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Assessing the dynamic behavior of online Q&A knowledge markets A system dynamics approach Mostafa Jafari, Roozbeh Hesamamiri, Jafar Sadjadi and Atieh Bourouni

Online Q&A knowledge markets 341 Received January 2012 Revised February 2012 Accepted March 2012

Department of Industrial Engineering, Iran University of Science and Technology (IUST), Tehran, Iran Abstract Purpose – The objective of this paper is to propose a holistic dynamic model for understanding the behavior of a complex and internet-based kind of knowledge market by considering both social and economic interactions. Design/methodology/approach – A system dynamics (SD) model is formulated in this study to investigate the dynamic characteristics of complex interactions in a fee-based online question & answer (Q&A) knowledge market. The proposed model considers the dynamic, non-linear, asymmetric, and reciprocal relationships between its components, and allows the study of the evolution of the market under assumed conditions. Findings – Some illustrative results show that: this market is very sensitive to the prices that the customers choose; low-priced questions are as important as high-priced ones; gradually increasing experts’ proportion of a question’s price reduces customer satisfaction and experts’ reputation; and training programs for experts result in higher customer satisfaction and researchers’ reputation. Furthermore, three types of customers are identified and discussed. Practical implications – This model can be used to change, manage, and control this market and also helps to design new similar markets. In addition, the proposed model helps to observe the behavior of a market under one or more policies before applying to the real world. Social implications – Since GA was shut down in 2006, the implications of this research serve as a strategic tool (strategic evaluation software) for understanding and examining the effects of policies for many existing similar Q&A business models. Furthermore, the SD approach can provide new insights into the field of online Q&A knowledge markets and overcome traditional econometric treatment of data for understanding the dynamic behavior of these markets. Originality/value – Understanding the complex social and economic behavior of Q&A markets is one of the most important concerns for academics and practitioners in the areas of online markets’ management. The paper shows how SD can provide attractive insights into the field of online fee-based knowledge markets based on a qualitative and quantitative modeling. However, the background literature lacks a holistic view of these kinds of markets. Keywords Knowledge economy, Information exchange, Strategy analysis, System dynamics (SD), Google Answers (GA), Knowledge management, Internet Paper type Research paper

1. Introduction Today’s business environment has been described as a “knowledge economy” (Drucker, 1969), where knowledge is a central input, activity, and output of many

Program: electronic library and information systems Vol. 46 No. 3, 2012 pp. 341-360 q Emerald Group Publishing Limited 0033-0337 DOI 10.1108/00330331211244887

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organizations. In this new era, knowledge is regarded as the most fundamental asset responsible for organizational success (Grant, 1996; Kogut and Zander, 1992). This concept has led much of the academic research to focus on knowledge as a good or service to be transferred by market transactions governed by the pricing mechanism known as the knowledge market (Stewart, 1996; Davenport and Prusak, 1998). The knowledge market enables, supports, and facilitates the mobilization, sharing, and exchange of information and knowledge among providers (sellers) and users (buyers) (Simard, 2000). Both the buyer and seller believe they benefit and gain utility in trading in the knowledge market (Mireille and Nijbof, 2005). In recent years, there has been a steady growth of online digital knowledge exchanges, such as knowledge banks (Tang, 2009), knowledge stores or malls, expert knowledge exchanges, intellectual property exchanges, stock market or investment knowledge exchanges, e-education or e-learning exchanges, community-oriented or social capital knowledge networks, intellectual capital exchanges, vertical or industry-specific knowledge markets, B2B knowledge exchanges, knowledge auctions, idea markets, and knowledge grids. Among all of these online knowledge business models, expert knowledge exchanges web sites are well known for facilitating the exchange of knowledge between knowledge buyers or seeker and domain experts (Roush, 2006; Shah et al., 2009). A variety of question and answer (Q&A) knowledge markets exist or have existed, such as answers.google.com, answerbag.com, askville.com, epals.com, exp.com, expertcentral.com, experts-exchange.com, fellowforce.com, freeiq.com, inforocket.com, and keen.com. Some of these web sites specialize in a specific area. For example, experts-exchange.com is a specialized service for computer and information technology-related issues, such as software programming and hardware troubleshooting. Google Answers (GA) was one of the most significant online markets before its shutdown in 2006. It was a successful fee-based knowledge market where experts sell their expertise to askers for a price quoted by the askers. In this market, Google hires knowledge experts to answer questions posted by customers on the online knowledge market. In this model, customers log into the market, ask their questions, and set a price for the answer (from $2 to $200). Since they do not know who will answer their question, Google recommends that customers set a higher price for answers requiring research. Setting a too-low price might result in a late or even no response. This means that the higher a price the customer offers, the more likely their question is to get answered quickly. When a question is posted on the market, all the experts have the same chance to read the question and decide whether to lock it for answering. While the question is locked, other experts cannot answer it. After a certain period of time, if the locker cannot answer the question, the question is again open to all. Therefore, experts try to catch questions within their knowledge areas as quickly as they can. It is not possible to negotiate the price with the asker in this market. Google determines that 75 percent of the price of an answer goes to the expert who answers it and, thus, 25 percent of the price goes to Google. After receiving an answer from an expert, customers can tip the expert from $1 to $100. As a reputation mechanism, each customer gives 1 to 5 stars to each expert based on the quality of the answer. In this market, any expert or customer may start discussions below any question. In this kind of market, there are three main actors: (1) Google; (2) customers; and (3) experts.

Each of these actors has a significant influence over the market with their decisions. For example, Google determines the price and tip ranges for questions, hires new experts, and starts training programs for experts. Customers determine the questions’ price, which is the most important factor of the market. They participate in reputation systems and discussions and can tip any expert. Experts also decide whether or not to answer a question with a determined price. The decisions of all of these actors have influence over each other, as when a question with a higher price is more likely to receive a quicker, longer answer (Chen et al., 2008). Even the announcement of GA has inspired others to adopt, accelerate, or even revive their own similar service such as Yahoo! Advice (LiveAdvice). Many other answering services were emerged with the idea introduced by Google. However, this rapid growth was doubtful since GA shutdown in 2006. Following, a considerable amount of literature has been published on GA before and after 2006 to answer many questions that have been raised to challenge other similar web sites and the considerable amount of investments. While the research to date has tended to focus on economic or social aspects of the market, a holistic, systemic, and dynamic view to the problem is ignored. On the other hand, considering the growing number of customers interested in acquiring knowledge from online markets (Chen et al., 2010) and the complexity of the inter-relationships of the markets’ various concepts, it is necessary to assess the dynamic behavior of these markets. Thus, the objective of this research is to understand market dynamics in the specific context of fee-based Q&A knowledge markets via the case study of GA. In order to study the market’s behavior, we deal with the complexity of analysis through the methodology of system dynamics (SD) which is able to consider dynamic, non-linear, asymmetric, and reciprocal relationships between attitudinal and behavioral variables and allows analysis of the progress of the market under assumed circumstances by considering both social and economic elements (Wu et al., 2011; Dutta, 2001; Cho et al., 2008; Sterman, 2000; Yeon et al., 2003; Lin and Liu, 2008). This paper proceeds as follows. The next section reviews relevant literature and describes previous research on GA. In the third section, SD methodology is introduced, following in the fourth section, we propose the conceptual dynamic model of GA. In the fifth section, the formulized dynamic model of the system is developed and the future system progress under hypothesized simulated conditions is simulated in order to understand the effects of specific key variables. In this section, three hypotheses are tested on the basis of the model and in the following section, results are discussed, implementations are described and further research is communicated. 2. Research background GA’s predecessor was Google Questions and Answers, which was launched in August 2001. This service involved Google staffers’ answering questions by e-mail for a flat fee of $3. GA was launched in April 2002 and came out of beta in May 2003. It was receiving more than a hundred question postings per day when the service ended in December 2006. Since its launch in 2002, this new online knowledge market has been the subject of many studies, even after its closure in 2006, as depicted in Figure 1. Edelman (2004, 2012) was the first to study GA’s answerer behavior by gathering a database of 40,000 answers and questions from April 2002 to November 2003. For each question on the web site, he observed the question (text, title, categorization within GA’s taxonomy), the time at which it was asked, the payment amount offered by the asker, and the

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Figure 1. The timeline of GA studies

asker’s username. For answered questions, he observed the time at which the question was answered, the answer itself, and the answerer’s username. By analyzing the database, he concluded the following trends in answerer’s behavior: . more experienced answerers provide answers with the characteristics askers most value and receive higher rankings as a result; . answerers’ rate of earnings increases with experience; . answerers who focus on particular question categories provide answers of higher quality; and . answers provided during the business day receive higher payments per hour. This study showed that over 78 percent of the questions had a fee of less than $20 and very few had fees of $50, $100, or $200. He also found that the number of characters in each answer related positively to the asker’s quality perceptions and the answerer’s experience. The more characters the answerers provided, the more tips and stars they gained, and vice versa. Moreover, the more questions they answered, the more experience they had in the market, which also caused a better reputation for their quality. In January 2005, Regner (2005) studied the pricing and tipping behavior of GA users. He developed a model based on reciprocal theories of social preferences empirically tested by field data. Based on this study, he showed that a significant amount of users of GA are motivated by social preferences in pricing and tipping. Answerers (GA experts) appear to adjust their efforts based on the users’ previous tipping behavior. Rafaeli et al. (2005, 2007) described the behavior of about 500 GA experts over a 29-month period. They found that, although the mechanism of this market is simply fee-based, the labor economics of response to price and tip alone does not paint the full picture. There also exist non-monetary incentives, such as “star” ratings from recipients and feedback in the form of comments, which account for some of the variance in participation. They tested thousands of answers to prove that GA experts are associated with a hybrid of material (economic) and social motivators.

Zhang and Jasimuddin (2008) examined the different levels of pricing strategies for GA. Based on the assumptions that consumers optimally price their questions to obtain answers and a firm maintains the online knowledge market by determining the optimal price allocation to experts, they identified two types of consumers: spin-off and mainstream. While spin-off GA users are casual in their use of the service, mainstream users are customers who utilize GA seriously to derive additional utilities from this knowledge market (Zhang and Jasimuddin, 2008). In addition, they investigated how the firm can use minimal and maximal posting prices to regulate the knowledge market. Chen et al. (2008) investigated the effects of various design features of online knowledge markets by conducting a field experiment at GA. They studied the effects of prices, tips, and reputation systems on answer quality and answerers’ efforts by posting real reference questions from the Internet Public Library to GA under different pricing schemes. They found that posting a higher price led to a significantly longer, but not better, answer, while an answerer with a higher reputation provided significantly better answers. They also highlighted the significance of reputation systems for knowledge markets. Raban (2008) analyzed data representing four years of activity by 523 experts who gave about 52,000 answers on the GA web site. The analysis revealed that for the frequent answerers the tip was followed by social incentives, such as comments and ratings. Occasional experts’ tips were followed by their fees for answers and then by comments. Raban (2008) suggests that a pure economic incentive serves as enticement, but social incentives encourage persistent participation by GA experts and finally lead to higher average economic gains. Reviewing the previous literature reveals that as a well-known and best practice (Zhang and Jasimuddin, 2008) of online fee-based knowledge markets, GA has been the focus of research for many years. Understanding this kind of market is a complex task for academicians and practitioners due to internal, tightly coupled elements and feedback structure and nonlinear, asymmetric relationships between concepts. Furthermore, none of the previous studies consider these features of GA as a whole dynamic system. Thus, the background literature on GA lacks a holistic view of this market. This view for studying any phenomena permits the representation of mental models of the research problem as a dynamic system. The objective of this paper is to overcome this shortcoming and understand the structural causes that provoke the behavior of GA over time. 3. Research methodology As one of the first responses to the shortcomings of Operations Research (OR) and other management science techniques for complex problems, such as large numbers of variables and nonlinearity, an idea now known as SD was introduced by Forrester in the 1960s at the Massachusetts Institute of Technology (MIT) (Forrester, 1961). With background knowledge of electric circuits, servo-mechanism theory, and feedback control theory, he developed a powerful method and a set of tools to model and analyze problems in complicated situations. SD is a policy modeling methodology based on the foundations of decision making, feedback mechanism analysis, and simulation. Decision making focuses on how actions are to be taken by decision makers, such as GA’s askers, answerers, and owner company. The feedback loop deals with the way generated information provides insights into decision making and affects decision making in future similar cases. The simulation provides decision makers with a tool to work in a virtual environment where they can view and analyze the effects of their decisions in a projected future.


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Figure 2. Causal loop and stock-flow representation

The main purpose of SD is to understand and model complex and dynamic socio-economic systems. It employs concepts of nonlinear dynamics and feedback control in terms of its two major types of model: the causal loop diagram (CLD) and stock flow diagram (SFD). A CLD consists of variables connected by causal links shown by arrows with a polarity. A positive link (denoted by “ þ ” on the arrow) implies that if the cause increases (decreases), the effect increases (decreases). A negative link (denoted by “ 2 ” on the arrow) implies that if the cause increases (decreases), the effect decreases (increases) (Sterman, 2000). A simple example is depicted in Figure 2. If the birth rate increases or decreases, the population correspondingly increases or decreases. Also, if the population increases, the death rate increases. But if the death rate increases, the population decreases. There are two feedback loops in this example. The positive reinforcement loop on the left indicates that increased birth rate causes a larger population. This positive feedback should generate population that continues to grow. The second feedback loop on the right is negative reinforcement (or “balancing”). Clearly, population growth cannot continue forever, because as there are more and more people, there is also more death. In this example, if the birth rate and death rate are equal, then the population would not change over time. However, if the birth rate is greater than the death rate, the population will increase. Causal loops are immensely helpful in eliciting and capturing the mental models of decision makers in a qualitative manner. Interviews and conversations with people who are a part of the system are important sources of the quantitative and qualitative data required in such modeling. Stocks and flows, along with feedback, are the central concepts of dynamic systems theory. Stocks are accumulations as a result of a difference in input and output flow rates to a process/component in a system. Stocks give the systems inertia and memory based on which decisions and actions are taken. All stock and flow structures are

composed of stocks (represented by rectangles), inflows (represented by arrows pointing into the stock), outflows (represented by arrows pointing out from the stock), valves, and sources and sinks for flows (represented by clouds). Figure 2 shows a simple example of SFD. The value of stock at t time would be made by adding the initial stock value ðStockt2dt Þ to the input and output difference during the time dt. Stock-flow formulas are illustrated as follows: Stockt ¼ Stockt2dt þ dt · ð Inflowt2dt 2 Outflowt2dt Þ d ðStockÞ ¼ Inflowt 2 Outflowt dt As shown in Figure 2, the variable entitled “population,” is only depicted as the stock (unit: person), while both “birth” and “death” (unit: person/year) are presented as the flows. Additional variables for the simulation are also added to SFD. Fractional birth rate describes birth rate per person. For example, for a couple that has four children throughout their lifetime, the fractional birth rate can be 2 (unit: dimensionless). If the current population is one thousand, the expected number of births on the current population will be one thousand (birth rate ¼ fractional birth rate £ population). Meantime, birth is a long process throughout one’s lifetime, one that belongs to the population stock. Subsequently, yearly birth rate, “birth,” can be obtained by birth rate/average lifetime. The age distribution of the population stock is assumed to be a uniform distribution, and the male-to-female ratio is assumed to be one to one. Similarly, yearly death rate, “death,” can be expressed by an equation, population/average lifetime (expectancy). Birth increases the population and proportionally increases death. This will lead to the decrease in population, which in turn decreases birth. Consequently, a non-liner relationship exists among variables, so population cannot be calculated through linear equations. SD methodology has a long history in different e-business and Internet research areas, such as customer trust analysis in B2C e-business (Winch and Joyce, 2006), ICT supported education (Haugen et al., 2010), e-crime behavior (Chiu et al., 2010), Internet-based crisis communication management (Dong et al., 2011), Internet marketing (Lin and Liu, 2008), and Internet growth modeling (Dutta and Roy, 2004). In this study, the authors adapted SD based on the dynamic complexity (Sterman, 2000) of the market under investigation. Dynamic complexity of the market arises because of characteristics such as constant change in the market, the interactions and feedbacks between markets’ actors, non-linear relationships between concepts, and history-dependency. 4. Feedback structure of GA This study has focused on studying the GA market based on its basic concepts, including Google Revenue, Expert Proportion, Expert Payoff, Expert Reputation, Expert Risk, Customer Satisfaction, Expert Training, Answering Questions, Suggested price, Customer (Asker) Payoff, Customer Perception of Receiving Answer, Question Utility, Additional Utility, and Ask New Questions. The causal structure of these concepts is depicted in Figure 3. As discussed earlier, answering questions is the most important function of the GA market. Thus, the Answering Question concept, which shows the number of answered questions with acceptable quality, is considered in CLD. Expert Payoff implies the tendency of a specific expert to answer a specific question. When a new question is


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Figure 3. Causal loop diagram of GA

posted on the market, experts decide whether to answer it. If the expert’s payoff is more than a threshold, he or she would try to lock and answer the question. Another important concept is the perception of customers on getting their answers by utilizing the market. This concept is involves trust in the market and depicts the tendency of customer to post a new question or further questions to the market. Expert Proportion is the amount of the fee allocated to the expert. Expert Reputation is the creditability of experts which depicts an estimation of her answers’ quality. On the other hands, Expert Risk is the potential that experts’ participation in the knowledge market leads to a loss or an undesirable outcome. Customer Satisfaction is a measure of how provided answers supplied by the knowledge market meet or surpass asker expectations. Customer (Asker) Payoff implies the tendency of a specific customer (asker) to ask a question or participate in the market. Customer Perception of Receiving Answer is a concept which shows the amount of asker’s expectation to receive her answer from the market. By considering these concepts and the actual behavior of the market, five important feedback loops are identified, as presented in Figure 3 in alphabetic order. 4.1. Answer quality control loop (A) In general, the experts at GA try to maintain their reputation. Based on previous studies (Rafaeli et al., 2007), good experts try to be highly active on the market. This establishes their reputation of being active on the market and ensures more income. As depicted in the CLD, the more questions being answered by experts, the more trust is developed by customers. This results in more stars and a good reputation for active experts. Experts with more stars are more likely to send high quality answers to the market. Thus, when experts have better reputations, they have more risk in sending answers to the market. Since experts try to keep their reputation for quality, they mitigate risk by not sending answers to the market. They have less motivation to answer a question. This balancing

loop implies that reputation is very important for experts, and they often do not risk it by sending low quality answers to the market (Regner, 2005; Raban, 2008). 4.2. Expert training loop (B) Google assigns some percent of its revenue to experts’ training programs. This training would decrease the risk of their sending a bad answer and of their not answering questions. Thus, with training the experts are able to answer more questions on the market, and consequently, the revenue of Google increases. 4.3. Revenue control loop (C) When there is more income for Google, it is possible for Google to increase the experts’ proposition. This increase in proposition would decrease revenue at first, but over time more income would encourage experts to increase their participation in the market. 4.4. Revenue-percentage loop (D) When there is more revenue for GA, Google can increase the experts’ percentage so they gain more by answering questions. This encourages the experts and increases their payoff to participate in the market. 4.5. Price control loop (E) Customers are willing to set lower prices for their questions, but they want the market to react to their price and send the answer faster and with more detail. Thus, based on the customer’s own utility in receiving answers from the market, he or she determines his or her payoff. If the customer posts a question with a low price, the expert’s payoff decreases, so the question is less probable to receive an answer from the market. This influences the customer’s perception of receiving an answer for their offered price. 5. The dynamic model The dynamic model is proposed through writing the equations that depict the relationships between the elements of the system. Vensim PLE (Ventana, 2011) is utilized for building the model and running the simulations. Based on the previously described CLD, the stock-flow diagram of the GA market is depicted in Figure 4. In the proposed dynamic model, four basic structures are depicted including dynamic answering structure, dynamic structure of posting new questions, dynamic revenue structure, and dynamic structure of expert training. The dynamic answering structure reveals the behavior of experts at GA. The “Unanswered Questions” level is transmitted into the “Answered Questions” level with an Answering Rate. Minimum Income and Expert Payoff are concepts (variables) that influence experts’ Answering Rate. Minimum Income is a variable that shows the minimum requested income for an expert. This structure is formulized as below: Answering Rate ¼ IF THEN ELSE ðQuestions .¼ 20; IF THEN ELSE ðExpert Payoff . Minimum Income; 1; 0Þ; 0Þ R Answered QuestionsðtÞ ¼ Answered Questionsð0Þ þ ½Answering Rate 2 0dt Answered Questionsð0Þ ¼ 0


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Figure 4. Stock-flow diagram of GA

In this structure, if the Expert Payoff is greater than his or her Minimum Income, then the Answering Rate would be 1 at each time step. The initial value for Minimum Income variable is determined as 0.05. The most important factors influencing the Asking Rate variable are Asker Payoff and Price Control. Other variables influencing Asker Payoff include Utility of Question, Additional Utility, Cost of Other Ways, Prediction of Answering, and Price. As discussed earlier, if the customer strongly expects to receive an answer, he or she set a lower price for the question. Thus, Prediction of Answering variable affect Asker Payoff and Price variables. The Price Control variable is the threshold for deciding whether or not to post the question to the market. If the customer sets a higher value for his or her threshold, he or she would select a higher price for their question. When customers send a higher priced question to the market, they want to receive a more detailed answer of higher quality. Thus, the dynamic structure of posting new questions is formulized as below: R QuestionsðtÞ ¼ Questionsð0Þ þ ½Asking Rate 2 Answering Ratedt Questionsð0Þ ¼ 0 Asker Payoff ¼ ðUtility of Question þ Additional Utility 2 PriceÞ * Prediction of Answering

þðUtility of Question þ Cost of Other WaysÞ * ð1 2 Prediction of AnsweringÞ

AskingRate ¼ IF THEN ELSE ðAsker Payoff . Price Control; 1; 0Þ As depicted in the model, Google Profit Rate is a function of Answering Rate, Price, and Research Proportion. There is also a negative effect of Refund Requests upon the Profit Rate. Thus, the dynamic revenue structure is formulized as below: Profit Rate ¼ Answering Rate* Price* ð1 2 Expert ProportionÞ R Company ProfitðtÞ ¼ Company Profitð0Þ þ ½Profit Rate 2 0dt


Company Profitð0Þ ¼ 0 The dynamic structure of expert training depicts a simple training mechanism for improving the skills and knowledge of experts at GA. In this context, Google spends some of its profit on training programs. This is performed by different workshops, training courses, and conferences. This structure is formulized as below: Training Rate ¼ Company Profit* Train Percentage R TrainingðtÞ ¼ Trainingð0Þ þ ½Training Rate 2 0dt Trainingð0Þ ¼ 0 5.1 Model validation In order to ensure that the proposed model would provide valuable insights, a transparent and explicit validation process was required to help build an acceptable confidence level in the model. The objective was to complete a deeper understanding of the model, extend the view of the research team, and discuss potential applications more thoroughly. Sterman (2000, 2001, 2002) proposed 12 tests for building confidence in a SD model. Suitability tests determine whether the model is suitable for the problem it addresses, while consistency tests study whether the model is consistent with the share of reality it attempts to capture (Richardson and Pugh, 1981). The first approach, as described above, was to calibrate the model against experts’ opinions. In this study, we sent out a web-based questionnaire to 87 former GA experts. These GA experts tend to have a good level of working experience at GA. Of the 87 possible questionnaires, 57 were gathered and analyzed. As depicted in Table I, respondents have an average 42.6 months of experience at GA with around 670 answered questions and a rate (Edelman, 2004) of 4.498. Thus they represent the most active and experienced former GA experts. Many of these experts started with GA’s launch and moved to Uclue (uclue.com) after GA shut down. The survey was built and analyzed by QuestionBuilder software.

Experience title Work experience at GA Number of answered question Rating (upper limit ¼ 5)

Average 42.6 months 670 4.498

Table I. Work experience profile of respondents

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Based on our experts’ community, the most important factor in answering a question is its price. Expert risk, complexity of the question, and the tipping behavior of the customer are secondary elements in locking a question. From the viewpoint of customers, the most important factor is defined as receiving an answer. After getting an answer, the following are listed by decreasing level of importance: the quality of the received answer, the speed of receiving it, its length, and provided URLs and external links. They also confirm that there is a close relationship between ratings and tips, and better reputations result in more risk for experts. Particularly, they confirm the model structure and utilized formula. 5.2 Simulation parameters and results The proposed dynamic model is simulated by using Vensim PLE software. The simulation was run based on simulation control parameters, and user-defined parameters. The final time for the simulation is 1,000 hours. The implementation of the model was for a specific fee-based online knowledge market (GA), whereby a time horizon of 1,000 hours was used based on inputs from former GA experts. The initial time for the simulation is hour ¼ 0, and the time step is 1. The user-defined parameters of the model are thresholds and utility-like in nature, thus they are dimensionless and bounded between 0 and 1. Table II depicts user-defined parameter names, their related description, and values based on existing sources of information. Afterwards, the model is implemented based on its parameters, and the following graphs show the dynamic behavior of the market. As shown in Figure 5 (A), in the 1,000-hour period of simulation, the number of answered questions continuously increases. Figure 5 (B) implies that the Prediction of Answering for askers (customers) grows fast at the first hours of simulation, but it remains steady (about 0.45) after 80 hours. Figure 5 (C) depicts the behavior of Expert Reputation, which starts to grow fast. After about 300 hours, however, it becomes relatively steady at the level of 0.44. Asker Satisfaction varies, but Figure 5 (D) shows that its tendency is to become constant around 0.57. The behavior of Expert Risk is shown in Figure 5 (E). It decreases, but at the end of simulation, there exists a level of 0.41 of risk for each expert. Finally, Figure 5 (F) implies the trend of Google profit. 5.3 Hypotheses testing The main purpose of this paper is to develop a dynamic model of an online knowledge market in order to understand its behavior. In this study, we examined three dynamic hypotheses. These hypotheses are: H1. The higher prices for questions cause the market to fail over a period of time. H2. Decreasing the experts’ percentage results in less customer satisfaction and lower experts’ reputations. H3. Training programs for experts result in increased customer satisfaction and experts’ reputations. The first two hypotheses are selected based on the fact that they are among the most common beliefs of experts and market managers in these kinds of knowledge markets.

Parameter name Utility of question

Value Description 0.63

Based on Zhang and Jasimuddin (2008), customers are willing to post their questions on the market and spend a specific amount of money because of utility. According to Rafaeli et al. (2005), this parameter is determined by the following formula: Number of Answers with Rating 23869 ¼ ¼ 0:63 Number of Answers 37971

Additional Utility

0.2


By receiving answers from the market, each asker would have a change in their utility. He or she tries to maximize his or her utility of using the knowledge market (Zhang and Jasimuddin, 2008). This parameter is determined by the following formula (Rafaeli et al., 2007): Number of Answers with Tip 7504 ¼ ¼ 0:2 Number of Answers 37971

Question’s complexity

0.52

This parameter is determined by the following formula (Rafaeli et al., 2007): Number of un 2 Answered Questions 39704 ¼ ¼ 0:52 Total Number of Questions 77675

Skill of Asker

0.5

The asker’s skill can also be estimated by the following formula: Number of Questions with Discussion 39436 ¼ ¼ 0:5 Total Number of Questions 77675

Cost of Other Ways

0.5

This parameter is determined based on the asker’s skills and the complexity of the question. An asker with more skill would have a lower cost of other ways; if the question is highly complex, then cost of other ways would increase. Since the complexity of the question and the asker’s skill parameters are approximately 0.5, the cost of other ways is also assumed to be 0.5.

Training Fraction

0.05

This parameter determines how much of the income is assigned to the training programs for experts. It is determined as 0.005 for this simulation.

Refund Request

0.02

This represents unsatisfied customers of GA. The amount is assumed to be 0.02 based on estimation of former GA experts.

Minimum Income

0.05

Each expert had a different perception of income by utilizing the market. This parameter determines the threshold of participation for experts based on the price of the question.

On the other hand, since the model is able to test the outcome of improving programs, the third hypothesis is added. To test the first hypothesis, we used a Vensim Lookup function based on price frequency calculated by Edelman (2004). The Price variable is formulized as below:

Table II. User-defined simulation parameters

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Figure 5. Simulation results

Proposed price ¼ Price Look up ðRANDOM UNIFORM ð0:0001; 1; 0:1Þ * Prediction of answering * ð1 2 Price ControlÞÞ

In order to test the first hypothesis, we have produced the simulation results after a change in Price Control from 0 to 0.4. As presented in Figure 6, when customers increase their price, their prediction of receiving an answer also increases. Sending questions with higher prices to the market results in more answers provided by experts. Thus, experts would be more active, and their reputation would greatly increase. However, since the asker likes to set the lowest possible price to his or her question, the experts’ threshold of payoff would increase by sending higher priced questions. When the customer is more satisfied with the market, he or she continuously sets a higher Price Control threshold. Therefore, the number of questions will decrease. This behavior happens because the market trends towards answering expensive questions. When all askers set higher prices to their questions, in time it is not possible for experts to meet the requirements of askers. Thus, the number of questions decreases, and the market will eventually fail.


Figure 6. The effect of Price on Prediction of Answering, Answered Questions, and Expert Reputation

In this situation, the expert’s risk dramatically increases, which causes Expert Payoff to decrease. Consequently, when the customer increases his or her Price Control threshold, the number of total questions declines. Because customers send fewer questions to the market, Total Questions and Answered Questions are going to be equal after 750 hours. In this period, Google sees more profit, but it is not consistent. Based on Google’s decision, 75 percent of the price of the questions is dedicated to the expert who answers it. The remaining 25 percent would remain at Google for revenue, and other costs. As discussed earlier, we assume that the Expert Proportion is affected by Company’s Profit. Thus, In order to test the second hypothesis, we have used a Lookup Function. From Company Profit 0 to 50, the Expert Proportion is determined as 0.6. When profit increases from 50 to 100, it would be 0.65. Finally, for profit more than 100, the Proportion remains 0.75. Company Profit is a variable which depict a coefficient of actual profit of the company. By applying this policy, Google would increase the Proportion of experts based on its profit. Figure 7 shows that if Google considers this strategy, it will have more profit. However, the Prediction of Answering, Asker Satisfaction, and Expert Reputation would decrease. In order to test the third hypothesis, it is assumed that Google can spend some proportion of its revenue on training programs for experts. The simulation results depict that by changing the Training Fraction from 0 to 0.005 (0.5 percent), Google is able to increase Prediction of Answering, Asker Satisfaction, and Expert Reputation. The proposed model confirms this behavior for GA.

6. Three types of customers In order to study the behavior of the market, we have distinguished three types of customers at GA by considering different parameter values. These customers are

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Figure 7. The effects of growing Expert Percentage based on Company Profit

identified by using their Price Control threshold, Questions Utility, and Additional Utility (Table III). Type 1 customers are askers who have a very low utility for their questions. On the other hand, their utility would not increase much by receiving answers. This type of customer does not set a high price for their questions and always tries to set the lowest possible price ($2 to $10). Type 2 askers have an average utility for their questions, and, after receiving an answer, their utility will increase to some extent. They set an average price for their questions (normally from $5 to $50). Type 3 customers are askers who are really utilizing the knowledge market to gain additional utility. They also have rather high utility for their questions. Type 3 customers set higher prices for their questions ($50 to $200). Considering these three types of customers, we have performed the simulation depicted in Figure 8. After performing the simulation with the proposed types of customers, the behavior of the market is captured in Figure 8. As discussed earlier, the market is very sensitive to the price, so Type 3 customers cause more Answered Questions on the market because they post questions with better prices to the market. This will encourage the experts to answer those questions rather than low-priced ones. Furthermore, answering more questions results in more profit for Google. Thus, Type 3 customers are more profitable for the market owner.

Table III. The three types of customers

Price control Question utility Additional utility

Type 1

Type 2

Type 3

0.2 0.3 0.1

0.3 0.63 0.2

0.4 0.8 0.45


Figure 8. The behavior of GA based on three types of customers

Zhang and Jasimuddin (2008) identified two types of customers and suggested eliminating spin-off customers (Type 1). However, the GA behavior shows that Prediction of Answering and Customer Satisfaction for Type 2 are less than for Types 1 and 3. Type 1 customers set a lower price to their questions and thus have a high prediction of receiving answers. However, Type 3 customers also have a high prediction of receiving answers by setting higher prices to their questions. The behavior depicts that the market response to lower-priced and higher-priced questions will be faster than to average-priced questions. Type 1 customers increase the reputation of GA experts more than Types 3 and 2. Type 3 customers also have a higher effect on the proportion of experts because they post higher-priced questions.

7. Discussion, implications, and further research The study reveals a number of graphs demonstrating the situation, behavior, and reaction of the GA market under stated conditions. A graphical user interface was built as a simulation for the management team to enable them to alter certain variables and then see how the market would behave.

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Due to the limited space of the article, only a few selected graphs to show the behavior of some key indicators in the system are presented and only four hypotheses are tested. However, the proposed model is able to describe dynamically all the key variables as shown in Figures 3 and 4. The model was conceptualized so that it considers all five feedback loops presented in the model. Furthermore, the following suggestions are developed for managers of many existing similar Q&A business models: . Based on the best practice of GA, consider the importance of price selected by customers. Do not try to eliminate lower-priced questions from the market with marketing plans or policies because this results in more risk for experts and customer dissatisfaction. Based on this behavior of the market, three types of customers are identified. Simulation results show that Type 1 and Type 3 customers are both more important than Type 2 customers. . Increasing the experts’ percentage of the fee does not seem to be reasonable practice. It lowers the reputation of experts. . Establish training programs, conferences, and workshops for experts. These will have a considerable positive effect on owner’s profit. The results from using the dynamic model describe some assumptions about the causes of GA’s failure. The authors would like to continue by analyzing failure root causes of GA and studying why this market was discontinued. The dynamic model can also be developed and formulized more precisely in future works. 8. Conclusion Just as in all other markets, in online Q&A knowledge markets different strategies have important consequences for maximizing profits, gaining market share, and finally surviving in the new and challenging e-business environment. Thus, the most important question for these kinds of businesses is what strategies to implement and how all stakeholders, including consumers, experts, and the owner company, can benefit from them. Customers can simply miss good answers to their questions by setting too low a price for them, and they can also spend too much money on inferior information. On the other hand, experts may encounter problems in selecting questions in their field of interest when confronted with irregular prices. And, finally, the whole business might be in danger if the profit does not satisfy its supervisor or owner company. Since there are many effective but nonlinear variables for selecting these strategies, a dynamic model has been developed to explain the behavior of the system under investigation. The purpose of this dynamic model is first to understand the behavior of a special kind of fee-based online knowledge, and then to examine the hypotheses. This model can be used to change, manage, and control this market and to help design new similar markets. The above discussion clearly illustrates how a dynamic simulation model can be linked to normative decision making as well as serve as a tool to gain insights into policy levers for any similar online fee-based Q&A knowledge market rather than GA. References Chen, Y., Ho, T. and Kim, Y. (2008), “Knowledge market design: a field experiment on Google Answers”, Working Paper, available at: www.si.umich.edu/, yanchen (accessed September 15, 2011).

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