Open access

A Dynamic Resource Allocation on Service Supply Chain

Written By

Soo Wook Kim and Kanghwa Choi

Published: February 1st, 2008

DOI: 10.5772/5351

Chapter metrics overview

3,149 Chapter Downloads

View Full Metrics

1. Introduction

Recently, as internet business is growing rapidly, it has been bringing a lot of changes in many aspects of commercial business activities, and especially internet shopping malls, dealing with individual consumers, have continued radical changes and development. Thus, internet-based companies are required to implement variously different and new approaches in the ways of conducting their businesses. Therefore, it is becoming one of the most significant strategic elements for internet-based companies to correctly understand the purchase patterns of individual consumers and properly provide business environments that can give the maximum satisfactions to those individual consumers in service supply chain.

However, despite this radical change in the environment of internet shopping, it is true that most studies on internet-based businesses so far tend to just focus on such studies as the overall size of internet business, investigation on current market status, and practical study on consumer buying patterns from the perspective of consumers, lacking studies in such fields as the strategic management method from the perspective of companies and business investment decisions. Not to mention the lack of such studies as how the shopping mall management strategies, currently being used by the majority of internet-based companies, influence attracting customers to make purchase and customer satisfaction, and how customers’ reactions from these strategies are affecting the management outcomes of business entities. Having these as the background in this study, the structure and the strategy of internet-based companies are analyzed based on dynamic speculations which emphasize an internally circulating feedback structure, as getting away from the currently existing linear speculations. Also, a speculative framework for decision makers to learn and understand the behavioral mechanism of the buying decision process is provided by building a system dynamics simulation model which can support long term decision making in the overall speculation instead of the direction of partial and short term oriented approaches. In doing so, the study intends to derive the most efficient investment decision support model for internet-based companies to operate their internet shopping malls in an overall way by understanding the correlation and interdependency among political variables, being needed for the operation of internet-based companies.

For this purpose, after going through the systematic classification and the analysis of existing literature and documentary records related to the internet shopping mall, in this research, four elements - internet advertisement, price reduction, web contents and web server - are derived as the primary factors influencing the buying decision before purchase. And the quality of delivery service and the service of customer support are selected as the variables influencing customer satisfaction and repeat purchases after initial purchase. By doing so, a general business model of internet shopping malls is developed. Additionally, the system dynamics model, which can support decision making for dynamic resource distribution of the overall internet shopping mall, is developed by combining the circulating interactions of cause and effect of individual feedback loop on each influencing variables and the feedback loop of the profit structure of internet shopping mall. In order to evaluate the validity of this dynamic resource distribution model, an internet bookstore, which is one of the most common types of internet shopping malls, is selected as a sample case.

Statistical Approach System Dynamics Approach
1. way of Inference Previous empirical data Logical causal relationships among variables
2. Analysis Subject Static behavior Dynamic behavior
3. Analysis Focus Correlational Relationships Feedback Relationships
4. Analysis Purpose Numerical Accuracy Structural Accuracy
5. Prediction Short-term Prediction Long-term Prediction
6. The Experiment of Various Policies Difficult Easy

Table 1.

A comparison of existing research method and system dynamics method on the internet shopping.

This study analyzes the Internet utilization pattern of customers by comprehensively investigating the previous studies on the behavior pattern of customer in terms of Internet business among the service supply chain using system dynamics. In other words, this study analyzes the behavioral changes of resource allocation of internet shopping malls in service supply chain. Also, under the assumption of an ever-growing and ever-developing industry of internet shopping, this study examines the aspect of dynamic changes of the closed feedback circulation structure in which created profits are invested in other resources of companies, making an increase in resources, subsequently resulting in the increase of productivity, customer satisfaction, and profit re-generation. Based on the analysis, this study develops a research framework that supports strategic decision-making for Internet business resource allocation. Based on the above analysis, this study provides meaningful suggestions on a set of advisable investment portfolios that can bring the most optimized profits to companies. Such research framework would be helpful for providing a typology of Internet business model that can be specialized by each industry.


2. Literature review on key elements for purchasing in the internet shopping mall.

2.1. Decision making steps for the purchasing in the internet shopping mall.

Individual consumers, under the environment of Internet, go through various steps of decision making to finally reach the step of making an actual purchase. This process itself varies depending on the kind of product, the consumer’s individual character, and the purchasing status of the product. Generally speaking, consumers go through the steps of problem cognition, information search, alternative evaluation, buying decision, and evaluation after purchase. An Internet-oriented purchase also goes through same steps as the decision making model for a general purchase. However in the internet-oriented purchase, compared to the offline market, due to the uniqueness of the internet, the processes for information search and alternative evaluation can be carried out rapidly, and the potential problems caused by the lack of information and the lack of judgment objectivity can be minimized through providing various supports needed for purchase decision making.

In Finskud(2003)’s study, consumer’s buying steps were divided into 6 different steps such as non-cognitive step, cognitive step, knowledge acquiring step, consideration step, purchasing step, and loyalty step. By using these 6 steps, 6 processes related to purchase such as recognition process, knowledge acquiring process, process for feeling attractive to, evaluation process, and purchasing process were established.

Also in the study by Cleland(2000), channels related to customer purchase are categorized by non-cognitive customers, cognitive customers, browsers, users, and loyal customers. And for each category, the degrees of influences from 8 different elements (Connectivity, Communication, Content, Convenience, Community, Customer care, Customization, Consent) are analyzed.

Another research, which analyzes the behavioral pattern of Internet shopping, is the web conversion efficiency model by Berthon et al. (1996). In this study, the web was regarded as advertising media, thus the flow of an Internet surfer’s action was modeled as a 6 stepped process (surfer → aware surfer → hits → active visitors → purchases → repurchases). Furthermore, the concept of 5 step measurement (‘aware efficiency’, ‘locatability / attractability efficiency’, ‘contact efficiency’, ‘conversion efficiency’, and ‘retention efficiency’) was suggested.

Also, Kalakota & Whinston(1996) explained decision making steps for product purchase through the internet by classifying three different steps of ‘pre-purchase interaction, purchase consummation, and post-purchase interaction.

2.2. The purchasing elements for internet shopping

In this study, as variables which make potential consumers aware of a product or brand and attract people to visit an internet shopping mall, elements related to marketing promotion such as internet advertisement, internet promotion, sales promotion activity, and the word of mouth activity are defined. Also, strategic variables related to actual purchase from an Internet shopping mall are categorized into three variables of price discount promotion, the quality improvement of web system, and the intensification of web contents. And strategic elements related to customer satisfaction after purchase are categorized into the quality of delivery service and the quality of customer support. Strategic variables such as price promotion, web system quality, and quality of web contents have meaningful effects on the level of customer satisfaction. The level of customer satisfaction is the collective concept of the website features of Internet shopping malls such as the web system and web contents. The quality of delivery service and customer support service, which are the evaluation elements for measuring customer satisfaction after purchase, are the variables related to service quality inducing re-purchase or market exit through satisfaction or dissatisfaction by customers. The previous studies on service quality elements related to Internet purchase are as follows.

Variables Researcher
Internet Advertisement & Promotion Berthon, P., Pitt, L.F., and Watson, R.T. (1996), Cleland. Robin S. (2000), Ducoffe, Robert H. (1996), Grover, R. (1992), Peterson, Robert A., Sridhar Balasubramanian and Bart J. Bronnenberg (1997),Smith, R. E. & Vogt, C. A. (1995).
Word of Mouse Bone, P. F. (1995), Bristor, J. M. (1990), Chrysanthos Dellarocas (2003), Ellison, G. and Fudenberg, D. (1995), Gelb, B. and Johnson, M. (1995), Harrison-Walker, L. J. (2001), Richins, M. L. (1983), Richins, M. L. & Root-Shaffer, T. (1988), Singh. Jagdip (1990), Smith, R. E. & Vogt, C. A. (1995), Sundaram. D. S., Motra. K., & Webster. C. (1998).
Price Promotion Bailey, J. P. (1998), Grewal, Dhruv and Howard Mannorstein (1994), Kalra, Ajay and Ronald C. Goodstein (1998), Krishnamurthi, Lakshman and S. P. Raj (1985), Roughgarden, J. (1998), Smith, Michael F. and Indrajit Sinha. (2000).
Quality of Web System Bucklin, Randy and Catarina Sismeiro (2000), Donthu, Naveen (2001), Dorian S. and Schubert, P. (1998), Lin, J. C., and H. Lu (2001), Liu, C. and K. P. Arnett (2000), Sedehi, H, Boscolo, F and Vaccaro, N. (2000), Wan, H. A. (2000), Yoo, Boonghee & Naveen Donthu (200l).
Quality of Web Contents Barnes, S. J., and Vidgen, R. (2001), Dholakia, Utpal M., and Lopo L. Rego (1998), Davis, F. (1989), Dorian S. and Schubert, P. (1998), Egger, Florian N. (2000), Ghose, Sanjoy and Wenyu Dou (1998), Huizingh, E. K. R. E. (2000), Moe, Wendy and Peter Fader (2001a), Urban, G., Fareena, S. and William Qualls (1999).
Quality of Delivery Bienstock, Carol C., John T. Mentzer, and Monroe Murphy Bird (1997), Brynjolfsson, Erik and Michael D. Smith (2000), Erik R. Larsen, Ann van Ackere and Kim Warren (1997), J. T. Mentzer, J. F. Daniel, and J. L. Kent (1999), J. T. Mentzer, J. F. Daniel, and G. M. T. Hult (2001), Membrillo, Annabel, James Ritchie-Dunham, and Conrado Garcia Madrid (1999), Oliva, R. and Sterman, J. D. (2001), Randall, L. and M. Senior (1992), Zeithaml, V.A., A. Parasumaran and L.L. Berry (1990).
Quality of Customer Support Service Anton, J., Vivek, B. and Bill, H. (1999), Feinberg, R. A., I. Kim, B. Hokama, K. Ruyter, and C. Keen. (2001), Fung K. K. (2001), Garnett, O., A. Mandelbaum, and M. L. Reimann (2002), Grossman, T. A., D. A. Samuelson, S. L. Oh, and T. R. Rohleder (2001), Mandelbaum, A. and N. Shimkin (2000), Saltzman, R. and V. Mehrotra (2001).

Table 2.

Literature review of major variables for online purchasing.


3. Research model and methodology

3.1. Conceptual framework

As can be seen in figure 1, the conceptual framework is constructed based on previous literature. Potential customers go through each step of the Internet shopping market by recognizing the existence of online shopping malls with the use of the internet. As Internet users go through each step, they create profits for internet based companies by sometimes being just one-time buyers or by sometimes being loyal customers depending on the operation strategies of internet shopping malls. Depending on the investment ratio by strategic decision making, these profits are re-invested back into the operations of internet shopping mall, which is the process of company growth. In other words, the whole flow goes through the following process in circulation; Investment with efficient resource allocation → Promotion for the purchase/re-purchase of internet oriented customer → Profit generation → Re-investment in the operating resources of internet based companies. Thus, it is impossible to understand the process for explaining the growth of Internet based company in the perspective of a simply continuous linear process, and the process for growth can only be understood when intricate circulating interactions of cause and effect, which exist among explaining variables, are grasped.

Figure 1.

The system dynamics model of resource allocation decision making.

Therefore, in this study, for the purpose of examining the series of such circulating causal relationships among strategic elements related to Internet purchasing, the system dynamics framework of the efficient decision making for resource allocation in Internet shopping mall is developed. VENSIM DSS Ver. 5.5, which is the tool for system dynamics simulation, is utilized to construct and analyze the model.


3.2 The expansion process of demand in pre-purchase step.

3.2.1. The system dynamics model of investment for internet advertisement and promotion

Bass, in his expansion model (1994), presents advertisements and promotional activities that stimulate radical demand, along with the word of mouth activities that can induce mimicking demand, as the political variables that attract customers in the process that potential customers visit the web site with the recognition of a product and brand.

When it comes to attracting customers to the web site and getting them involved in actual purchase, Internet advertisements and promotions, which stimulate radical demand in the Internet shopping mall, are more cost-effective and efficient than traditional media. They provide information on the internet shopping mall or their products, eventually deriving sales through bi-directional online communication. Furthermore, they are utilized to introduce new products of an internet shopping mall or promote purchase by providing customized information to existing customers via emails, and by contacting the customer continuously (Cleland & Robin S. 2000; Ducoffe & Robert H. 1996).

Figure 2.

The system dynamics model of investment for Internet advertisement and promotion.

Regarding word of mouth, as described in the literature review, the customers with previous purchase experience tend to introduce the shopping mall or products to other potential customers. In other words, satisfied customers will spread positive words, and those unsatisfied will spread negative words. And through these processes, customer inflow by positive word of mouth and customer loss by negative word of mouth is created (Godes, D. & Mayzlin, D. 2003).

Each auxiliary variable, which are used in the system dynamics model of this study, was measured by utilizing the example of an Internet book store, which is one of the most common forms of internet shopping mall, and these variables were applied to the actual model. And in the case where it is hard to draw concrete value, the variable values presented in existing theoretical study were used. Regarding the growth of the potential market, the types of growth are hypothetically divided in two different types for analysis. First, with the assumption that the overall market size of an internet bookstore makes a letter S shaped logistic curve, in the early stage, the market grows slowly, however, later as time flows, the market makes rapid growth, and in its maturity stage the growth gets slowed down making the typical S shaped market size. The second model assumes that the Internet bookstore market shows an exponential increasing pattern, meaning that the market grows rapidly every year with a regular ratio. The rapid growth ratio from this model may be able to explain the rapidly growing pattern of the Internet bookstore market but since it is more general that the market growth speed gets dull and slows down as time flows in reality, each of the above 2 assumptions on market growth was applied to subsequent analysis.

Regarding the efficiency evaluation on investment for advertisement and promotion, in order to measure the efficiency of on-line advertisement, such issues as how much traffic the internet advertisement has created, in what channels customers come across the information on the site, and what types of banner advertisement are being introduced to customers have to be grasped. Therefore, in this study, for evaluating the availability of advertisements, values provided from the selected internet bookstore, and the values from analyzing a log file such as ‘hits’, ‘page views’, duration of stay, sessions, and the number of visitors being the barometer for evaluating the efficiency of advertisement, are used. Also, the data of CPM (click per mill) and CTR (click through rate), both having relations to Internet advertisement, are used. For the availability of promotional activities, this study used measurement values acquired from customer reaction toward the promotional activities of the sampling company. And among the variables related to word of mouth, the probability of contact with loyal customers, which is the measurement variable for the positive word of mouth effect, and the probability of contact with non-purchasing customers, which is the measurement variable for the negative words of mouth effect, were applied with historical data from other previous theoretical literature. The previous literature suggests that the negative words of mouth have about 7.5 times more effect than positive word of mouth. And for the information of selection ratio and rejection ratio, values from previous literature were applied. The efficiency of positive word of mouth and negative word of mouth were constructed with nonlinear function and analyzed the degree of changes in values through sensitivity analysis.

3.2.2. The system dynamics model of investment on the quality of web system

It is the most important thing for an Internet shopping mall to provide faster and more stable system maintenance as a virtual retail shop under Internet based circumstance. However, if the number of visitors or transactions for the web site increases, it creates more web traffic, causing the appropriateness of web server infrastructure to go down. And this, again, reduces Internet transactions due to the decrease in business attractiveness, resulting in a negative feedback structure. The negative feedback effects means that the overall structure of a sub-system circulating around a feedback loop is in the form of scattered mixture between some increase from positive effect and some decrease from negative effect, and eventually maintaining a stable system structure as time goes by. However, if profits from Internet business transactions are invested on web server, an improved web server infrastructure can play as the factor for revitalizing Internet business transactions by elevating the appropriateness of the server. Therefore, the operator of an internet shopping mall must commit to constant investment on the infrastructure of a web server in advance for making smooth transactions in the internet shopping mall possible(Donthu & Naveen 2001).

The following is the explanations of the system dynamics model on the quality of web system.

If the number of users searching or purchasing products in web site increase, the traffic of web site gets heavier, resulting in the increase of crowdedness in web site. This will cause the overload of web server capacity, considerably slowing down the loading speed and

Figure 3.

The system dynamics model of investment on the quality of web system.

download speed of web site. Overloaded server capacity puts continuous pressure of investment on increasing more web server capacity as the traffic of customers in web siteincreases. And the administrator of web server in the Internet shopping mall has to make an investment decision on a web server by judging from the difference between the status oftraffic in current web system and the expectation of customers on crowdedness in the process of searching through or purchasing products from the mall. And depending on the difference or the directions between the degrees of web traffic crowdedness that customers feel actually and customers expect, customers have satisfaction or dissatisfaction toward their purchase from the Internet shopping mall. Customers who are satisfied with the speed or reliability of the web server system end up buying products or services from the Internet shopping mall, and customers who are not satisfied give up their purchase from the mall and end up using offline malls, or completely give up their purchase from anywhere.

Therefore, investment on web server lessens the gap between customer’s recognition and their expectations on the web system, making the purchase environment amicable. However, the administrator of a web site needs to have a solid pre-management plan on traffic, since the investment related to the expansion of web server takes some interval time (Liu, C. & K. P. Arnett, 2000). Thus, it is essential for the administrators of Internet shopping malls to provide conditions in advance and timely fashion to customers that they can enjoy shopping through Internet under the circumstance of faster speed and more stability in the step of searching or purchasing.

3.2.3. The system dynamics model of investment on the quality of web contents

To convert simple site visitors of internet shopping mall into actual consumers, more useful and more reliable information has to be provided to them. In other words, it is essential not only to construct useful contents that can stimulate the interest of customers, but also to provide a systematically structured web site with systematic navigational structure in which customers can easily browse through. Therefore, the contents provided by the web site of the internet shopping mall have to be equipped with, first, the merchandising function introducing the variety of products to customers, secondly, the navigational function making easier product search possible, thirdly, the web design emphasizing external aspects such as visual appeal, overall website design, graphics, and effective color usage, and fourth, information security providing information reliance on purchase. The issue has been raised that these four aspects have to be individually analyzed with a more subtle and scientific approach. By doing so, customers will be experiencing feelings of satisfaction or dissatisfaction, and making a purchase decision or giving up purchasing, based on information provided by the Internet shopping mall (Dholakia, Utpal M. & Lopo L. Rego 1998; Huizingh, E. K. R. E. 2000).

In the system dynamics model of investment on the web contents of internet shopping mall, it is suggested that the environment of internet shopping has to be constructed so that it can provide more abundant and organized information to customers through the four variables which came from previous research (Moe, Wendy & Peter Fader 2001). Each influential variable decides the quality of web contents by the relative differences between the customers’ actual recognition of the contents of an internet shopping mall by visiting it, and the level of quality that customers can accept through the four variables. The quality of contents in a web site like this becomes a very important standard for customers to judge the site by looking at the quality gap between actual recognition and expectation, thus, this quality recognized by customers becomes very influential element for decision making on purchase.

Figure 4.

The system dynamics model of investment on the quality of web contents.

In this study, as the materials for system dynamics simulation, values for four variables composing web contents are decided based on relative ratio between acceptable values and actual values. These values are analyzed by utilizing concrete values acquired from the sampled Internet bookstore’s own consumer surveys. And for the anticipation level of what customers expect on the contents of the web, the average values of Internet bookstore industry in Korea from the survey conducted by KWPI (Korea Web Power Index) are used.

3.2.4. The system dynamics model of investment on price promotion

Price promotion is one of the important elements influencing customers’ purchase decision in the internet shopping mall. According to Bailey’s research(1998), the profit increase of a company has a positive effect on the investment related to the quality of service, and improved quality of service accelerate customer demand by providing an extra price premium on product or service. Furthermore, it is analyzed that improved quality of service boosts up the level of attractiveness customers feel about a product or service, bringing positive effect on the market share of a company. Moreover, the revenue increase of a company is followed by the cut down of cost due to its negotiation superiority and economies of scale. And the cost reduction effect brings down the price of products, making a low price policy possible (Smith, Michael F. & Indrajit Sinha. 2000). Such price differentiation strategy has a positive effect on increasing sales in the internet shopping mall by attracting more purchasing needs from customers. But, as examined in other previous studies, the level of satisfaction on price through price reduction policy is not an element influencing directly on the overall satisfaction level toward internet shopping mall, but it can influence a customer’s purchase decision in the case that there is no product differentiation in each internet shopping mall. Therefore, rather than applying the same range of price discounts on the overall products in the internet shopping mall, the administrator needs to maintain the price strategy that can speed up product sales through such promotional activities as differentiated pricing policy depending on the types of customers, limiting the quantity of a products to be sold, and special pricing promotion for limited time.

Figure 5.

The system dynamics model of investment on price related promotion.

Although the investment on price promotion plays a positive role in the profit structure of an internet based company, it also has negative aspects. As examined in literature review, the investment on price promotion functions as a factor to bring more purchases from the users of an Internet shopping mall, stimulating more sales. However, on the contrary, discounted pricing can be an element to worsen directly the profit of a company, because it causes customers to have a tolerance on continuous price reduction, so the internet based company has to maintain persistent price promotion to derive continuous purchasing from customers. Therefore, the policy maker of an internet shopping mall has to use the promotional policy with full consideration in positive and negative aspect of discounted price.

The investment on price discount means that some portion of the net revenue of a company is being utilized for price promotion, and through the width of the discounted price compared to the reference price on same products in other internet shopping mall, customers feel the attractiveness of the discounted price, stimulating customers’ purchase. The effect of purchase influencing price discount is calculated by multiplying the price effect on the level of purchasing attractiveness with the value of actual price/acceptable price level. And, the price effect on the level of purchasing attractiveness is modeled in nonlinear function by applying Gutenberg- type price sensitivity function.

Furthermore, price discounts have a reverse function on the profit structure of an internet based bookstore. When the company revenues are calculated by multiplying total quantity of products sold and the sales price, the decrease in sales price at the same time leads to lower the accumulated revenues of a company. Thus, the administrators of internet shopping malls must satisfy their customers with appropriate pricing ranges and methods by grasping both the negative and positive aspects of discounted price.

3.3. The process of demand expansion after initial purchase

Customers evaluate the internet shopping mall they use depending on their purchasing experiences from the mall such as how fast and safely the delivery of product they ordered is made and how conveniently problems regarding their ordered products can be resolved. Based on evaluation via their shopping experiences, customers decide whether or not they should revisit to repeat their purchase. Therefore, it is not necessarily possible that customers are satisfied with the internet shopping mall just because it had a promotional event one time and built a good purchasing environment on the website. Only when all the necessary elements customers want are operated together in harmony, will customers’ full satisfaction be possible.

3.3.1. The system dynamics model of investment on the service quality of delivery

According to the market growth model by Membrillo et al., (2002), the more the customer orders are received the more there will be delays of product delivery, diminishing the market attractiveness of a product or service. And the increase in the delay ratio in product delivery adds pressure on the company to expand investment in additional delivery capacity. Thus, in the same model, they insist that companies have to satisfy customers by minimizing delay of product delivery, and utilizing their capacity efficiently. Also, in the studies by Oliva & Sterman(2001) and Mentzer et al., (2001), as customers place more and more orders, the backlogs of placed orders increase, needing the expansion of delivery capacity, eventually bringing the feedback effect that performs delivery smoothly through the expanded delivery system capacity which is added after some time interval.

In the case of an Internet shopping mall, the more customers make their purchase on line, the more delays of deliveries there are for the portion that exceeds current delivery capacity. Even though the delays in product deliveries keep adding up, it happens that Internet based companies and their belonging market can recognize this only after some time. Furthermore, even at the point of time Internet based companies realize the delays, there is already a big gap between the delivery service customers experience and the delivery service the company aims for, due to the accumulation of overloaded orders from customers. Then, it will make the customers’ level of dissatisfaction on delivery service increase, causing the cancellation of orders or the decrease of potential orders. That is, the loop related to delays in product deliveries has a negative feedback effect, so orders will decrease more and more after some point. However, delays in deliveries give the administrator of a internet shopping mall more pressure on expanding delivery capability and capacity, thus, some portion of company profits is invested in expanding the delivery system. In fact, the pressure effect of expanding the facilities for delivery service is modeled by indicating in a nonlinear way the difference between the actually recognized delay of product delivery due to the increase in orders being placed and the duration of product delivery that the Internet shopping mall company is aiming for (Sterman, 2000).

Delivery capability expanded from the re-investment on the delivery service processes orders efficiently and provides satisfactory feelings to customers by bridging the gap between what customers expect and what they experience. By doing so, it influences customers’ decision on re-purchase by boosting up the loyalty of customers towards online purchasing (Bienstock et al., 1997).

Figure 6.

The system dynamics model of investment on the service quality of delivery.

3.3.2. The system dynamics model of investment on the quality of customer support service

An Internet shopping mall has to think deeply about strategies and operations that can provide customers with contact opportunity on some meaningful benefits and make them happy. It is the customer support center which is the closest contact point improving relationships with customers by listening to customers. The performance of the customer support center, which is the contact point between customers and company, depends on how it satisfies customers with a reasonable amount of cost, and prevents them from staying away from the company. The customer support service team processes the complaints or the questions from customers by utilizing the resources related to the service with limited amount of budget. Then, the way and delay level of responding to customer complaints and questions influences customers’ confidence on the Internet shopping mall, and ultimately affect the level of customer satisfaction after their purchase. Therefore, it is essential for Internet based companies to be equipped with a real time customer support system that can respond to complaints and questions in a timely and kindly manner (Anton, J. et al., 1999).

As the number of customers placing orders online at an Internet shopping mall increases, more various questions or complaints regarding the purchases or the deliveries of products are created. And these questions or complaints are resolved through the customer support center of the Internet shopping mall, the information desk of web based call center, and the

Figure 7.

The system dynamics model of investment on the quality of customer support service.

FAQ (Frequently Asked Question) section of the site. There are some differences between the service level customers expect regarding the management of complaints and questions and service level provided actually by the internet shopping mall. According to the level of such difference, the service quality of call center that the customers of the mall feel about will be differentiated. The quality of call center influences the level of satisfaction after their purchase, further, affects customer loyalty. Therefore, the administrators of Internet shopping malls need to respond to customers’ request in timely fashion by utilizing the human and physical resources that can process efficiently the service claim related to their consumers (Ward Whitt, 1999).


4. Simulation analysis and its result

4.1. The validity of the system dynamics model

Before analyzing the decision making model of Internet shopping mall, first, the validity of the model has to be confirmed. According to the study of Barlas (1989), the method of evaluating the validity of model can be divided into two general approaches. One is the validity of model structure and the other is the validity of model behavior.

First, in the validity of model structure, after the conceptual modeling was constructed based on various theories and research results from previous literature regarding the purchase decision in the internet shopping mall, the structural validity and boundary appropriateness were verified by going through several discussions with many practical managers, followed by amending the structure of the model. Also, in this study, the unification of measurement unit was achieved by conducting simulations for 60 months along with according the unit of measurement from the numerical formulas of each variable at the same time. In the validity of model behavior, the pre-tests on each partial loop were conducted to reflect the elements from the pattern of purchase decision in the Internet shopping mall such as the symptoms of problems that can be witnessed in real world, the behavioral pattern, the behavioral change in each step, and the fluctuation. And, the initial value of the model is adjusted to arrive at the balancing status – a certain point where the condition of simulation reaches the stabilized step through the connection of loop in the overall model. By doing so, the conditions for the reproducibility and normality of model behavior were satisfied. Also, by conducting sensitivity analysis on each LOOKUP function, the validity check on each function is carried on at the same time.

The most important and the most influential LOOKUP function among all the LOOKUP functions used in this study is the function of price discount on the level of attracting purchase. The price sensitivity function of Gutenberg type is used for the LOOKUP function. The hypothesis suggested in the Gutenberg model is that if the price gap between the company’s own product price (Pi) and average competing price (Pi*) is small, such difference in pricing have a relatively low influence on the quantity of company products sold or the market share of the product.

Figure 8.

Non-linear price response function (Gutenberg Model).

Figure 9.

LOOKUP function of the effect of price on attractiveness.

In the Gutenberg model, the section between A and B is the area of price by which customers’ purchasing decisions are not influenced. The model assumes that customers in this section will keep purchasing the product or the service of the company with the intention of paying extra money. This assumption is only possible when current customers have favorable feelings about purchasing the product of the company and there exists a switching cost for customers to change their preference. Therefore, when the price gap is not big, customers mostly tend to keep purchasing products they currently use.

Thus, this study analyzed the interactive relationship between price discount and the quantity of products purchased by customers by the Gutenberg model. In other words, customers tend to show the increase of their purchasing intention when they recognize more significantly transactional effectiveness – the difference between internal reference price and actual sales price which is formed through the price comparison with other internet shopping malls. In the sensitivity analysis for the influence of price discount on the level of attracting purchase, the effect of discounted price on customers’ buying decision and the decision making related to the overall resource allocation is appeared to be not much significant. However, it is found that depending on the partial changes of the nonlinear relation of LOOKUP function, the values of overall stock variables and flow variables change bit by bit. But it is also found that this changing ratio lies within an acceptable range, not having major influence on decision making.

4.2. Simulation analysis

When analyzing the company in the example, the simulation is conducted for 5 years (60 months) with the unit of analysis in a month, considering the environment of internet market, changing so fast more than ever. In the analysis for the example company, the cases are divided in two assumptions for the analysis and comparison, one being the growth pattern of internet bookstore market in (A) S-shaped logistic curve and the other being (B) in the form of constant increasing growth. In the case with the assumption of the growth pattern of internet bookstore market being an S-shaped logistic curve, the assumption means that there is a negative feedback structure for the overall size of internet bookstore market not to be able to grow continuously. Under the basic assumption of Bass’s model of expansion in demand, the market grows due to the definition feedback loop at the early stage. However, as time passes, the market growth gets oppressed by the strengthening of the effect of a negative feedback loop (Bass, 1994). Under the case that the overall size of the market is in the form of S-shaped logistic curve due to the limit of growth, the demand of the market grows rapidly at the early stage due to the effect of the limited number of potential users and then later, the demand decreases due to growth limits. Namely, when the overall market size is called and the number of actual consumers at the point of time is called, the number of potential users left is. And assuming the conversion ratio of potential consumers to actual consumers as, at the point of time, the number of converted consumers from the potential to the actual is and. When is calculated through this, it is as following formula (1).
n ( t ) = M 1 + [ ( M n 0 ) / n 0 ] e c t ,             t 0 E1

In the system dynamics model with the assumption of the overall market size of internet bookstore being in the form of S-shaped logistic curve with the basis of Roughgarden’s study (1998), the market growth rate of an internet bookstore is calculated by multiplying the market growth rate and the overall market size. And, here, the arithmetic formula for the overall market size is calculated by summing all the numbers of potential consumers, the site visitors, the online consumers, the loyal customers, and those who gave up their purchase in the online market. And the growth rate of internet bookstore market is analyzed by using the S-shaped LOOKUP function. Through a formula such as the above, the model is designed in the way that the internet bookstore market grows in rapidly in the early stage, then later after some point, the growth rate of the market lessens or stops with no more expectation of further growth.

And another analysis is conducted on the model assuming that the internet bookstore market maintains continuous growth in the form of constant increasing function, overcoming its limit. In the case with the assumption of the internet bookstore market being in the form of constant increasing growth, it is assumed that the expansion in demand grows with no limit due to the infinite potential customers. In the study by Kirkwood (1988), the number of actual buyers at the time of is calculated with the assumption that the number of actual buyers during the time being.
n ( t ) = n 0   e c t ,             t 0 E2

With having the study by Kirkwood (1998) as the basis, the growth rate of internet bookstore market in the assumption being the market constant increasing growth is calculated by multiplying the potential size of the market and the market growth rate. And in this case, the growth rate of internet bookstore market is modeled by having it through STEP function with the basis of future growth rate of internet shopping market, which was used in ‘the analysis and the prospect of domestic internet shopping market’ by KISDI(Korea Information Strategy Development Institute) and with the analysis of historical growth rate information (1999~2004) on domestic internet bookstores in Korea.

In the case with the assumption that the size of the internet bookstore market is in the form of an S-shaped logistic curve (A), the following are the results from the analysis of important stock variables based on the data of an exemplifying company. That is to say that the number of visitors for internet bookstores shows rapid growth in the early stage, then around the 20th month, shows the symptom of a temporary lessening of growth speed, however again in the 26th month, it shows rapid growth again. Also, after reaching to the maximum point around the 43rd month, it turned back to the tendency of rapid decrease. The number of consumers placing orders with the online bookstore shows gentle growth from the initial point, however around the 50th month, shows the tendency of going back to decline. The number of loyal customers to the internet bookstore, in its early stage, shows the tendency of continuous increase, however around the 11th month, the tendency of increase is lessened, showing its status as the growth in the number of loyal customers being stopped, and around the 24th month, showing its tendency of increase. Accordingly, the accumulated profits of internet bookstore show continuous tendency of increase with a rather declining tendency around the 80th month. And in the case with the assumption that

Figure 10.

The stock variables of the internet bookstore case.

the market size of internet bookstore increasing by constant increasing growth (B), by looking at the results from the analysis of stock variables in the exemplifying company, it isfound that the number of site visitors constantly increases until the 36th month, however, starting from the 36th month, the gentle increase settles in. The number of consumers, placing orders with internet based bookstore, appears to be increasing and then startingaround the 20th month, shows radical growth, which maintains its growing tendency for a while. Regarding the loyal customers of internet bookstore, it shows the pattern of radical increase in early stage and then stop its growing pattern briefly around the 20th month, then showing the gentle decline until the 42nd month and then going back to the pattern of radical growth. Thus, it seems that the accumulated profits of internet bookstore follow continuously the increasing pattern as well.

This pattern - that the values of the stock variables for some duration falls all at the same time, stagnate without going through the increasing pattern and then going back to the pattern of increase – is caused by the investment delay on the delivery service, which is one of the service quality elements after purchase, and the service capability of customer support. In other words, as the number of customers placing orders increase, it is possible to provide continuous services to the customers through the existing system of delivery service and customer support service in early stage, however from certain point of time, it becomes that exceeding number of orders is not matched with the existing system, resulting in not being able to respond appropriately to some of the requests by customers who then end up having dissatisfied experiences with the internet bookstore. The administrator of internet bookstore, realizing the lack in the capability of its service, tries to provide the quality service by putting additional investment on delivery service and customer support service. However since it necessarily involves with the delay in expanding the service capacity, the number of loyal customers does not increase until expansion in the service capacity is fully done. Therefore, the administrator of an internet bookstore has to arrange the proper environment for customers to be satisfied and then become loyal customers. And it means that securing the service capacity fully in advance through the estimated projection of demand on the delivery and customer support service is the important element for satisfying customers and considering the profit of the company at the same time.

4.3. The analysis of the most optimized portfolio for providing the best profit ratio to the internet bookstore

The primary purpose of this study is, from the perspective of the administrator of internet bookstore, to answer the question of how to maximize the profit of a company after allocating the resources of a company in most efficient way by using the methodology of system dynamics. In other words, the study is to verify, from the perspective of profit maximization, what the most optimized portfolio is as to investment alternatives. For this purpose, the investment alternative, providing the most optimized profitability to the internet bookstore, was compared by adjusting the investment ratio of 6 investment alternatives, which were used in the previous section of the study. Among the various combinations of the investment alternatives except for some of extreme cases, about 36 alternatives were compared for analysis. From 36 alternatives, three alternatives, providing the most optimized profitability to the internet bookstore, were derived for the case with the assumption that the size of internet bookstore market is in the form of S-shaped logistic curve and the case with the assumption of it being in the form of constant increasing growth. Thus, 6 alternatives are derived, they are as follows.

Alternative (A) In the case of S-shaped logistic curve (B) In the case of constant increasing growth
Investment Control Variables Alternative(A) Alternative (B) Alternative (C) (Optimal) Alternative (D) Alternative (E) Alternative (F) (Optimal)
Internet Advertisement & Promotion 20 15 10 15 10 10
Web System Quality 15 20 20 20 15 15
Web Contents Quality 10 10 10 15 20 20
Price Promotion 20 15 10 10 15 10
Delivery Service Quality 20 20 25 20 20 25
Customer Support Service Quality 15 20 25 20 20 20

Table 3.

Investment portfolio of Strategic Alternatives.

4.3.1. Strategic investment alternative (A)

Strategic investment alternative (A) is the one investing mostly on internet ads or promotions, discounted price, and delivery service under the condition with the assumption that the size of internet bookstore market is in the form of S-shaped logistic curve. When the results of analysis are looked into, it has a lot of site visitors and purchasing customers in early stage, however, it has a difficulty of increasing the number of visitors or consumers continuously. That is because there is limit for making the constant growth in the number of customers due to the decline of investment on the web server that can cover a lot of visitors or consumers in early stage, resulting in the increase of traffic. Also, this alternative is evaluated as being unable to fulfill satisfaction of increasing loyal customers continuously due to the lack of investment in the customer support service.

4.3.2. Strategic investment alternative (B)

The strategic investment alternative (B) is the one with lower investment ratio in internet ads and promotions along with price reduction and, instead, with relatively higher investment ratio in web server systems and customer support systems, under the assumption that the size of internet bookstore market is in the form of S-shaped logistic curve. It shows much higher profitability than the strategic investment alternative (A). Looking at the results of system dynamics in alternative (A), it shows the pattern in that there are less visitors in early point than the alternative (B), however in later stage, it has a more growing number of visitors constantly than the alternative (A), securing about two times more number of customers than the alternative (A).

Furthermore, when it is evaluated based on the total profitability, it has about 1.3 times more profits than the alternative (A) by having its growing ratio of loyal customers continuously increase. It is evaluated to be a good investment alternative in the sense that it has the construction of the stable web server, and more smooth delivery service with its capability of being able to come closer to customers. However, in the sense that it lacks in securing customers at the initial stage, and actual consumers, it is viewed as rather insufficient alternative. Thus, it is a good strategy only when used properly in the stage of market maturity and maintaining some level of market share already won.

4.3.3. Strategic investment alternative (C)

The strategic investment alternative (C), being the most optimized investment portfolio under the condition with the assumption that the size of internet bookstore market is in the form of S-shaped logistic curve, has about the same level of investment ratio with the alternative (B) except for the fact that it has less investment ratio on internet ads and promotions along with price reduction by about 5%, and instead has 5% more investment ratio on delivery service and customer support service. Although its initial profits are less than other strategic investment alternatives, in the long term, it provides the most optimized profit structure. The alternative (C) is the one with concentrated focus of investment in customer support service after purchase. Even if it lacks in securing initial customers, it is the strategy, viewed as the very effective investment alternative that can convert a growing number of site visitors and consumers to loyal customers efficiently. When examined in more detail, more investment in the quality of service after purchase makes customers have higher expectations that the quality of delivery service and customer support service is very excellent. And by doing so, it can increase the level of repeated purchases of customers. Also it can boost up customer loyalty after purchase by providing more convenient and kindlier customer support through the expanded delivery service, and support service, and by actually making deliveries in a timely and accurate fashion. Thus, it shows the 1.5 times more increase in the number of loyal customers than the alternative (B). The phenomenon such as this reflects that the expansion of investment in delivery service and customer support service is becoming the very important element in the profitability of internet shopping malls as the variable that can attract general consumers to be its loyal customers and at the same time as the variable that can increase the repeat purchase ratio, under the circumstance that the ratio for repeat purchase is being raised as an important issue due to the growing tendency of an internet shopping mall market despite a recent slow down of the internet oriented market. However, the alternative (C), just like the alternative (B), also is viewed as an alternative lacking in attracting site visitors or buyers at the initial point. Thus, this alternative is considered to be the one that can be applied only when the market of internet bookstore is in its growing phase or maturity phase as well.

Therefore, the most optimized investment portfolio, under the condition with the assumption that the size of internet bookstore market is in the form of S-shaped logistic curve, has its investment in the order of ‘delivery service and customer support service > web server system > internet ads and promotions along with price reduction’. And it is analyzed that this provides the most profits for the internet bookstore.

4.3.4. Strategic investment alternative (D)

The most optimized investment portfolio in the condition with the assumption that the size of internet bookstore market increases constantly shows rather different result than the investment strategies in the condition with the assumption that the market size of internet based bookstore is in the form of S-shaped logistic curve. First, the strategic investment alternative (D), which is one of the most optimized investment alternative portfolios, has the same investment pattern as the alternative (C). It is the alternative with rather higher investment ratio in web server systems, delivery services, and customer support services. This alternative has an increasing effect on initial site visitors due to the investment increase in web server; however, it shows a pattern of the increasing ratio slowing down as time passes. But since it has lower investment ratio in price reduction, internet ads, promotion along with promotional activities, and the contents of web, it has difficulty securing initial buyers. However, as the number of visitors and consumers increase, it appears to have higher frequency for conversion from simple consumers to loyal consumers due to the fact that the quality of rich fundamental service and infrastructure such as web server, delivery service and customer support service gets better, in which they had investment in at the initial stage. Thus, as time passes, the profit scale of an internet bookstore shows the pattern of gradually growing.

4.3.5. Strategic investment alternative (E)

The strategic investment alternative (E), which is one of the most optimized investment alternative portfolios in the situation with the assumption that the market size of internet bookstore increases in the form of constant increasing pattern, is simply the politic alternative with reduced investment in the internet ads, sales promotional activities, and the web server, and instead increasing the investment in web contents and discounted price, compared to the alternative (D), while maintaining the same range of investment ratio for delivery service and customer support service as the alternative (D). In other words, this alternative is made for the purpose of promoting the securing the initial customers, which is the weakness of the alternative (D) by strengthening the investment in web contents and increasing the investment ratio in discounted price. Examining the result of system dynamics, there seems to be no customer-attracting-effect at the early stage by increasing investment ratio in discounted price and web contents. However, after some point of time later, it shows the pattern of radical increase in the number of site visitors and consumers. This indeed means that the investment in web contents takes some time for attracting customers, thus, as time passes, radically increasing the number of consumers due to the expansion of investment in price reduction policy and web contents despite of the fact that these two investment alternatives seem to have no effect on increasing customers in early stage. Also, the expansion of investment in delivery service or in loyal customers, who have a tendency of increasing radically in numbers after some point of time, is viewed as the one with the contribution of promoting the brand recognition of the internet bookstore in some parts by providing more stable delivery service to customers.

4.3.6. Strategic investment portfolio (F)

The strategic investment alternative (F), which is the most optimized investment alternative portfolio on the condition we make the assumption that the size of internet bookstore market increases in the form of constant increasing growth, is the politic alternative with reduced investment in some portion of price reduction, instead expanding investment in delivery service, while maintaining the level of investment ratio on customer support service and web contents when it is in comparison with the alternative (E). In other words, this alternative provides the most optimized profitability to internet bookstore by reducing the investment in price reduction a little and by increasing the investment ratio on delivery service instead. Examining the result of system dynamics by this alternative, it has less initial effect on attracting site visitors than the alternative (D), however, it has a better effect on attracting initial visitors and securing actual consumers than the strategic investment alternative (E). On condition we make the assumption that the size of internet bookstore market increases in the form of constant increasing growth, the issue on the importance of investment in web contents is raised. Also, the alternative continuously secures loyal customers and purchasing consumers by expanding the support for delivery service and customer support service. It has less growth rate of loyal customers at the initial stage than the alternative (E), however, as time passes, the level of increase gets much bigger. Furthermore, in the aspect of company profitability, it eventually provides about 1.5 times more profits than the alternative (E) by continuously boosting up the profitability of the internet bookstore.

As a result of the simulation analysis, it was found that the optimal resource allocation portfolio providing maximum profits to the Internet bookstore involves large-scale investment on delivery service and customer support service which are the key factors for post-purchase customer satisfaction, regardless of the growth pattern or size of the Internet bookstore market. Consequently, from the above analysis, the investment ratio of resources for the profit maximization of an Internet bookstore was drawn.

As examined so far, on condition we make the assumption that the market size of internet bookstore increases in the form of constant increasing growth, the best combinations of investment alternatives are in the order of ‘delivery service > customer support service, web contents > web server > internet ads and promotion, price reduction’ for providing the most profits to the internet bookstore. Therefore, summing all the results so far, allocating more investment in the quality of service, that can provide satisfaction to customers, such as delivery service, and customer support service tend to provide the most profits to a company even if rather different investment portfolios are shown, depending on the market condition of an internet bookstore. Furthermore, things like building an additional web server and strengthening the contents of the web are other variables, making the purchase smooth. Although price reduction, which is one of the strategic variables and has been utilized by internet based bookstores, appears to have some effects on securing customers at the early stage of the market, it is analyzed that the effect is not persistent. And the price reduction is the variable with both sides of profit growth by securing customers and profit loss by reducing the price. Therefore, the administrator of an internet based bookstore has to operate the price reduction policy with a proper method in an appropriate ratio.

Figure 11.

Optimal Investment portfolio(Strategic Alternative 1).

Figure 12.

Optimal Investment portfolio(Strategic Alternative 2).

Figure 13.

Optimal Investment portfolio(Strategic Alternative 3).

Figure 14.

Optimal Investment portfolio(Strategic Alternative 4).

Figure 15.

Optimal Investment portfolio(Strategic Alternative 5).

Figure 16.

Optimal Investment portfolio(Strategic Alternative 6).


5. Conclusion and upcoming research direction

Through the study, 6 variables for interactions between causes and effects are found, related to the profit maximization of an internet based company in management aspect from the internet bookstore, which is one of the most common types of internet shopping malls. And by connecting all the variables in complex, the map for the cause and effect is systematically constructed. Furthermore, by verifying the necessary information of system dynamics model for the purpose of testing the resource distributed decision making model, it made the resource distributed decision making possible. Based on these contents, the relation between the variable, related to the level of customer’s purchase / loyalty in the internet bookstore, and the resource distribution decision making is able to be witnessed.

Conclusively, based on the comprehensive examination of the results, this study provided a framework for dynamic resource allocation decision-making, and proposed a management strategy which allows consumers to shop under a more favorable environment, and simultaneously enables an Internet bookstore to accomplish management objectives such as continuous growth and profit maximization.

The meanings that the study has, in relation with the behavioral resource distribution decision making of an internet bookstore, are as follows. First, the unified form of modeling is conducted after verifying partial knowledge, related to the decision making on purchase in the existing internet shopping malls so that viewing them from the overall perspective is possible. As seen from the previously analyzed outcomes, if investment on price reduction is expanded as for the strategic variable, securing visitors or actual consumers in the early stage of a internet bookstore, securing initial visitors or consumers may be possible, however, it will, after some point of time, rather bring the bad influences on the shopping environments of general/loyal consumers by affecting the purchase environment and the web surfing environment in a bad way due to the increase of a web server traffic. Even if the initial profitability of an internet bookstore increases through the variable, it will eventually worsen the profitability as time passes.

Furthermore, due to the increase in the number of buying customers, the complaints will be accumulated regarding delivery service or customer support service, possibly causing the potential consumers to stay away from the internet shopping mall or current consumers to give up their purchases. Therefore, the administrator of an internet bookstore must unify the partial knowledge, related to the purchase decision making on the internet, and utilize the investment strategies for each variable with the consideration on the overall picture from the systematic perspective. Secondly, the suggested methodology in the study makes the decision maker of a internet shopping mall learn and understand the behavioral mechanism of objective system. In other words, it shows that the alternatives for decision making related to the resources management are changing depending on the condition the internet based company is facing. Also, the most optimized alternative for making decision is suggested. Such influential elements as internet ads, promotional activities, and the price reduction are important, which can stimulate the purchase for new customers, whereas providing newer and richer contents and establishing faster and more accurate delivery service are needed for existing customers and loyal customers. Furthermore, it is mostly important to arrange the condition where customers can make transactions under a more convenient and more stable environment. The mutually important strategic variable for new customers or existing consumers is to build the web environment with more stability and faster speed, thus, from the perspective of an internet based company, making more investment on the hardware of the web service is necessary. In other words, it is essential to try eliminating various risk factors involved with the purchase before-hand, such as the environment and security where faster connection to the web server is possible. And it is another meaning of this study to prevent the potential errors of management before-hand, which are easy to be made, by controlling the values of important variables per each point of time or per each situation. Thirdly, the customer attraction effect of price reduction, which was used mostly by internet bookstores in their initial period of formation to compete with offline bookstores, and the effect of increase in profitability of an internet based bookstore are examined. As seen in the results from analysis, price reduction policy in the market of the internet bookstore has partial effects on securing customers at the initial stage or sales promotions; however, it eventually worsens the profit structure of the internet bookstore. From the perspective of internet based bookstores, securing customers may be important, but what is more important than that is to secure actual/loyal customers who can create profits for companies. It appears that price reduction policy, in some degree, has some effects on securing initial customers or attracting purchasing customers, whereas it does not have any effects on promoting the actual purchase, compared to the investment in the effect of price reduction. Therefore, the administrator of a internet bookstore has to arrange more convenient and safer environment for internet purchase by using the long term perspective oriented effect of attracting customers such as specializing the contents of the web, building a database, and strengthening the capability of more advanced web server rather than trying to boost up the increase in the number of customers through a short term oriented price reduction policy.

While conducting the study, the following are the potential research issues as to the limit of the study that have to be considered in the expanding step of the future upcoming study. First, it lacks in analysis of customer’s behavior, conducting each step of searching and purchasing in different areas of online and offline. In other words, the case that searching products, related to the product purchase, is conducted online while actual purchase being done offline and the case that searching is done offline, while the purchase is done online are not applied in the model. Although online shopping has been a trendy thing due to the widespread environment of the internet recently, in some parts, customers still have separated categories of products each for online and offline. According to the study by Peterson et al., (1997), regarding the purchase possibility in internet shopping malls, it is likely that the purchase possibility is high when the product is cheap, to be purchased more frequently, and has less chance of being differentiated in the aspect of product specialization. It means that the ‘low-involvement-products’ – low risk products which are pretty much standardized with low price range – are to be purchased through online more likely, whereas ‘high-involvement-products’ – high risk products which are not standardized and are not in the low price range – are more likely to be purchased through offline still. Therefore, the issue regarding customers’ shopping related behavioral interactions in between offline and online will have to be further researched. Furthermore, the study lacks in investigating on complex shopping mall (broad-line) and the mixed form of online and offline shopping mall. It uses the internet bookstore, which is the type of internet-only-specialized-shopping mall, that the study conducted its research on, however, it is assumable that outcomes may be different in the case of a rather complex form of shopping mall or a mixed type of online and offline shopping mall. Therefore, it is necessary to further study these different types of shopping malls. Thus, other variables, which are not applied in this study, will have to be applied as further researches are carried on.


  1. 1. Anderson E. W. Sullivan M. W. 1993 “The Antecedents and Consequences of Customer Satisfaction for Firms,” Marketing Science, 12 2 125 143 .
  2. 2. Anton J. Vivek B. Bill H. 1999 “Call Center Performance Enhancement- Using Simulation and Modelling,” Ichor Business Books-Purdue University Press.
  3. 3. Bailey J. P. 1998 “Electronic Commerce: Prices and Consumer Issues for Three Products: Books, Compact Discs, and Software,” OECD
  4. 4. Barnes S. J. Vidgen R. 2001 “An evaluation of cyber bookshops: The Webqual method,” International Journal of Electronic Commerce, 6(1), 11 13 .
  5. 5. Bass F. M. Krishnan T. Jain D. 1994 “Why the Bass Model Fits Without Decision Variables,” Marketing Science, 13(3), 203 223 .
  6. 6. Berthon P. Pitt L. F. Watson R. T. 1996 “The World Wide Web as an advertising medium: toward an understanding of conversion efficiency,” Journal of Advertising Research, 36(1), 43 55 .
  7. 7. Bienstock Carol C. Mentzer John T. Bird Monroe Murphy 1997 "Measuring the Perceived Quality of Physical Distribution Service," Journal of the Academy of Marketing Science, 25(1), 31 44 .
  8. 8. Bolton R. N. 1998 “A dynamic model of the duration of the customer’s relationship with a continuous service provider: The role of satisfaction,” Marketing Science, 17(1), 45 65 .
  9. 9. Bone P. F. 1995 “Word-of-mouth effects on short-term and long-term product judgments,” Journal of Business Research, 32 213 223
  10. 10. Bristor J. M. 1990 “Enhanced explanation of word-of-mouth communications: The power of relationships,” Research in Consumer Behavior, 4 51 83 .
  11. 11. Brynjolfsson Erik. Smith Michael D. 2000 “Friction less Commerce? A Comparison of Internet and Conventional Retailers,” Management Science, 26 4 563 585 .
  12. 12. Bucklin Randy Sismeiro Catarina 2000 “A Model of Website Browsing Behavior Estimated on Clickstream Data,” Working Paper presented at the Marketing Science Conference at UCLA.
  13. 13. Bianchi Carmine Bivonab Enzo 2002 “Opportunities and pitfalls related to e-commerce strategies in small-medium firms: a system dynamics approach,” System Dynamics Review, 18 3 403 429
  14. 14. Chrysanthos Dellarocas 2003 “The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms,” Management Science, 49 10 1407 1424 .
  15. 15. Cleland Robin S. 2000 “Building Successful Brands on the Internet,” University of Cambridge, MBA Dissertation.
  16. 16. Kirkwood Craig W. 1998 “New Product Dynamics-Illustrative System Dynamics Models,”
  17. 17. Dholakia Utpal M. Rego Lopo L. 1998 “What makes commercial web pages popular?” European Journal of Marketing, 32 7 8, 724 736 .
  18. 18. Exelby David Warren Kim 2002 “Strategic Growth Dynamics- the Challenge for Smaller Firms,” Vanguard Brand Management,
  19. 19. Davis F. 1989 “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, 13 3 319 340
  20. 20. Donthu Naveen 2001 “Does your web site measure up?” Marketing Management, 10 4 November-December), 29 32 .
  21. 21. Dorian S. Schubert P. 1998 “Web assessment- a model for the evaluation and the assessment of successful electronic commerce application,” Proceedings of the thirty-first hawaii international conference on system science, 4 Subtrack 3.
  22. 22. Ducoffe Robert H. 1996 “Advertising value and advertising on the web”, Journal of Advertising Research, September/December, 21 35 .
  23. 23. Egger Florian N. 2000 “Towards a Model of Trust for E-Commerce System Design,” Http://
  24. 24. Ellison G. Fudenberg D. 1995 “Word-of-Mouth Communication and Social Learning,” The Quarterly Journal of Economics. 110(1), 93 125 .
  25. 25. Larsen Erik R. Ann van Ackere Warren Kim 1997 “The growth of service and the service of growth: Using system dynamics to understand service quality and capital allocation,” Decision Support Systems, 19 271 287
  26. 26. Feinberg R. A. Kim I. Hokama B. Ruyter K. Keen C. 2001 “Operational Determinants of Caller Satisfaction in the Call Center,” International Journal of Service Industry Management, 11 131 141 .
  27. 27. Fung K. K. 2001 “It is not how long it is, but how you make it long- Waiting lines in a multi-step service process,” System Dynamics Review, 17(3), 333 340 .
  28. 28. Garnett O. Mandelbaum A. Reimann M. L. 2002 “Designing a Call Center With Impatient Customers,” Manufacturing and Service Operations Management, 4 208 227 .
  29. 29. Gelb B. Johnson M. 1995 “Word-of-Mouth Communication: Causes and Consequences,” Marketing Health Services, 15(3), 54 58
  30. 30. Ghose Sanjoy Wenyu Dou 1998 “Interactive functions and their impacts on the appeal of internet presence sites,” Journal of Advertising Research, March-April, 29 43 .
  31. 31. Godes D. Mayzlin D. 2003 “Using Online Conversations to Study Word of Mouth Communication,” Yale SOM working paper.
  32. 32. Grewal Dhruv Mannorstein Howard 1994 “Market Price Variation, Perceived Price Variation, and Consumer’s Price Search Decisions for Durable Goods,” Journal of Consumer Research, 21 Dec. 453 460 .
  33. 33. Grossman T. A. Samuelson D. A. Oh S. L. Rohleder T. R. 2001 “Call Center,” In Encyclopedia of Operations Research, ed. S. L. Gass and T. M. Harris, 73 76 . Norwell, Kluwer Academic Publishers.
  34. 34. Grover R. 1992 “Evaluating the multiple effects of retail promotion on brand-loyal and brand-switching segments,” Journal of Marketing Research, 56(2), 76 89
  35. 35. Harrison-Walker L. J. 2001 “The measurement of word-of-mouth communication and investigation of service quality and customer commitment as potential antecedents,” Journal of Service Research, 4(1), 60 75
  36. 36. Herr P. M. Kardes F. R. Kim J. 1991 “Effects of word-of-mouth and product attribute information on persuasion: An accessibility-diagnosticity perspective, ” Journal of Consumer Research, 17 454 462 .
  37. 37. Ho C. F. Wu W. H. 1999 “Antecedents of customer satisfaction on the internet: An empirical study of online shopping,” proceedings of the 32nd hawaii international conference on system science, l999.
  38. 38. Hong Dong-Pyo 2004 “Prospects for the Korean On-line Retail Market,” Korea Information Strategy Development Institute,
  39. 39. Huizingh E. K. R. E. 2000 “The Content and Design of Web Sites: An Empirical Study,” Information & Management, 37 123 134 .
  40. 40. Hubl Gerald 1999 “Consumer Decision Making in Online Environment,” Advances in Consumer Research, 26 477 478 .
  41. 41. Jarvenpaa Sinka L. Todd Peter A. 1997 “Consumer Reactions to Electronic Shopping on the World Wide Web,” International journal of electronic commerce, 1 2 59 88 .
  42. 42. Mentzer J. T. Daniel J. F. Kent J. L. 1999 “Developing a Logistics Service Quality Scale,” Journal of Business Logistics, 20 1 9 33
  43. 43. Mentzer J. T. Daniel J. F. Hult G. M. T. 2001 “Logistics Service Quality as a Segment-customized Process,” Journal of Marketing, 65 4 82 105
  44. 44. Kalra Ajay. Ronald C. Goodstein 1998 “The Impact of Advertising Positioning Strategies on Consumer Price Sensitivity,” Journal of Marketing Research, 35 May, 210 224 .
  45. 45. Kang G. D. James J. Alexandris K. 2002 “Measurement of internal service quality: Application of the SERVQUAL battery to internal service quality,” Managing Service Quality, 1 12, 5 278 291 .
  46. 46. KCCI PR Office 2004 “Consumer Behavior at Internet Shopping Malls” The Korea Chamber of Commerce & Industry,
  47. 47. Kettinger W. J. Lee C. C. 1999 “Replication of Measures of Information Systems Research: The Case of IS SERVQUAL,” Decision Science, 30 3 893 899 .
  48. 48. Kalakota & Whinston 1996 “Frontiers of electronic commerce,” Addison Wesley Longman Publishing Co.
  49. 49. Korea Management Association Consulting (2004~2005) “2004/2005 Call Center Korean Service Quality Index”,
  50. 50. Korea Management Association Consulting (2004~2005) “2004/2005 Korea Web Power Index(KWPI)”,
  51. 51. Krishnamurthi Lakshman. Raj S. P. 1985 “The Effect of Advertising on Consumer Price Sensitivity,” Journal of Marketing Research, 22 May, 119 129 .
  52. 52. Lars Finskud 2003 “Competing for choice-Developing winning brand strategies,” VOLA press
  53. 53. Lin J. C. Lu H. 2001 “Towards an Understanding of the Behavioral Intention to Use a Web Site,” International Journal of Information Management, 38 217 230 .
  54. 54. Liu C. Arnett K. P. 2000 “Exploring the factors associated with web site success in the context of electronic commerce,” Information & Management, 38 23 33
  55. 55. Maier F. H. 1998 “New product diffusion models in innovation management- A system dynamics perspective,” System Dynamics Review, 14 285 308 .
  56. 56. Mandelbaum A. Shimkin N. 2000 “A Model for Rational Abandonments from Invisible Queues,” Queueing Systems, Theory, and Application, 36 141 173 .
  57. 57. Media Channel, “Rankey Reports”,
  58. 58. Membrillo Annabel Ritchie-Dunham James Madrid Conrado García 1999 “Applying System-Wide Discovery Analysis to the Market Growth Model,” System Dynamics Society 17th International Conference, New Zealand, Summer.
  59. 59. Milling P. M. 1996 “Modeling Innovation Processes for Decision Support and Management Simulation,” System Dynamics Review, 12(3). 211 234 .
  60. 60. Mittal V. Ross W. T. Jr. Baldasare P. M. 1998 “The asymmetric impact of negative and positive attribute-level perfom1ance on overall satisfaction and repurchase intentions,” Journal of Marketing, 62 33 47 .
  61. 61. Moe Wendy Fader Peter 2001a “Dynamic Conversion Behavior at e-Commerce Sites,” Working Paper, Wharton School.
  62. 62. Moe Wendy Fader Peter 2001b “Capturing Evolving Visit Behavior in Click-stream Data,” Working Paper, Wharton School.
  63. 63. Morecroft J. D. W. 1985 “Rationality in the Analysis of Behavioral Simulation Models,” Management Science, 31 (7), 900 916 .
  64. 64. Morganosky M. A. Cude Brenda J. 2000 “Consumer Response to Online Grocery Shopping,” International Journal of Retail & Distribution Management, 28 1 17 26 .
  65. 65. Novak Thomas P. Hoffman Donna L. Yung Yiu-Fai 2000 “Measuring the Customer Experience in Online Environments: A Structural Modeling Approach,” Marketing Science, 19 (1), 22 42 .
  66. 66. Oliva R. Sterman J. D. 2001 “Cutting Corners and Working Overtime: Quality Erosion in the Service Industry,” Management Science, 47(7), 894 914
  67. 67. Oliva R. Sterman J. Giese M. 2003 "Limits to growth in the new economy: exploring the’get big fast’. strategy in e-commerce", System Dynamics Review, 19 83 118 .
  68. 68. Oliva R. 1996 “A Dynamic Theory of Service Delivery: Implications for Managing Service Quality,” Ph. D. Thesis, Sloan School of Management, MIT, Cambridge, MA.
  69. 69. Parasuraman A. Zeithaml V. A. Berry L. L. 1985 “A Conceptual model of service quality and its implication,” Journal of Marketing, 49 Fall, 41 50 .
  70. 70. Peterson D. Eberlein R. 1994 “Reality check: A t bridge between systems thinking and system dynamics,” System Dynamics Review, 10 10 159 174 .
  71. 71. Peterson Robert A. Sridhar Balasubramanian Bronnenberg Bart J. 1997 “Exploring the Implications for Consumer Marketing,” Journal of the Academy of Marketing Science, 25 4 329 346 .
  72. 72. Pitt L. F. Watson R. T. Kavan C. B. 1995 “Service Quality: A Measure of Information Systems Effectiveness,” MIS Quarterly, 19 173 187 .
  73. 73. Randall L. Senior M. 1992 “Managing and Improving Service Quality and Delivery,” Technical Communications(Publishing), Ltd., England, 6
  74. 74. Richins M. L. 1983 “Negative word of mouth by dissatisfied consumer: A pilot study,” Journal of Marketing, 47 68 78 .
  75. 75. Richins M. L. Root-Shaffer T. 1988 “The role of involvement and opinion leadership in consumer word of mouth: An implicit model made explicit”, Advances in Consumer Research, 15 32 36 .
  76. 76. Roughgarden J. 1998 “Primer of Ecological Theory,” Prentice-hall, Upper Saddle River, New Jersey.
  77. 77. Saltzman R. Mehrotra V. 2001 “A call center uses simulation to drive strategic change,” Interfaces, 31 87 101 .
  78. 78. Sedehi H. Boscolo F. Vaccaro N. 2000 “E-Company: A SD Model to evaluate marketing on line investment,” System Dynamics Society 18th International Conference, Bergen.
  79. 79. Singh Jagdip 1990 “Voice, Exit, and Negative Word-of-Mouth Behaviors : An Investigation across Three Service Categories,” Journal of the Academy of Marketing Science. 18(1), 1 15 .
  80. 80. Sirrka L. Jarvenpaa Todd Peter A. 1997 “Consumer Reactions to Electronic Shopping on the World Wide Web,” International Journal of Electronic Commerce, Winter 1996-97. 1 2 59 88.
  81. 81. Smith Michael F. Indrajit Sinha 2000 “The Impact of Price and Extra Product Promotions on Store reference,” International Journal of Retail & Distribution Management, 28 2), 83 92
  82. 82. Smith Michael D. Bailey Joseph Brynjolfson Erik 1999 “Understanding Digital Markets: Revise and Assessment,” in Erick Brynjolfsson and Brian Kahin, eds. Understanding The Digital Economy, MIT Press.
  83. 83. Smith R. E. Vogt C. A. 1995 “The effects of integrating advertising and negative word-of-mouth communications on message processing and response,” Journal of Consumer Psychology, 4(2), 133 151 .
  84. 84. Sterman J. D. 1987 “Expectation Formation in Behavioral Simulation Models,” Behavioral Science, 32 190 211 .
  85. 85. Sterman J. D. 1989a “Misperceptions of feedback in dynamic decision making,” Organizational Behavior and Human Decision Processes, 43(3), 301 335 .
  86. 86. Sterman J. D. 1989b “Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment,” Management Science, 35(3), 321 339 .
  87. 87. Sterman J. D. 2000 “Business Dynamics: Systems Thinking and Modeling for a Complex World,” Irwin McGraw-Hill: Boston.
  88. 88. Steuer Jonathan 1992 “Defining Virtual Reality: Dimensions Determining Telepresence,” Journal of Communication, 42 Aug.), 73 93 .
  89. 89. Sundaram D. S. Motra K. Webster C. 1998 “Word-of-mouth communications: A motivational analysis,” Advances in Consumer Research, 25 257 531 .
  90. 90. Tsai N. 1999 “Web commerce: benefits and impacts,” 28th Western Decision Science Institute Annual Meeting.
  91. 91. Urban G. Fareena S. William Qualls. 1999 “Design and Evaluation of a Trust Based Advisor on the Internet,” MIT working paper,
  92. 92. Van Ackere A. Smith P. C. 1999 “Towards a macro model of National Health Service waiting lists,” System Dynamics Review, 15(3), 225 252 .
  93. 93. Van Dyke T. P. Prybutok V. R. Kappelman L. A. 1999 “Cautions on the Use of the SERVQUAL Measure to Assess the Quality of Information Systems Services,” Decision Science, 30 3 877 89l .
  94. 94. Van Iwaarden J. van der Wiele T. Ball L. Millen R. 2003 “Applying SERVQUAL to web sites: An exploratory study,” International Journal of Quality & Reliability Management, 1 20, 8 919 935 .
  95. 95. Wan H. A. 2000 “Opportunities to enhance a commercial website,” Information & Management, 38 15 21
  96. 96. Whitt Ward 1999 “Dynamic staffing in a telephone call center aiming to immediately answer all calls,” Operation Research Letters, 24 205 212
  97. 97. Wilson W. R. Peterson R. A. 1989 “Some limits on the potency of word of mouth information,” Advances in Consumer Research, 16 23 29 .
  98. 98. Wind J. Rangaswamy A. 2001 “Customerization: The next revolution in mass customization,” Journal of Interactive Marketing, 15(1), 13 32 .
  99. 99. Boonghee Yoo Donthu Naveen 2001 “Developing a scale to measure the perceived Quality of internet shopping sites(SITEQUAL),” Quarterly Journal of Electronic Commerce, 2(1), 31 47 .
  100. 100. Zeithaml V. A. 1988 “Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence,” Journal of Marketing Research, 52 July), 2 22 .
  101. 101. Zeithaml V. A. Parasumaran A. Berry L. L. 1990 “Delivering Quality Service: Balancing Customer Perceptions and Expectations,” The Free Press, New York.
  102. 102. Zeithaml V. A. 2000 “Service quality, Profitability, and the economic worth of customers: What we know and what we need to learn,” Academy of Marketing Science. 28 67 85 .
  103. 103. Zellweger 1997 “Web-based Sales: Defining the Cognitive Buyer,” Electronic Market, 7 3 10 16 .

Written By

Soo Wook Kim and Kanghwa Choi

Published: February 1st, 2008