable.

All the parties concerned, must understand the ultimate deliverables or outcomes of the technology forecast. In most of the cases, the forecaster and the decision maker are two different entities. To ensure success of the forecast, the decision maker needs to be aware of the underlying assumptions. This could be achieved in the best possible way, by involving decision maker in the forecasting process.

The overall findings suggest that more training on qualitative as well as qualitative techniques of forecasting should be given by the organizations. The organizations may collaborate with the research institutions or the universities to enhance its research and development capabilities. Moreover, it should be practised as an organization’s culture.

Conclusion

In today’s high velocity environment, the relevance of technology forecasting is undoubtedly greater than ever before. Therefore, a need of more efficient, more effective and result oriented forecast cannot be denied. To achieve such a forecast, one of the issues that needs due concern and proper addressing is the understanding of the CSFs of technology forecasting and its relevance through a multiple perspective approach. This issue is focussed through this article that has attempted to identify the CSFs of technology forecasting that should always be borne in mind while going for any forecasting activity. To ensure accuracy that is the most CSF of technology forecasting, understanding the nature and evolution of technological change, understanding the technology ecosystem, developing a forecasting method and availability of accurate historical data, is also a must. Lastly, a systems approach is inevitable for the precision of a technology forecast. For any technology forecast, a multi-screen analysis should be recognized at different levels: sub-micro level related to product, process and technology, micro level related to the organization/industry, macro level related to economy/finance of the country, super-macro level related to environment/demographics, etc.

References

Abdullah

N. N. Y.

(2007). Critical success factors in technology forecasting activities. Melaka.: Institute of Technology Management and Entrepreneurship, Universiti Teknikal Malaysia

Adomavicius

Bockstedt

Gupta

Kauffman

R. J.

(2006). Understanding patterns of technology evolution: An ecosystem perspective. Proceedings of the 39th Annual Hawaii International Conference on System Sciences (Vol. 8), p. 189.1.

Anderson

Fare

Grosskopf

Inman

Song

(2002). Further examination of Moore’s law with data envelopment analysis. Technological Forecasting & Social Change, 69(5), 465–477.

Anderson

T. R.

Hollingsworth

Inman

(2001). Assessing the rate of change in the enterprise database system market over time using DEA. Portland International Conference on Management of Engineering and Technology, 2001. PICMET’01 (Vol. 2), pp. 384–390.

Aravantinosa

Fallah

M. H.

(2006). A methodology to improve the mobile diffusion forecasting: The case of Greece. Working paper, Proceedings of ISPIM 2006 Conference—‘Networks for Innovation’. Athens, Greece.

Barat

(1992). Scenario playing for critical success factor analysis, Journal of Information Technology, 7, 12–19.

Barley

S. R.

(1998). What can we learn from the history of technology? Journal of Engineering and Technology Management, 15(4), 237–255.

Brancheau

Janz

Wetherbe

(1996). Key issues in information systems management: 1994–95 SIM Delphi result. MIS Quarterly, 20(2), 225–242.

Bretschneider

S. I.

Gorr

W. L.

(1989). Special issue on public sector forecasting. International Journal of Forecasting, 5(3), 303–304.

10.

Bullen

C. V.

Rockart

J. F

(1981). A Primer on Critical Success Factors, Working Paper No. 69, Center for Information Systems Research, Sloan School of Management, Massachusetts Institute of Technology (MIT).

11.

Capon

Hulbert

J. M.

(1985). The integration of forecasting and strategic planning. International Journal of Forecasting, 1(2), 123–133.

12.

Cheng

A. C.

Chen

C. J.

Chen

C. Y.

(2008). A fuzzy multiple criteria comparison of technology forecasting methods for predicting the new materials development. Technological Forecasting & Social Change, 75(1), 131–141.

13.

Coates

Farooque

Klavans

Lapid

Linstone

H. A.

Pistorius

, . (2001). On the future of technological forecasting, Technological Forecasting and Social Change, 67(1), 1–17.

14.

Coccia

(2005). Measuring intensity of technological change: The seismic approach. Technological Forecasting & Social Change, 72(2), 117–144.

15.

Cyert

R. M.

Kumar

(1994). Technology management and the future. IEEE Transactions on Engineering Management, 41(4), 333.

16.

Dasgupta

Gupta

R. K.

Sahay

(2011). Linking technological innovation, technology strategy and organizational factors: A review. Global Business Review, 12(2), 257–277.

17.

De Gooijer

J. G.

Hyndman

R. J.

(2006). 25 years of time series forecasting. International Journal of Forecasting, 22(3), 443–473.

18.

De Sousa

J. M. E.

(2012). Definition and analysis of critical success factors for ERP implementation projects. Ph.D. Universitat Politècnica de Catalunya, Barcelona, Spain. Retrieved 11 July 2012, from http://jesteves.com/Tesis_phd_jesteves.pdf

19.

Durmusoglu

Dereli

(2011). Proceedings of PICMET’ 11 on the Technology Forecasting Using Data Envelopment Analysis (TFDEA): Technology Management in the Energy Smart World (PICMET).

20.

Eberts

Salvendy

(1986). The contribution of cognitive engineering to the safe design and operation of CAM and robotics. Journal of Occupational Accidents, 8(1–2), 49–67.

21.

Eto

(2003). The suitability of technology forecasting/foresight methods for decision systems and strategy: A Japanese view. Technological Forecasting & Social Change, 70(3), 231–249.

22.

Esteves

Pastor

(2000). Towards the unification of critical success factors for ERP implementations, 10th Annual BIT conference, Manchester, UK.

23.

Evaristo

J. R.

(1996). Records of the discipline ‘Sistemas de Inteligência Competitiva’. Classes were given at Universidade Federal do Rio Grande do Sul.

24.

Fildes

(2006). The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion. International Journal of Forecasting, 22(3), 415–432.

25.

Gerdsri

Kocaoglu

D. F.

(2007). An analytical approach to building a Technology Development Envelope (TDA) for road mapping of emerging technologies: A case study of emerging electronic cooling technologies for computer servers. International Journal of Innovation and Technology Management, 4(2), 121–135.

26.

Golden

Milewicz

Herbig

(1994). Forecasting: Trials and tribulations. Management Decision, 32(1), 33–36.

27.

Gordon

T. J.

Helmer

(1964). Report on a long range forecasting study. Santa Monica, CA: RAND Corporation.

28.

Grubesic

T. H.

Murray

A. T.

(2005). Geographies of imperfection in telecommunication analysis. Telecommunications Policy, 29(1), 69–94.

29.

Halal

W. E.

(2007). Forecasting the technology revolution. Emerald Group Publishing Limited, 8(6), 382–386.

30.

Holland

Light

Gibson

(1999). A critical success factors model for enterprise resource planning implementation, European Conference on Information Systems (ECIS), Copenhagen, Denmark.

31.

Ilonen

Kamarainen

J. K.

Puumalainen

(2006). Toward automatic forecasts for diffusion of innovations. Technological Forecasting & Social Change, 73(2), 182–198.

32.

Jantsch

(1967). Technological forecasting in perspective. Paris: Organization for Economic Cooperation and Development (OECD).

33.

Kayal

(1999). Measuring the pace of technological progress: Implications for technological forecasting. Technological Forecasting and Social Change, 60(3), 237–245.

34.

Kock

Jenkins

Wellington

(1999). A field study of success and failure factors in asynchronous groupware supported process improvement groups. Business Process Management Journal, 5(3), 238–253.

35.

Khandewal

Miller

(1992). Information System Study. In: Opportunity Management Program. New York: IBM Corporation.

36.

Khandewal

Ferguson

(1999). Critical success factors (CSF) and the growth of IT in selected geographic regions, Hawaii International Conference on System Sciences(HICSS).

37.

Kumar

Nagpal

Venkatesan

(2002). Forecasting category sales and market share for wireless telephone subscribers: A combined approach. The International Journal of Forecasting, 18(4), 583–603.

38.

Kuwahara

(2001). Technology foresight in Japan—The potential and implications of Delphi approach. International Conference on Technology Foresight, Tokyo.

39.

Lawley

Summers

Koronios

Gardiner

(2001). Critical Success factors for regional community portals: A preliminary model, In Sylvie Chetty & Brett Collins (Eds) Bridging Marketing Theory & Practice - ANZMAC 2001. Australian & NZ Marketing Academy.

40.

Leidecker

J. K.

Bruno

A. V.

(1984). Identifying and using critical success factors. Long Range Planning, 17(1), 23–32.

41.

Lemos

A. D.

Porto

A. C.

(1998). Technological forecasting techniques and competitive intelligence: Tools for improving the innovation process. Industrial Management & Data Systems, 98(7), 330–337.

42.

Levary

R. R.

Han

(1995). Choosing a technological forecasting method. Industrial Management, 37(1), 14–18.

43.

Lin

C. C.

Tang

Y. H.

Shyu

J. Z.

(2010). Combining forecasts for technology forecasting and decision making. Journal of Technology Management in China, 5(1), 69–83.

44.

Linstone

H. A.

(1999). Decision making for technology executives: Using multiple perspectives to improve performance. Norwood, MA: Artech House.

45.

Madden

Tan

(2007). Forecasting telecommunications data with linear models. Telecommunications Policy, 31(1), 31–44.

46.

Martino

J. P.

(1999). Thirty years of change and stability. Technological Forecasting and Social Change, 62(1–2), 13–18.

47.

McCarthy

Atthirawong

(2001). Critical Factors in International Location Decisions: A Delphi Study, 12th Annual Conference of the Production and Operations Management Society, Orlando (USA).

48.

Meade

Islam

(2006). Modeling and forecasting the diffusion of innovation—A 25 year review. International Journal of Forecasting, 22(3), 519–545.

49.

Meyer

P. S.

Yung

J. W.

Ausubel

J. H.

(1999). A primer on logistic growth and substitution. Technological Forecasting and Social Change, 61(3), 247–271.

50.

Miller

P. E.

Swinehart

K. D.

(2010). Technological forecasting: A strategic imperative. Journal of Global Business Management, 6(2), 1–5.

51.

Mishra

Deshmukh

S. G.

Vrat

(2002). Matching of technological forecasting technique to a technology. Technological Forecasting & Social Change, 69(1), 1–27.

52.

O’Connor

(1978). Planning under uncertainty: Multiple scenarios and contingency planning. A Research Report from the Conference Board’s Division of Management Research (Conference Board Report No. 741), New York.

53.

Parr

Shanks

Darke

(1999). Identification of necessary factors for successful implementation of ERP Systems. In New Information Technologies in Organizational Processes—Field Studies and Theoretical Reflections on the Future of Work (pp. 99–119). Kluwer Academic Publishers.

54.

Phaal

Farrukh

C. J. P.

Probert

D. R.

(2004). Technology roadmapping—A planning framework for evolution and revolution. Technological Forecasting and Social Change, 71(1), 5–26.

55.

Porter

A. L.

Roper

A. T.

Mason

T. W.

Rosini

F. A.

Banks

(1991). Forecasting and management of technology. New York, NY: John Wiley and Sons.

56.

Radojicic

V. D.

Markovic

G. Z.

(2009). New technology forecasting using the bass model. 9th International Conference on Telecommunication in Modern Satellite, Cable, and Broadcasting Services, 2009. TELSIKS ’09, pp. 277–280.

57.

Raju

B. Y.

(2004). Book review: The dynamics of technology: Creation and diffusion of skills and knowledge. Global Business Review, 5(2), 241–243.

58.

Rasmussen

(1982). Human errors. A taxonomy for describing human malfunction in industrial installations. Journal of Occupational Accidents, 4(2–4), 311–333.

59.

Rockart

(1979). Chief executives define their own information needs. Harvard Business Review, 57(2) 81–92.

60.

Rowe

Wright

(1999). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15(4), 353–375.

61.

Schnaars

Chia

Maloles

(1993). Five modern lessons from a 55-year-old technological forecast. Journal of Product Innovation Management, 10(1), 66–74.

62.

Sripaipan

(2006). Foresight in a non-profit organization: A case of the technology promotion association (Thailand-Japan). Second International Seville Seminar on Future-Oriented Technology Analysis: Impact of FTA Approaches on Policy and Decision-Making, Seville, pp. 28–29.

63.

Sumner

(1999). Critical success factors in enterprise wide information management system projects, Americas Conference on Information Systems (AMCIS), Milwaukee, USA.

64.

Tishler

Dvir

Shenhar

Lipovetsky

(1996). Identifying critical success factors in defense development projects: A multivariate analysis. Technological Forecasting and Social Change, 51(2), 151–171.

65.

Umble

(2001). Enterprise resource planning systems: A review of implementation issues and critical success factors, 32nd Decision Sciences Institute Annual Meeting, San Francisco, USA.

66.

Vanston

J. H.

(2003). Better forecasts, better plans, better results. Research Technology Management, 46(1), 47–58.

67.

Vanston

L. K.

Hodges

R. L.

(2008). Forecasts for the US telecommunications network. Telektronnik, 104(3/4), 18–28.

68.

Venkatesan

Kumar

(2002). A genetic algorithms approach to growth phase forecasting of wireless subscribers. International Journal of Forecasting, 18(4), 625–646.

69.

Wang

T. Y.

Chien

S. C.

(2006). Forecasting innovation performance via neural networks—A case of Taiwanese manufacturing industry. Technovation, 26(5–6), 635–643.

70.

Wenrong

Xie

Tsui

(2006). Forecasting of mobile subscriptions in Asia pacific using bass diffusion model. IEEE International Conference on Management of Innovation and Technology (Vol. 1), pp. 300–303.

71.

Winebrake

J. J.

Creswick

B. P.

(2003). The future of hydrogen fueling systems for transportation: An application of perspective-based scenario analysis using the analytic hierarchy process. Technological Forecasting & Social Change, 70(4), 359–384.

72.

Yoo

S. H.

Moon

H. S.

(2006). An estimation of the future demand for portable internet service in Korea. Technological Forecasting and Social Change, 73(5), 575–587.

73.

Yoon

B. G.

Park

Y. T.

(2004). Morphology analysis approach for technology forecasting. IEEE International Proceedings on Engineering Management Conference (Vol. 2), pp. 566–570.