KR101188268B1 - Online game item purchase index analyzing system and method thereof - Google Patents

Abstract

A system for analyzing an online game item purchase index, comprising: extracting a database storing a result of a questionnaire for an online game and a questionnaire item result for game item value, character homogeneity, game satisfaction, and item purchase intention from the database And an analysis unit for analyzing the online game item purchase index using the extracted questionnaire result and the structural equation model, wherein the questionnaire result provides a questionnaire item and a questionnaire result data.

Description

Online game item purchase index analysis system and method {ONLINE GAME ITEM PURCHASE INDEX ANALYZING SYSTEM AND METHOD THEREOF}

The present invention relates to an online game item purchase index analysis system and method thereof.

Recently, with the expansion of wireless communication networks and data transmission speeds as well as the Internet, computer games or online games have been activated. These online games were initially based on the Internet in the form of MUG (Multi-User Game), which adds graphic elements from MUD (Multi-User Dimension, Multi-User Dungeon, Multi-User Dialogs). It's spreading rapidly.

In mug games, games that use avatars, which are the ego of gamers, are mainly used.Avatar is a kind of icon that game users can select. It is the alter ego that proceeds the game while performing the action. The game user may proceed with the game using the avatar, and as the game progresses, collecting game items may improve the ability of the avatar. An item refers to a game tool in a game, and may be used to improve an avatar's game ability or to enhance a visual effect.

On the other hand, the online game item providing method according to the prior art was a one-way providing method that does not reflect the requirements of game users. Online game item designers have only benchmarked items provided by existing online games, or simply reflect the needs of a small number of gamers. Therefore, in-depth analysis of why game users purchase online game items, that is, what factors influence online game item purchase intentions, has not been made. However, in view of the increasing proportion of game items in online games, the need for a system that can analyze the intention to purchase online game items has increased.

SUMMARY OF THE INVENTION An object of the present invention is to provide a system and method for analyzing an online game item purchase index by using factors and structural equation models that affect the online game item purchase.

As a technical means for achieving the above-described technical problem, the system for analyzing the online game item purchase index according to the first aspect of the present invention, a database storing the results of the questionnaire for the online game, the game item value of the database And an extractor for extracting a questionnaire item result for character homogeneity, game satisfaction, and item purchase intention, and an analysis unit for analyzing the online game item purchase index using the extracted questionnaire item result and a structural equation model. The item results include questionnaire items and question item result data.

In addition, the method for analyzing the online game item purchase index according to the second aspect of the present invention, generating a database according to the survey results for the online game, game item value, character homogeneity, game satisfaction of the generated database And extracting a questionnaire item result for item purchase intention and analyzing the online game item purchase index using the extracted questionnaire item result and a structural equation model, wherein the questionnaire item result is a questionnaire item and a questionnaire. Contains item result data.

According to any one of the above-described problem solving means of the present invention, the online game item purchase index-related elements are divided into game item value, character homogeneity, game satisfaction, and item purchase intention, and the detailed items of each element are classified by a structural equation model. By making statistical estimates, you can effectively analyze the online game item purchase index.

In addition, according to any one of the above-described problem solving means of the present invention, sub-items related to item purchase intention is classified into general purchase intention, urgent need of purchase intention, and certainty of purchase intention, and the weight for each subitem is a structural equation By estimating and reflecting the model, the online game item purchase index can be effectively analyzed.

In addition, according to any one of the problem solving means of the present invention described above, while introducing the consumption value theory to grasp the game item value, by modifying and applying to the game item which is a virtual product, a more accurate measurement method is presented.

In addition, according to any one of the above-described problem solving means of the present invention, it is possible to purchase online game items with a high cost-effectiveness and satisfaction in terms of game users, and grasp the propensity of game users from the service company side to the game users Creating and selling game items has the advantage of increasing online game item sales.

1 is a view showing the concept of an online game item purchase index analysis model according to an embodiment of the present invention.
2 is a diagram illustrating the concept of a game item value factor according to an embodiment of the present invention.
3 is a diagram illustrating a questionnaire for an online game according to an embodiment of the present invention.
4 is a block diagram illustrating a system for analyzing an online game item purchase index according to an embodiment of the present invention.
5 is a flowchart illustrating a method of analyzing an online game item purchase index according to an embodiment of the present invention.
6 is a flowchart illustrating an online game item purchase index analysis step according to an embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and like reference numerals designate like parts throughout the specification.

Throughout the specification, when a part is referred to as being "connected" to another part, it includes not only "directly connected" but also "electrically connected" with another part in between . Also, when an element is referred to as "comprising ", it means that it can include other elements as well, without departing from the other elements unless specifically stated otherwise.

1 is a view showing the concept of an online game item purchase index analysis model according to an embodiment of the present invention.

Referring to FIG. 1, the items related to the online game item purchase index are classified into item purchase intention (100), game item value (200), character homogeneity (300), and game satisfaction (400). The structural equation model is shown as the correlation of the details constituting the factor.

In the present invention, the online game item purchase index is statistically estimated using a structural equation model. In particular, the detailed items of item purchase intention 100 are divided into general purchase intention 110, urgency of purchase intention 120, and certainty of purchase intention 130, and the weights calculated using the structural equation model are applied. Calculate the online game item purchase index.

Game item value 200 relates to the extent to which game items are valued. A theory of consumption value was applied to grasp the game item value 200, and the detailed items of the game item value 200 were modified to fit the game item, thereby providing a more accurate measurement method. This will be described in detail with reference to FIG. 2.

2 is a diagram illustrating the concept of a game item value factor according to an embodiment of the present invention.

Values that the user feels for the game item include character capability enhancement value 210, enjoyment value 220, visual authority value 230, and return on investment value 240.

The character capability increase value 210 relates to the degree to which the character's ability increases according to the purchase of the game item, and is related to the level increase 211, the experience value improvement 212, the ability display 213, and the like. The enjoyment value 220 relates to the degree to which the enjoyment of the game is improved according to the purchase of the game item, and is related to the fun 221, the enjoyment 222, the excitement 223, and the like. The visual authority value 230 relates to the visual effect that a game user feels as the game item is purchased, and is related to the inferiority 231, the uniqueness 232, the envy 233, the superiority 234, and the like. The return on investment value 240 relates to the buyer's benefit of the game item purchase price, and is related to the value over cost 241, price validity 242, price adequacy 243, and the like.

The game item value 200 factor according to an embodiment of the present invention is a detailed item for the game item value 200 factor, the character capability increase value 210, the enjoyment value 220, the visual authority value 230, and the investment. The contrast effect value (240) is divided and a two-stage model is presented to derive each subitem from the questionnaire. In addition, this paper proposes a new measurement method by applying the theory of consumption value applied to products or services to online game items, which are virtual products.

Referring again to FIG. 1, character homogeneity 300 relates to the extent to which a game character is identified with the game user himself. That is, the degree to which a game user regards a game character as his alter ego.

Game satisfaction 400 relates to the degree of satisfaction with the game itself. Game satisfaction 400 is related to overall satisfaction with a game, relative satisfaction with other games, interest inducing elements, and the like.

The item purchase intention 100 relates to a user's intention to purchase an item. The item purchase intention 100 is related to the general purchase intention 110, the urgency of the purchase intention 120, the certainty of the purchase intention 130, and the like. The general purchase intention 110 relates to the general degree of intention to purchase an item, and the urgency of the purchase intention 120 indicates the degree of purchase intention of the item in consideration of the purchase time. The certainty of purchase intention 130 relates to the purchase intention of the item in consideration of the purchase possibility.

The online game item purchase index is calculated by reflecting weights on the general purchase intention 110, the urgentness of the purchase intention 120, and the certainty of the purchase intention 130, which are detailed items of the item purchase intention 100. The weights reflected for each subitem are calculated by the structural equation model.

A structural equation model in the online game item purchase index analysis model and an online game item purchase index calculation method using the same will be described with reference to FIGS. 1 and 2.

Items shown as dotted rectangles in FIG. 2, for example, level elevation 211, experience improvement 212, ability exertion 213, fun 221, etc., refer to observation variables in the structural equation model. Observation variables are variables that are directly measured through questionnaires and are used to measure latent variables in connection with latent variables. In this case, the latent variable refers to a variable in which the concept of construction is not directly observed or measured, and is indirectly measured by the observation variable. Items shown as solid rectangles or solid circles in FIGS. 1 and 2, such as item purchase intent 100, game item value 200, character homogeneity 300, game satisfaction 400, and general purchase intention 110 And the like represent latent variables in the structural equation model.

Meanwhile, in the structural equation, the arrow connection between the observation variable and the latent variable or the arrow connection between the latent variables means a path coefficient between each variable.

Accordingly, each of the observation variables shown as rectangles in FIGS. 1 and 2 is measured from the questionnaire results classified as the corresponding variables, and the corresponding observation variables and arrows connected to each observation variable reflect the predetermined path coefficients. The value of the latent variable is calculated. This will be described in detail with reference to the questionnaire data shown in FIG. 3.

3 is a diagram illustrating a questionnaire for an online game according to an embodiment of the present invention.

The latent variable character capability increase value 210 is measured from the observation variable level increase 211, experience improvement 212, and ability exertion 213. The questionnaire data regarding the level increase 211 is 'If you buy an item and play the game, the speed of level increase of the game becomes faster'. The experience data of the game can be received more`, the questionnaire data on the ability display (213) may include, 'If you buy the item and play the game, you can show the ability that has not been possible so far'. The response data regarding the questionnaire data for each observation variable may use a numerical scale such as a seven-point scale. When the survey result of each observation variable is calculated, the value of the character capability improvement value 210 is calculated by reflecting a path coefficient in each observation variable value.

In the present embodiment, the response data is collected using the 7-point scale, but the present invention is not limited thereto. In addition, various measures may be used.

Using the same method, values can also be calculated for the enjoyment value 220, the visual authority value 230, and the return on investment value 240. In addition, the game item value 200 may be calculated by applying a path coefficient to the character variable capability improvement value 210, the enjoyment value 220, the visual authority value 230, and the ROI value 240, which are potential variables. have.

In addition, using the questionnaire data shown in FIG. 3, the item purchase intention 100 also calculates the general purchase intention 110, the urgent need for purchase intention 120, and the certainty of the purchase intention 130 as potential variables. The value for the item purchase intention 100 may be calculated from the general purchase intention 110, the urgency of the purchase intention 120, and the certainty of the purchase intention 130. Character homogeneity 300 and game satisfaction 400 can also be calculated using the same method. Meanwhile, the path coefficient values considered between the variables in the structural equation model may be determined by various methods such as user setting and automatic setting by the system.

As illustrated in FIG. 1, the online game item purchase index may be calculated as a weighted average of the general purchase intention 110, the urgency of purchase intention 120, and the certainty of purchase intention 130. That is, when the general purchase intention is X1, the urgent need of purchase intention is X2, and the certainty of purchase intention is X3, the online game item purchase index (OPI) according to the present invention is expressed by Equation 1 below. Is calculated.

[ Equation  One]

OPI = X1 × W1 + X2 × W2 + X3 × W3

Here, W1, W2, and W3 mean weights for X1, X2, and X3, respectively, and the sum of the weights may be 1. In this case, each weight may be estimated through the structural equation model.

Using the calculated online game item purchase index, game users can purchase online game items with high cost-effectiveness and satisfaction, and game service companies identify game users' tendency to make and sell game items desired by game users online. This has the advantage of increasing game item sales.

4 is a block diagram illustrating a system for analyzing an online game item purchase index according to an embodiment of the present invention.

Online game item purchase index analysis system 500 according to an embodiment of the present invention generates a database 600 based on the response to the questionnaire survey of the questionnaire, the online game item purchase index of the generated database 600 After extracting the questionnaire items that affect, the online game item purchase index is analyzed using the extracted questionnaire and structural equation model.

In particular, the online game item purchase index analysis system 500 according to an embodiment of the present invention calculates the online game item purchase index using detailed items related to the game item purchase intention 100 and weights thereof, and the weight is Calculate using a structural equation model.

As shown in FIG. 4, the online game item purchase index analysis system 500 includes a database 600, an extraction unit 700, and an analysis unit 800.

The database 600 stores a survey result for calculating an online game item purchase index. After providing questionnaire data to the questionnaire, response data to the questionnaire data is received. The survey data may include not only about the online game item, but also about the overall online game. Provision of survey data and reception of response data may be performed using a network server. The received response data is stored in the database 600.

The extraction unit 700 extracts a questionnaire item result related to each factor of the online game item purchase index from the collected database 600. In detail, the extractor 700 includes a game item value extractor 710, a character homogeneity extractor 720, a game satisfaction extractor 730, and an item purchase intention extractor 740. The survey item result is extracted from the database 600. The survey item result includes the questionnaire item and the questionnaire result data.

The analysis unit 800 analyzes the online game item purchase index using the extracted questionnaire result and structural equation model. In detail, the analysis unit 800 is applied to the questionnaire item related to the item purchase intention 100 from the structural equation model, the model application unit 810 applying the structural equation model to the questionnaire extracted from the extraction unit 700. A weight calculator 820 for calculating a weight to be calculated and a purchase index calculator 830 for calculating an online game item purchase index using the calculated weight and question items related to the item purchase intention 100 are included.

The model applier 810 applies the values extracted by the game item value extractor 710, the character homogeneity extractor 720, and the game satisfaction extractor 730 to the structural equation models illustrated in FIGS. 1 and 2. . In this case, the model applying unit 810 may further include means for reflecting a path coefficient between the observation variable and the latent variable or between the latent variables.

In the weight calculation unit 820, the detailed items constituting the item purchase intention 100 from the structural equation model applied in the model applying unit 810, that is, the general purchase intention 110, the urgent need for the purchase intention 120, and the purchase intention The weight to be reflected in the certainty 130 of the intention is calculated.

The purchase index calculator 830 calculates the weight calculated by the weight calculator 820 for each detailed item related to the item purchase intention 100 extracted by the item purchase intention extractor 740 to finalize the online item purchase index. Calculate as

Since the latent variables and observation variables for applying to the structural equation model, the structural equation model have been described in detail with reference to FIGS. 1 to 3, the descriptions thereof will be omitted within the overlapping range.

5 is a flowchart illustrating a method of analyzing an online game item purchase index according to an embodiment of the present invention.

First, a database according to a survey result is generated (S100).

After providing questionnaire data to the questionnaire, response data to the questionnaire data is received. The survey data may relate not only to online game items, but also to online games as a whole. Provision of survey data and reception of response data may be performed using a network server. The received response data is stored in the database.

Next, a questionnaire item result related to game item value, character homogeneity, game satisfaction, and item purchase intention is extracted from the generated database (S200).

Since the game item value, character homogeneity, game satisfaction, and item purchase intention are potential variables for calculating the online game item purchase index, the survey item results of the related observation variables are extracted. The survey item result may include a questionnaire item and a questionnaire item result data.

Next, the online game item purchase index is analyzed using the extracted questionnaire result and structural equation model (S300).

Step (S300) for analyzing the online game item purchase index will be described in detail with reference to FIG.

6 is a flowchart illustrating an online game item purchase index analysis step according to an embodiment of the present invention.

First, a structural equation model is applied (S310).

The game item value, character homogeneity, and game satisfaction factor extracted in the questionnaire item result extraction step S200 are applied to the structural equation models shown in FIGS. 1 and 2. In this case, the method may further include a step for reflecting a path coefficient between the observed variable and the latent variable or the latent variables applied to the model.

Next, the weight to be reflected in the result of the questionnaire item for the item purchase intention is calculated (S320).

From the structural equation model, the detailed items constituting the item purchase intention, that is, the weight to be reflected in the general purchase intention, the urgency of the purchase intention, and the certainty of the purchase intention are calculated.

Next, the online game item purchase index is calculated (S330).

An online item purchase index is finally calculated by calculating the weight calculated in the step S320 of calculating each weighted item related to the item purchase intention extracted in the survey item result extracting step S200.

Since the latent variables and observation variables for applying to the structural equation model, the structural equation model have been described in detail with reference to FIGS. 1 to 4, descriptions thereof are omitted within the overlapping range.

For reference, the components illustrated in FIG. 4 according to an embodiment of the present invention mean software components or hardware components such as a field programmable gate array (FPGA) or an application specific integrated circuit (ASIC), and perform predetermined roles. .

However, 'components' are not meant to be limited to software or hardware, and each component may be configured to be in an addressable storage medium or may be configured to reproduce one or more processors.

Thus, as an example, a component may include components such as software components, object-oriented software components, class components, and task components, and processes, functions, properties, procedures, and subs. Routines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables.

Components and the functionality provided within those components may be combined into a smaller number of components or further separated into additional components.

The system and method for analyzing an online game item purchase index according to an embodiment of the present invention may also be implemented in the form of a recording medium including computer executable instructions, such as a program module executed by a computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, the computer-readable medium may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically includes any information delivery media, including computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transport mechanism.

The foregoing description of the present invention is intended for illustration, and it will be understood by those skilled in the art that the present invention may be easily modified in other specific forms without changing the technical spirit or essential features of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

100: willingness to buy item 110: general willingness to buy
120: Urgent Intention to Purchase 130: Certainty of Purchase Intention
200: game item value 210: character ability increase value
220: pleasure value 230: visual authority value
240: return on investment value 300: character homogeneity
400: Game Satisfaction 500: Online Game Item Purchase Index Analysis System
600: database 700: extracting unit
710: game item value extraction unit 720: character homogeneity extraction unit
730: game satisfaction extraction unit 740: item purchase intention extraction unit
800: analysis unit 810: model application unit
820: weight calculation unit 830: purchase index calculation unit

Claims (15)

In the system for analyzing the online game item purchase index,
A database that stores the results of surveys for online games,
An extraction unit for extracting a questionnaire item result about game item value, character homogeneity, game satisfaction, and item purchase intention from the database;
Including an analysis unit for analyzing the online game item purchase index using the extracted questionnaire results and structural equation model,
The survey item result includes a questionnaire item and a questionnaire item result data,
The analysis unit,
A model application unit for applying the result of the questionnaire on the game item value, character homogeneity, and game satisfaction to a structural equation model including an observation variable according to the survey result and a latent variable according to a path coefficient between the preset observation variables;
A weight calculator configured to calculate a weight to be reflected in the questionnaire for the item purchase intention from the structural equation model;
A purchase index calculator configured to calculate the online game item purchase index using the questionnaire item result regarding the item purchase intention and the calculated weight;
The purchase index calculator is a product of a general purchase intention (X1) and a weight (W1) of the general purchase intention (X1), the urgent need of purchase intention (X2) and the weight of the purchase intention (X2) (W2) Calculate the online game item purchase index (OPI) by summing the product of the product of and the certainty of purchase intention (X3) and the weight (W3) of the certainty of purchase intention (X3),
The questionnaire item regarding the purchase intention of the item includes at least one of a general purchase intention, an urgent need of a purchase intention, and a certainty of a purchase intention.
delete delete The method of claim 1,
The questionnaire item for the value of the game item comprises at least one of the character capability improvement value, the enjoyment value, the visual authority value and the return on investment value.
delete The method of claim 4, wherein
The character ability improvement value includes information about at least one of level increase, experience value improvement, and ability exertion in the database.
The method of claim 4, wherein
Wherein said enjoyment value comprises information about at least one of fun, enjoyment, and excitement in said database.
The method of claim 4, wherein
The visual authority value comprises information about at least one of inferiority, uniqueness, envy and superiority in the database.
The method of claim 4, wherein
The return on investment value analysis system includes information on at least one of value above cost, price validity and price suitability in the database.
In the method of analyzing the online game item purchase index,
Creating a database according to the survey result of the online game,
Extracting a questionnaire item result about game item value, character homogeneity, game satisfaction, and item purchase intention from the generated database; and
Analyzing the online game item purchase index using the extracted questionnaire item results and structural equation model,
The survey item result includes a questionnaire item and a questionnaire item result data,
Analyzing the online game item purchase index using the extracted questionnaire results and structural equation model,
Applying a questionnaire item result for game item value, character homogeneity and game satisfaction to a structural equation model consisting of an observation variable according to the survey result and a latent variable according to a path coefficient between the preset observation variables;
Calculating a weight to be reflected in the questionnaire for the item purchase intention from the structural equation model; and
Calculating the online game item purchase index using the questionnaire item result regarding the item purchase intention and the calculated weight value;
The calculating of the online game item purchase index using the questionnaire item result of the item purchase intention and the calculated weight may be a product of a general purchase intention (X1) and a weight (W1) for the general purchase intention (X1). A product of the weight of the purchase intention (X2) and the weight of the purchase intention (X3) and the weight of the purchase intention (X3) and the intention of the purchase intention (X3) Summing up to calculate the online game item purchase index (OPI),
The questionnaire item regarding the purchase intention of the item includes at least one of a general purchase intention, an urgent need of a purchase intention, and a certainty of a purchase intention.
delete delete 11. The method of claim 10,
The questionnaire item for the value of the game item comprises at least one of the character capability improvement value, the enjoyment value, the visual authority value and the return on investment value.
delete A recording medium on which a program for performing the method according to claim 10 or 13 is recorded.

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