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IJTD Editorial Board Editor-in-Chief:

Ali Hussein Saleh Zolait, U. of Bahrain, Bahrain

Associate Editors:

Eshaa Mohammed Hamad Al-Khalifa, U. of Bahrain, Bahrain Samuel Fosso Wamba, NEOMA Business School, France Luke Houghton, Griffith U., Australia Abdul Razak Ibrahim, U. Malaya, Malaysia Minna Mattila, Laurea U., Finland

International Editorial Review Board: Mayyadah F. Abdulhaleem, U. of Bahrain, Bahrain Zaitun Abu Bakar, U. of Malaya, Malaysia Emad Ahmad Mohammed Abu-Shanab, Yarmouk U., Jordan Farooq Ahmed, National U. of Computer & Emerging Sciences, Pakistan Saleh Alwahaishi, King Fahd U. of Petroleum and Minerals, Saudi Arabia Wasan Shaker Awad, U. of Bahrain, Bahrain Patrick Chau, Hong Kong U., Hong Kong Shang Gao, Zhongnan U. of Economics and Law, China Güney Gürsel, GATA, Turkey Abdalla Hamed, U. of Wales Institute, UK Mohammad Hossain, North South U., Bangladesh Raymond Huang, Auckland U. of Technology, New Zealand Efosa C. Idemudia, Arkansas Tech U., USA Princely Ifinedo, Cape Breton U., Canada. Huma Javed, U. of Peshawar, Pakistan Hassan Yousif Kamal, U. of Bahrain, Bahrain Sherif Kamel, The American U. in Cairo, Egypt Rawan Tayseer Holo Khasawneh, Yarmouk U., Jordan Lutz M. Kolbe, Universität Göttingen, Germany

Sitwat Langrial, U. of Oulu, Finland Ewa Lechman, Gdansk U. of Technology, Poland Eldon Y. Li, Yuan Ze U., Taiwan Lei Li, Hefei U. of Technology, China Armindo Macedo, Cundus AG, Germany Christos Michalakelis, Harokopio U. of Athens, Greece Amine Nehari-Talet, King Fahd U., Saudi Arabia Krassie Petrova, Auckland U. of Technology, New Zealand Shana Ponelis, U. of Wisconsin-Milwaukee, USA Devendra Potnis, U. of Tennessee, Knoxville, USA Wali Rahman, U. of Malakand, Pakistan T. Ramayah, Universiti Sains Malaysia, Malaysia Umar Ruhi, U. of Ottawa, Canada Simone Sala, Università della Svizzera Italiana, Switzerland Noor Akma Mohd Saleh, U. of Malaya, Malaysia Khalid Soliman, Hofstra U., USA Craig Standing, Edith Cowan U., Australia Ainin Sulaiman, U. of Malaya, Malaysia Yuan Sun, Zhejiang Gongshang U., China Yun Wan, U. of Houston-Victoria, USA

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International Journal of Technology Diffusion January-March 2014, Vol. 5, No. 1

Table of Contents Research Articles 1

Optimal Introduction Timing Policy for a Successive Generational Product Deepti Aggrawal, Department of Operational Research, University of Delhi, Delhi, India Ompal Singh, Department of Operational Research, University of Delhi, Delhi, India Adarsh Anand, Department of Operational Research, University of Delhi, Delhi, India Mohini Agarwal, Department of Operational Research, University of Delhi, Delhi, India

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17 Mobile Government Services: Challenges and Opportunities Hassan Y. A. Abu Tair, Department of Computer Science, King Saud University, Riyadh, Saudi Arabia Emad A. Abu-Shanab, Department of MIS, Yarmouk University, Riyadh, Saudi Arabia 10.4018/ijtd.2014010102

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26 Investigation of Determinants of Total Factor Productivity: An Analysis of the Impact of Investments in Telecoms on Economic Growth in Productivity in the Context of Transition Economies Sergey Samoilenko, Department of Computer Science and Computer Information Systems, Averett Univeristy, Danville, VA, USA Kweku-Muata Osei-Bryson, Department of Information Systems, Virginia Commonwealth University, Richmond, VA, USA 10.4018/ijtd.2014010103

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43 Confirmative Pressures in ERP Institutionalisation Azadeh Pishdad, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, Australia Abrar Haider, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, Australia 10.4018/ijtd.2014010104

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56 A Comprehensive Summary Review of Internet Advertising and Online Market Places that Provides Detailed Insights and Understanding On What Information Systems Discipline is About Efosa Idemudia, Department of Business Data Analytics & Information Systems, Arkansas Tech University, Russellville, AR, USA 10.4018/ijtd.2014010105

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73 Competitive Strategies in the Computer Industry Shameem Akhter, Western Oregon University, Beaverton, OR, USA Nayem Rahman, Portland State University, Beaverton, OR, USA Mohammad Nirjhar Rahman, University of Rajshahi, Rajshahi, Bangladesh 10.4018/ijtd.2014010106

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Copyright

The International Journal of Technology Diffusion (IJTD) (ISSN 1947-9301; eISSN 1947-931X), Copyright © 2014 IGI Global. All rights, including translation into other languages reserved by the publisher. No part of this journal may be reproduced or used in any form or by any means without written permission from the publisher, except for noncommercial, educational use including classroom teaching purposes. Product or company names used in this journal are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. The views expressed in this journal are those of the authors but not necessarily of IGI Global.

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International Journal of Technology Diffusion, 5(1), 1-16, January-March 2014 1

Optimal Introduction Timing Policy for a Successive Generational Product Deepti Aggrawal, Department of Operational Research, University of Delhi, Delhi, India Ompal Singh, Department of Operational Research, University of Delhi, Delhi, India Adarsh Anand, Department of Operational Research, University of Delhi, Delhi, India Mohini Agarwal, Department of Operational Research, University of Delhi, Delhi, India

ABSTRACT Globalized economy has led firms to introduce new innovations in the market quite frequently. Optimal Introduction time is an important strategic decision for firms because an early introduction may not take off as customers; channel members and other required partners might not be receptive enough, on the other hand too late an entry results in loss of opportunity for the firm. The decision is even more critical when introducing successive generations over time. In this study, the authors have developed an analytical approach to help decide the optimal introduction time for successive generational product. The timing decision depends on whether firms push the product to market before competitors or invest more time in process & product design and improvement. The authors have examined the case where a firm introduces successive generations of a durable product for which demand is characterized by an innovation diffusion process. Results are supplemented by a numerical example. Keywords:

Globalized Economy, Innovation, Innovation Diffusion Process, Optimal Introduction Time, Successive Generation

INTRODUCTION Successful sequential products introduction contribute substantially to long-term financial success and are an effective strategy to increase primary demand. It strengthens the competitive position of the company in the market. Also, a new innovation can be an ideal tool to segment the market through sequential product releases. In today’s time most of the products and services we consume represent improved

versions of earlier generations, and today’s products will eventually be substituted by even newer generations (Jiang, 2012) Further, in a competitive market, firms are often forced to constantly improve their products in order to stay competitive. Therefore, continuous product improvement in the form of successive generations is frequently observed in the marketplace. When the time interval between technologies is short the earlier technology may continue to diffuse

DOI: 10.4018/ijtd.2014010101 Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

2 International Journal of Technology Diffusion, 5(1), 1-16, January-March 2014

through a population of potential buyers even as the substitution process is under way. Therefore, the demand for an earlier technology may continue to grow even as the substitution process occurs, as illustrated in Figure 1 (Norton-Bass, 1987). The figure depicts the sequential release of a product, which with time compete with the earlier available generation in the market to strengthen the competitive edge. Well known examples including Apple Inc.’s iPhone and iPad, and Microsoft’s Office and Windows lines of products (Jiang, 2012). Customers who want to use a product early generally tend to value them more and therefore are willing to pay more. For example, publishing companies conventionally release hardcover version of a book first at a higher price and follow it up with the lower-quality, lower-priced paperback version afterward. As the continuous innovation becomes the survival mantra for companies, many manufacturers introduce new innovations often by adding new variants into the basic product (Kapur, Chanda, Tandon, & Anand, 2010; Singh, Kapur, & Anand, 2011). Generally, in the initial phase of a product lifecycle the rates of extensions made

are typically slow but they tend to widen over time. The companies normally target consumer subgroups with new products customized to suit special needs. This practice is most visible in many consumer durable goods markets, such as the markets for consumer appliances and consumer electronics. But the risk is formidable as for most business organizations especially in high technology product market the development are associated with high cost and risks (Cooper, 1979; Cooper & Kleinschmidt, 1986) and failure may cause considerable financial loss and embarrassment to the firm. Thus, identification of factors that drive product outcome can help a firm to better utilize the resources committed to the product delivery process and increase the market demand for a firm’s new products (Zirger & Maidique, 1990). In recent years many research studies has been made to understand the apparent reason of high failure rates of new product in the market place (Dougherty, 1990; Cooper, 1993; Urban & Hauser, 1993; Sivadas & Dwyer, 2000; Ernst, 2002). These researches indicate that the commercial success of new products depends on how well the market opportunity has been

Figure 1. Series of technological generations (Source Norton-Bass, 1987)

Copyright © 2014, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


identified and how the complication of new product introduction timing is analyzed and incorporated into the decision making process. Thus, the companies need to judiciously balance the conflicting marketing requirements of variety and complexity of introduction of a new innovation to keep the failure risk as low as possible. Determining the optimal introduction timing of new products is a difficult task that becomes even more complex when the firm plans to introduce a product line consisting of a number of different product generations that will potentially compete with each other (Wilson & Norton, 1989, Moorthy & Png, 1992; Mahajan & Muller, 1996; Tyagia & Raju, 2006; Libai, Muller, & Peres, 2009). Often, the sale of a new generation comes from three major sources (Chanda & Bardhan, 2008; Kapur, Chanda, Tandon, & Anand, 2010): a. Switchers from the parent product to the new generation (i.e., cannibalization) b. From the repeat users and c. New customers to the product generation, attracted to the specific characteristics of the line extension. Thus, managers would like to reap the gains from both markets as early as possible to shrink the inter-release time. But the process is not always smooth and often managers have to answer the complex issues concerning the introduction time of an advance generation product in the market. The complexity mostly arises due to: 1. Introduction of a new generation more often than not cannibalizes the sales of the first (and higher-margin) generation product because, after the second generation product is released, many customers who would otherwise purchase the higher-end version would switch to the lower-margin version (Lehmann & Weinberg, 2000). 2. Shortening the inter-release time has a dynamic impact on consumer buying decisions. Even before the second generation is released many of the potential purchasers

of first generation product may delay their purchase and wait for the lower-priced version if they expect the second generation product to become available sooner. Thus, the decision to delay the release or premature introduction of advance generation is related to customer expectations and understanding the determinants of these expectations is important. Hence a decision model needs to be able to account for these tradeoffs in deciding on the optimal timing strategy. This paper considers a firms decision on the introduction timing for successive product generations. We examine the case where a firm introduces multiple generations (in this case we have taken two successive generations) of a durable product for which demand is characterized by a demand diffusion process. Here, First generation is the available generation in the market and Second generation is the new generation to be launched. Under the cost bound environment, the paper assumes that a second or an advance generation product will be introduced in the market once the cumulative sales of first or earlier generation product attain the desired goal. Therefore, a second generation product will be introduced only when the preceding generation has acquired its desired goal, i.e. it has captured its desired market. The present article is divided into the following sections: the upcoming Section comprises of literature survey to provide a theoretical background, then we discusses about the related theory in connection with the proposition, the later section comprises of cost centric model development, and then we present the role of promotional expenditure on new product introduction time. In later half numerical illustration has been provided in order to validate the proposal. Finally, the article concludes with a detailed managerial implication followed by conclusion and references.

LITERATURE REVIEW The basic idea behind the introduction of successive product generations is to capture the



benefits of advances made by the firm’s research and development department in terms of generating new features, enhancing the product design, producing new product technology etc. over time (Chanda, 2011). As discussed study of technology-market structure analysis involves describing how product generations compete against each other and often new innovation in the market doesn’t immediately replace the previous one that it intends to substitute, but starts to compete with it. Thus, it may be unreasonable to immediately release a new product generation after each new technology is produced. Rather, sometime it is optimal for the firm to delay an introduction until sufficient adopters have purchased the existing generation product in the marketplace to minimize the cannibalization effect. In practice, many different goals are connected to the introduction of product line development. For last two decades the optimal introduction time of sequential generation product has generated considerable research interest among the marketing scientists. Norton and Bass (1987, 1992) provided one of the earliest diffusion models that can describe the sales growth phenomenon for multiple generations competing in the market. The model later on extended by Wilson and Norton (1989) to study the one-time introduction decision for a new product generation. During model formulation the authors assumed that a new generation has fixed positive effects on market potential along with negative effects due to cannibalization. On their analysis Wilson and Norton (1989) concluded that it will either be optimal to introduce the improved product as soon as it is available or never at all (now or never rule). Purohit (1994) found that, if the firm can decide the extent of innovation, then a product replacement strategy is profitable than a line extension. Mahajan and Muller (1996) advocated the now or at maturity rule. They conclude that it will be optimal to either introduce the improved product as soon as it is available or when enough sales have been accumulated for the previous product generation. Cohen et al. (1996) assume that product can only be sold during a fixed window of time. Thus delaying the product introduction time

for further development can lead to a better product and will generate higher revenues over a period of time. Gjerde et al. (2002) studied a firm’s decisions on the level of innovation to incorporate into successive product generations. The academic literature about optimal introduction timing of successive-generation product is scattered and it shows some restricting views. For example, most of the prior analysis in this field is based on the assumption that the next product generation is available and remains unchanged regardless of when it is introduced. In contrast, we assume that a firm’s decision to holdup or premature release of the product-generation depends on the market share captured by existing generation product under cost criteria. In this paper we have considered that the firm will keep on investing money on promotion for the existing generation till the next generation is introduced or till the whole promotional budget is consumed. As such, delaying introduction of an advance generation to a later date may lead to minimizing the cannibalization effect.

THEORY DEVELOPMENT The preceding section describes two potentially similar theories for new product introduction timing (i.e. ‘now or never rule’ and ‘now or at maturity rule’). Both the theories have considered the role of advance generation in the decision making process irrespective of the availability of the product Mahajan and Muller (1996), Wilson and Norton (1989). Looking beyond these traditional approaches, in this paper we have proposed a different methodology to analyze the optimal introduction timing of next generation product. Under cost oriented environment (Garmabaki, Kapur, Singh, & Sanger, 2012), we have assumed that a firm’s decision to release next generation product depends on market-share achieved by the earlier generation product under the known budgetary constraints. In this following section, we develop several criteria that may be used to analyze the different situations that favor the optimal release time of new generation.



MODEL DEVELOPMENT

constant parameters m, p andq denote the po-

Before we introduce the mathematical model, we briefly describe the model assumptions used at the different stages of the model development process. Though the successive generations for a firm might be large enough in number, in order to get rid of the complexity related to modeling, we assume a case when the firm is introducing only two successive generations. The firm knows how the demand for the existing generation evolves over time and the firm understands how to allocate the promotional resources to achieve the desired target customers. We also assume that the firm is cost centric and the objective is to minimize the total costs of production & promotion over a period that starts at the introduction of the new product generation and which ends when a more advance product generation is introduced. During model development we further assume that in the cost model the second generation demand has no role to play as we are more concerned to find the optimal time to introduce the second generation product at the earliest stage of the first generation’s product life cycle. F.M. Bass proposed the basic mixed innovation diffusion model in 1969 (Bass, 1969; Kapur, Chanda, Tondon, & Anand, 2012; Singh, Anand, Kapur, & Aggrawal, 2012). This model is appropriate for a single generation production. The Bass model assumes that there exists a finite population of prospective adoption who with time increasingly adopt the product. F.M. Bass categorized potential adopters in two categories as innovators and imitators depending on which way they get information about the product. Innovators make independent purchase decisions and previous buyers influence imitators by word of mouth. The model can be represented by the following differential equation:  N (t ) dN (t )   (m − N (t )) = p + q  m  dt 

(1)

Where N(t) represents the cumulative number of adopters by time t, and the three

tential market size, the coefficient of innovation, and the coefficient of imitation, respectively. Equation (1) shows that the diffusion rate at a given time t is equal to the product of (i) the instantaneous probability of adoption at time t, which increases linearly with the number of existing adopters, and (ii) the number of potential adopters who have not adopted by time t. The cumulative number of adoptions and the non-cumulative diffusion rate at time t, denoted by S (t ) , can be derived based on Equation (2):      1 − e −(p +q )t    N (t ) = m   = m.F (t )  q  −(p +q )t     1 +  e  p   (2)    (p + q )2  1 − e −( p +q )t  S (t ) = m. . 2   −( p +q )t   p  1 + ( q / p ). e     (3) The density function (f(t)) or each period sales is obtained by differentiating (2) with respect to t, (i.e.),

f (t ) =

2  (p + q )  −(p +q )t e   p    2

  1 +  q e −(p +q )t      p  

where, F (t ) =

(4)

t

∫ f (t )dt is

the cumulative

0

likelihood of purchasing the product at time ‘ t ’. The above model enables the managers to solve an important concern of new products by determining the time to peak sales ( t * ) and magnitude ( S t * ). As derived by Bass (1969),

( )

the time to peak and magnitude is given by:



t* =

considers the information provided by the existing generation to determine the optimal time of introduction of the advance generation product.

q  ln   (p + q )  p  1

The Cost Model

and

( )

S t* =

m (p + q ) 4q 2

(5)

The inflection point for each period sales is obtained by differentiating (4) twice with respect to ‘t’ and solving for ‘t’, which yields (Mahajan, Muller & Srivastava, 1990) ∗∗ tleft =

q  1 ln   2 − 3 p + q  p 

∗∗ tright =

(

)

q  1 ln   2 + 3 p + q  p 

(

)

(6) (7)

As discussed the related literature that studies the demand for successive generations of product introduction (Norton & Bass, 1987, Wilson & Norton, 1989 etc.) are based on overlapping-generation diffusion curve that requires substantial amount of information about the advance generation product (Figure 2). The current paper contributes to this literature by proposing a modeling framework that explicitly

In this section, we describe the cost model. We introduce the model in the specific context of durable produces only; however, the modeling framework can be generalized to a broader range of marketing settings where consumers make decisions about related products that are sequentially released. The paper is based on the monopolistic assumption, where a firm introduces an advance generation product at time T . Each consumer needs at most one unit in that product category. We are interested in understanding the optimal time of offering the advance generation product in the market place under the cost constraint. Like done in a recent study by Garmabaki, Kapur, Singh and Sanger (2012), we have certain set of assumptions: C 1 be the cost of production of first generation product till the second generation product is introduced in the market-place (i.e. t ≤ T ). C 2 be the cost of production of first generation product after the introduction of second

Figure 2. Inflection points of Bass distribution curve



generation product in the market-place (i.e. t > T ). C 3 is the promotional cost per unit time for first generation product (i.e. t ≤ T ). N (T ) is the cumulative number of adopters of the first generation product till time T . n (T ) is the number of adopters at time ‘T ’. (See Figure 3). C (T ) is the total expenditure made by the firm on first generation product at time T . C B is the total budget allocated for the promotion of first generation product. N 0 is the target sale of the product after which the second generation will be introduced. Through out the analysis we have assumed thatC 2 > C 1 . The assumption is quite intuitive in nature. Till time ‘T ’ (i.e. before introduction of second generation product) the sales of first generation will be on the higher side as a result the cost per unit production decreases. But as the second generation product introduced at time ‘T ’, it will reduces the rate of adoption of first generation product considerably and that increases the per unit production cost drastically (see Figure 4). Under the model assumption and notation, the cost function C (T ) can be defined as (Garmabaki et al., 2012): C (T ) = C 1N (T ) + C 2 m − N (T ) + C 3T   (8) To determine the optimal timeT , the objective function can be written as

Now, (9) ⇒ Where

∂C = (C 1 − C 2 ) n (T ) + C 3 ∂T

dN (T )

m

= N / (T ) = n (T ) = dT 2  (p + q )  −(p +q )T e   p   

(10)

2

  1 +  q e −(p +q )T     p    Now,

C3 ∂C = 0 ⇒ n T* = C 2 −C1 ∂T

( )

(11)

Hence T* =

q  1 ln   p + q  p 

( )

Now, if n T * >

(12)

C3 C 2 −C1

then there ex-

ist T1 and T2 such that n (T1 ) = n (T2 ) where, T1 =

q  1 ln   2 − 3 p + q  p 

(

)

and

*

Min. C (T ) = C 1N (T ) +   C 2 m − N (T ) + C 3T       subject to  C 3T ≤ C B   N (T ) ≥ N 0 

T2 =

q  1 ln   2 + 3 p + q  p 

(

)

(13)

such that (9)



Figure 3. Shape of the adopters curve of Bass model

Figure 4. Cost function having both maxima and minima points



∂2C (T ) ∂T 2

T =T1

∂2C (T ) ∂T 2

     maxiima for cost function       > 0 T2 is the point of   minima for cost function   (14)

C (T2 ) then there exist two points Te and Tf

such that

C (0) = C (Te ) = C (Tf ) , then Te or Tf

Let us consider the promotional expenditure as C 3T , which is a linearly increasing function ofT . Also, If N (T ) ≤ N 0 , then there exists a positive and a unique solution T * = T0 satisfying:

is the optimal time to introduce the advance generation product (refer to Figure 5).

N (T ) = N 0

Now ifC (0) = C (Te ) = C (Tf ) , then by

for

using Lagrange’s mean value theorem, we get: N ' (T ) =

N (T ) − N (0) T −0

T >0 =

C3 C 2 −C1

If N (0) > N , then T * = 0 0

Applying the promotional expenditure constraint on the cost function, we have the following theorem:

where T = Te or

(17)

Tf

(15)

Theorem 2: If C (0) < C (T2 ) then there exists

a point TC such that C (TC ) = C (T2 ) , then TC is the optimal time to release the advance generation product (refer to Figure 6).

Now, since C (TC ) = C (T2 )

Theorem 3: If TP be the time point when the entire promotional budget has been consumed, i.e., at T = TP we haveC 3T = C B . Then, we have the following cases:

Case 1

( )

C3

then C (T ) will be inC 2 −C1 creasing function for 0 < T < ∞ , then If n T * ≤

⇒ (C 1 − C 2 ) N (TC ) + mC 2 + C 3TC =

(C

1

− C 2 ) N (T2 ) + mC 2 + C 3T2



Figure 5. Shape of cost function when C (0) > C (T2 )

Figure 6. Shape of cost function when C (0) < C (T2 )



1. If N (0) ≥ N 0 , then both the generation product should be introduced simultaneously i.e. T ∗ = 0 . 2. If N (∞) < N 0 , then Compare T0 and TP . a. IfT0 > TP , then we can conclude that the promotional budget is not sufficient enough to achieve the sales goal of first generation product to N 0 . So, the introduction of second generation has to be delayed till the target sales of first generation product have been achieved by investing more capital on promotional campaign. b. IfT0 < TP , then the second generation product should be introduced at timeT0 .

Case 2 n(T * ) > C cases:

C3 2

−C 1

, then we have the following

1. If T0 > Tp , Budget is insufficient to achieve the target sales of first generation product. So, the optimal decision is to hold the second generation product for some time till the first generation target sales N 0 has been achieved. 2. If T0 < Tp , then we have the following cases: a. If T0 < T2 < TP , then the optimal time to introduce the second generation product is T2 i.e. T * = T2 . b. If T0 < TP < T2 , then we have the three following cases: i. If T0 < TP < T1 , then introduce the second generation product at the time point T0 i.e. T ∗ = T0 . ii. If T1 < T0 < TP , then introduce the second generation product at the time point TP .

iii. If T0 < T1 < TP , then compare the total cost at the two time points (i.e. C (T0 ) and C (TP ) ) and whichever is minimum corresponding time point is optimal. a. If T2 < T0 < TP , then introduce the second generation product at the time point T0 .

NUMERICAL ILLUSTRATION AND SOME NEW RESULTS In this section, the property of the various optimal policies has been described on the proposed model using numerical example. The purpose of this study is to get some insight into the result and also to study the effect of advertising expenditure on the optimal policies of introduction of new generation product so that the overall expenditure can be minimized. To do so, we have considered DRAM’s dataset in our analysis. DRAMs are the highest volume commodity semiconductors built today, with about 11% of the total semiconductor market. It has shown clear discrete innovations in its product characteristics, especially in memory density. Also due to the PC boom and the growing need for memory in all information appliances, the DRAM sector became the lead product in the overall integrated circuit (IC) market in about 1990 (Victor and Ausubel 2001). In this section we have considered the sales data of 4K DRAM having 12 yearly observations for parameter estimation. The 4K DRAM-chipsets were introduced in the year 1974 and were in the market till 1985. The parameters present in equation (4) were estimated using SPSS. The results of the parameter estimation are given in Table 1. From Table 1, the worldwide estimated initial market size of 4K DRAM chipsets can be given as 314.05 (in million). From Equation (11) and using the estimated value of p and q , we have T1 = 1.098 and T2 = 15.292 and the point of inflection T * = 4.098 . Thus from (12) and estimated values of T1 and T2 we can



Table 1. Estimation result of 4K-DRAM dataset Parameter

Estimates

Standard Error

m

314.05

1.2703

p

0.01734

q

0.96303

= Rs 500 and N 0 =219.87 (= 0.7m ) i.e. before introduction of the second generation product the firm wants to capture the 70% market share of the first generation product. From a firm’s perspective it is important to set a target goal for the existing product and introduce the new generation product in the marketplace when the desired goal is achieved that will help to minimize the cannibalization effect. Thus, Equation (9) can be written as:

(18)

The objective here is to find the optimal value of T (i.e. the optimal time of introduction of second generation product) under the minimum cost criterion. ForT * = 4.098 , we have n T * = 78.35 . Hence, for this case

( )

C3 C 2 −C1

(= 1.67)

or Tf such that N ' (T ) =

C3 C 2 −C1

, where

T = T orT . e f The above condition will be satisfied, when T = 9.4 . e Now, Te or Tf will be the optimal time to introduce the second generation product if and only if, it satisfies the budgetary constraint. Now, from (18) we have Rs. 50 × 9.4 < Rs 500 . A l s o , a t Te or Tf = 9.4 , w e h a v e

N (T ) = 312.29 .

Thus, Te or Tf satisfies all the constraints

N (T ) ≥ 219.84

( )

0.9996

0.0246

the time pointT2 = 15.292 . The other base values considered in the paper are taken as follows: C 1 = Rs 150 C 2 = Rs 180 C 3 =Rs 50 C B

n T* ≥

R2

0.0013

conclude that the cost function will be maximum at the time point T1 = 1.098 and minimum at

Min C (T ) = 150N (T ) + 180  314.05 − N (T ) + 50T   s.t. 50T ≤ 500

Adjusted

a n d

C (0) = Rs 56, 529 andC (T2 ) = Rs 47872 .

of (17). Hence the second generation of DRAM i.e. 16K should be introduced at the time period 9.4. Furthermore, on solving target sales constraint and budgetary constraint, we find T0 = 4.987 and T = 10 respectively. We can P very well make out that these values satisfy case 2 of theorem 3; i.e. as T < T ⇒ T0 < TP < T2 which further imP 0 pliesT1 < T0 < TP . Utilizing this we see that when only the role of promotional effort is considered, the second generation can be introduced at time pointT = 10 . P

Thus by theorem 1, there exists two points Te



MANAGERIAL IMPLICATION During the initial period of the product life cycle the adoption may be slow with promoters trying for acceleration (intensity of diffusion). Later the rise in the curve is steep especially for successful products. But sales (profitability) of a technology product can be improved only till it attains its natural performance limit. When a technology starts approaching its natural limit of performance, a new technology needs to take over and begin its life cycle. Not all technology substitution becomes successful, making new products risky but necessary ventures for companies. Thus, from a managerial perspective, it is important to focus on the introduction timing for successive product generations. The decision to delay the release or premature introduction of advance generation is related to customer expectations and understanding the determinants of these expectations is important. The scope is quite large because it needs a decision model that account for these tradeoffs in deciding on the optimal timing strategy. The optimization model discussed in this article assumes that a second or an advance generation product will be introduced in the market once the cumulative sales of first or earlier generation product attain the desired goal. The theoretical results that were discussed in this article highlight the market characteristics of new products and the adoption process, particularly for successive generational products. They also suggest several policy implications for alternative entry time strategies. 1. Due to rapid technological progress in the recent past, it becomes imperative to understand the impact of a firm’s strategy on the introduction timing of a new product on consumers’ purchase decisions at the earliest stage of the product life cycle. The release policy of next-generation products should be decided keeping in mind the performance of products that are currently in the market that belong to the family and the attributes that indicate consumers’ expectations of future products.

2. Market cannibalization of the existing product by the advance generation product is one of the important issues in multiple generation diffusion that has been discussed widely in literature. The introduction of a successful next-generation product can have a significant effect on the market of the existing-generation product. Due to the lure of advanced features/utilities, people can skip the existing generation, thereby reducing the estimated market volume of the earlier generations’ products. From a management point of view it can be unreasonable to instantly release a new generation once it is developed. Lilly and Walters (1997) made an extensive study of the market cannibalization effect on technological products and suggested that it is better to delay the introduction timing of a new generation if the negative effects of cannibalization are too high. Thus from a managerial prospective it is important to set a target goal for the existing product and introduce the new generation product in the marketplace when the desired goal is achieved that will help to minimize the cannibalization effect. 3. The high technology market has become highly competitive and survival in this tough competition calls for frequent introduction of new product. Naturally it is the prime concern for every marketer to promote their brand as a shade better than the competitors. Proper promotional (advertising) plan can come handy in such circumstances. However, simply informing a customer that a brand exists is not enough. Promotions should be targeted towards the prospective audience in such a way that it forms a positive impact on the customer and in the process creates brand recognition. Thus it is important for management to plan promotional strategy keeping target audience in mind and at the same time management must make sure that promotional campaign creates a positive impression of the brand in the mind of the customers, creates a need in



them to try the brand and a commitment to continue using it. Another important issue a manager has to face is when to stop promoting a product? Ideally, a firm shouldn’t stop promoting in a specific time unless the product is limited. More prospects come everyday so stop promoting a product will lead a loss. But for fixed market size it is important to know when to stop otherwise it may delay the introduction time of the superior quality product in the market.

CONCLUSION Life-cycle management of high-technology products is a staggering task. In this perspective, the optimal introduction timing of upgrades of software products is one of the difficult decisions a manager must make. From a management point of view it is important to understand the optimal interval between upgrades that can help to manage R&D so that upgrades are ready on time, and marketing efforts are tailored to drive demand for new versions as they become available. The basic idea behind the introduction of successive product generations is to capture the benefits of advances made by the firm’s research and development department in terms of generating new features, enhancing the product design, producing new product technology etc. over time. Thus to get the competitive edge it is critical to know the optimal entry time of a new product. Too late an entry is likely to lead to significant loss of opportunity. On the other hand early introduction of a new product may not take off as customers, channel members, and other required partners might not be receptive enough. The timing decision depends on whether companies invest more time in product and process design and improvement or push the product to market before competitors. In recent years many research studies has been made to determine the optimal introduction timing of new products. But unfortunately most of the analyses in this field are based on some restricting assumptions e.g. most of the prior analysis assumed that the next product generation is

available and remains unchanged regardless of when it is introduced. In this paper a more general model has been developed based on simple assumptions, consistent with the basic innovation-diffusion modeling literature for the sales growth of a product. The optimization model proposed in the paper is based on the assumption that the firm will keep on investing money on promotion for the existing generation till the next generation is introduced or till the whole promotional budget is consumed. The theoretical contribution of the paper lies on the proposed modelling framework that explicitly considers the information provided by the existing generation to determine the optimal time of introduction of the advance generation product. During model development it has been assumed that in the cost model the second generation demand has no role to play as we are more concerned to find the optimal time to introduce the second generation product at the earliest stage of the first generation’s product life cycle. Under dynamic promotional cost, the paper assumed that a firm’s decision to release next generation product depends on market-share achieved by the earlier generation product under the known budgetary constraints. We have developed several criteria that may be used to analyze the different situations that favor the optimal release time of new generation. The optimization model discussed in the paper is of static nature. But due to globalize nature of the market, the decision approaches become highly dynamic. Thus, one obvious limitation of the paper is the ignorance of the dynamic nature of the optimal decision timing and can be the future scope for work. The per unit production costs considered in the analysis are constant in nature. But due to learning curve effect as cumulative adopters’ increases, learning and experience cause the cost per unit to decrease. So, it will be interesting to see whether the dynamic behavior of cost function have some influence on the introduction time of the advance generation product. Finally, the framework proposed in the paper can be extended for the introduction of sequential innovations of platform base products.



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Deepti Aggrawal obtained her B.Sc and M.Sc degree in 2007 and 2009 from University of Delhi, Delhi (INDIA). She was Operations Manager in Axis Bank till she joined as a research scholar in the Department of Operational Research in 2011. Her Research areas include Marketing and Software Reliability. She is a life member of SREQOM and has publications in journals of national and international repute. Ompal Singh is currently an Associate Professor in the Department of Operational Research, University of Delhi (INDIA). He has been active member of the Society for Reliability Engineering, Quality and Operations Management (SREQOM) since 2000. He has published several papers in International/National Journals and Proceedings. His research interests are in software testing, and software reliability engineering. He has recently developed interest in Innovation diffusion modelling and has published some papers in the same area. Adarsh Anand did his doctorate in the area of Innovation Diffusion Modeling and Software Reliability Assessment. Presently he is working as an Assistant Professor in the Department of Operational Research, University of Delhi (INDIA). He did his M Phil in Operational Research in 2010. He has publications in journals of national and international repute. He is a lifetime member of the Society for Reliability Engineering, Quality and Operations Management (SREQOM). Mohini Aggarwal obtained her M.Sc degree in Operational Research in 2012 from University of Delhi, Delhi (INDIA). She joined as a research scholar in the Department of Operational Research in 2012. Her Research areas include Marketing and Software Reliability.


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