# Research on Product Innovation Intention Analysis System Model Based on Computational Thinking

Authors: Shuo Zhang; Hua Zou; Zhicheng Qiao; Jian Sun; Yan Xu
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The starting point of this study is to improve the realistic problem of high failure rate of product innovation and improve the success rate of product innovation. First, starting from the relationship between user demand and innovation, this study constructs a product innovation-demand screening system model from a macro perspective and describes the circular promotion relationship between demand and innovation. Second, apply computational thinking to further characterize the system model. From the point of view of mining frequent item sets, this paper constructs an analysis model of product innovation intention from a microscopic perspective and makes a correlation analysis of innovation and demand, providing technical support for the combination and screening of innovation and demand. The model runs in a Python environment. This study uses the Apriori algorithm to improve the efficiency of frequent set mining. This model has no specific restrictions on the selected users, the way to select demand data is broad, and the data are easy to obtain. The use of large amounts of data for analysis also reduces the bias of experts and leading users in evaluating and screening innovation intentions. The simulation of the operation and screening efficiency of the product innovation intention analysis model shows that (1) the model runs effectively. (2) The number of innovation intentions screened by the model can be regulated by adjusting only two variables: min_support and min_confidence. However, the number of strong association rules is more sensitive to the adjustment of min_support. (3) The innovation intention analysis model has significantly improved the efficiency of innovation intention screening. (4) Adjusting the innovation portfolio according to the analysis results of innovation intention can make new products more popular.


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## Body

## 1. Introduction

The theme of China’s economic and social development during the 14th Five-year Plan period is to promote high-quality development and innovative development is the driving force for high-quality economic development [1]. The essential demand and ultimate goal of enterprise innovation is to obtain market and pursue profit [2, 3]. Product innovation is the final carrier of enterprise innovation. Therefore, product innovation is a strategy for enterprises to deal with market competition [4]. By analyzing the relationship between demand and supply, Aoki and Yoshikawa [5] and others concluded that product innovation can create new demand and maintain or promote economic growth [6]. Greenwood and Uysal [7], Garcia-Torres [8], and Giuliani et al. [9] also carried out research on product innovation and economic growth from different perspectives and applied different research methods, and the research results also confirmed the promotion of product innovation to economic growth from different aspects.Studies have found that products that are brand new to the market and companies account for only 10% of existing new products. Even SONY, which is famous for its innovation, has achieved 80% of its new products by revising and improving existing products. As product innovation will face many difficulties, its inherent difficulties mainly lie in the uncertainty of technology and the market. Owing to the uncertainty of technology and market, R&D risks are high, and many R&D projects fail [10]. In reality, many empirical studies show that the failure rate of new products is very high [11]. Research by the product development management association (PDMA) shows that the success rate of new products entering the market is only 59% [12], and this research does not take into account the majority of projects terminated during the development process. There is a worse research result: among the 11 new product ideas, three will enter the development stage, 1.3 will enter the market, and finally, only one will achieve commercial success in the market [13]. This situation has not improved in recent years. In Cooper’s 2017 book, it is mentioned that only 13% of companies believe that their total new product results meet their annual profit targets. After development and testing efforts are completed, about 40% of new products still fail at launch; statistically, only one out of every 7 to 10 new product concepts is a commercial success [14]. Therefore, how to effectively control R&D risks and improve the success rate of product innovation is an urgent problem to be solved [15].


### 1.1. Research Review on Improving the Success Rate of Product Innovation

#### 1.1.1. Success Factors of Product Innovation and Influencing Factors of Product Innovation Performance

In order to solve the problem of low success rate of new product launch, many scholars have carried out research on new product success factors and product innovation performance. Specific research results are shown in Table1. Research on new product success factors/influencing factors of product innovation performance.Table 1
Research on new product success factors/influencing factors of product innovation performance.
Influence factor/key success factorStudyMarket positioning and creativityIm and Workman [16]Compare innovation with imitation strategyZhou [17]Emotion and learning abilityAkgün et al. [18]Extent of global engagement, information spillovers, and market structureLederman [19]Market oriented and entrepreneurial orientedHong et al. [20]Acquisition ability, digestion ability, transformation ability, and development abilityZhou and Li [21]Tsai [22]Service business modelsIvanka et al. [23]Customer collaborationYao et al. [24]Checks and balances effect of opportunistic behavior between integrators and key suppliersLi et al. [25]Structural holesAhuja [26],Lechner et al. [27],Ying [28]Development process factors (proficiency of marketing activities and protocols) and strategic factors (product advantage and product market strategy)Montoya-Weiss and Calantone [29]Product characteristics, firm strategy, firm process, and marketplace characteristicsHenard and Szymanski [30]Culture, market orientation, and dedicated human resourcesEvanschitzky et al. [31]It can be seen that there are many factors that affect the performance of product innovation and determine the success of product innovation. The factors change over time. Some are getting stronger and some are getting weaker [31]. In the actual product innovation process to improve the innovation performance or the success rate of new product launch to comprehensively consider all the influencing factors is not realistic. Therefore, some scholars have carried out research on product innovation prediction and product innovation decision-making on this basis.


#### 1.1.2. Product Innovation Evaluation and Decision-Making Research

The relevant studies on product innovation evaluation and decision-making are summarized in Table2. At present, there are two main research methods for product innovation prediction and decision-making. The first method is to invite experts in related fields to make judgments and give suggestions. Such as the comparative creativity assessment (CCA), the multi-point creativity assessment (MPCA) [32], the consensual assessment technique (CAT), etc. [33]. Almost all evaluations are based on the standard evaluation of the judges [34]. The second approach is to conduct experiments (and/or) develop models. These two methods will encounter some problems in practical research.Table 2
Product innovation evaluation and decision-making-related research.
Evaluation and decision-making methods for product innovationStudy17 features of the new product screening system, 33 dimensions for evaluating and screening new productsUdell [37]A four C’s framework (chain/constituents/cost-revenue projections/change)Hostager et al.[38]Consensual assessment technique (CAT)Hennessey et al. [33]Comparative creativity assessment (CCA) and multipoint creativity assessment (MPCA)Oman et al. [32]Multicriteria fuzzy decision-making methods based on intuitionistic fuzzy setsLin et al. [35]Fuzzy-multiobjective genetic algorithm modelPonticelli et al. [36]Fuzzy decision-making method of analogical detection is proposed by using fuzzy analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS)Pandey et al. [40]Idea marketsSoukhoroukova et al. [39]Through contextualisation, multicriteria decision analysis (MCDA)Gabriel et al. [34]Extraction mechanism of product creative information based on basic trust assignment functionShan et al. [41]Dynamic fuzzy partial order multiattribute group decision-making in single-peak formZuo et al. [42]Lead user identificationFeng et al. [43]First of all, when relying on experts to forecast and make decisions on product innovation, they will face problems such as incomplete collection of decision information and fuzzy objectives and conditions. In reality, experts are often unable to fully express their meaning in the description and judgment of problems. For example, experts say: young, attractive, beautiful, and so on. How to define such adjectives? For example, experts mention: overwhelming, widespread, insignificant, and so on. How are these representations quantified? These are the problems existing in real research. Another practical problem is that when conducting experiments (and/or) developing models, it is inevitable that some random, uncertain changes will occur during the experiments, and systematic errors are inevitable. In order to solve the above problems, some scholars have carried out research on fuzzy decision-making. Lin et al. [35] proposed a new method to deal with multi-criteria fuzzy decision problems based on intuitionistic fuzzy sets. In order to reduce the influence of fuzzy concepts on decision-making. Ponticelli et al. [36] established a fuzzy multi-objective combined genetic algorithm model to solve the evaluation of alternative processes and the selection of alternative solutions in the manufacturing process.Second, the limitation of product innovation evaluation and screening solely relying on experts to predict new products is also shown in the following aspects: (1) The number of experts participating in evaluation is limited. (2) The limitations of what experts are good at. (3) Scoring is highly subjective, and experts’ opinions often differ. Many scholars are aware of these problems and design research programs from different research perspectives in order to reduce the limitation of making decisions only through expert evaluation. Udell and Baker [37] found in his research that the interviewed managers had different perspectives on new products due to their different focus areas. Therefore, in order to evaluate new product ideas as accurately as possible, 17 features of new product screening system and 33 dimensions of evaluation and screening of new products are proposed. And around these 33 dimensions for the screening of new product ideas. Hostager et al. [38] analyzed the value chain of new products and evaluated new products according to the profitability of members on the value chain. Soukhoroukova et al. [39] proposed the method of finding, screening, and evaluating new product concepts and technologies through the idea market. In the process of actual operation, the researcher used virtual stock to represent the idea of new product, tested the operation of market creation in the enterprise, and completed the search, screening, and evaluation of new product ideas through employees. Expanding the range of people involved in evaluating and providing product innovation ideas. Pandey et al. [40] comprehensively applied fuzzy analytic hierarchy process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) proposed a fuzzy decision-making method for analogical detection of new product prediction. Choose the best analog product from existing analog products to predict the diffusion of new products and then predict new products. Gabriel et al. [34] proposed to evaluate and select innovative ideas by selecting the best compromise between different dimensions of the problem through the contextized method and using multi-criteria decision analysis (MCDA) tool. In line with the idea of “xperts before indicators,” Shan and Du [41] realized the dynamic ordering of product ideas with the help of Internet technology. This decision not only involves the participation of experts but also introduces the majority of users to participate in the evaluation. At the same time, participants are allowed to infer only some of the indicators that they are familiar with or belong to their own professional fields, thus improving the effectiveness of decision-making information. Zuo et al. [42] considered consumer preference, introduced the single-peak preference theory in the study of multi-attribute group decision-making, and proposed the research method of dynamic fuzzy partial order multiattribute group decision-making in the form of single-peak. But the information preferences of different decision-makers are given the same weight in the study. This will skew the decision.From the research trend of product innovation prediction and product creativity evaluation and screening, more and more scholars attach importance to the discussion of new product evaluation and product innovation and creativity screening from the perspective of user preference. It is not uncommon to study product innovation performance from the perspective of customer collaborative innovation and “leading user” participation in innovation. The research on customer collaborative innovation focuses more on the mutual learning between users and R&D personnel, and the complementary advantages in knowledge composition and skill application. Alternatively, it studies the relationship among product innovation performance, customer collaborative product innovation and transformational learning [24]. The research on leading users focuses more on the identification of leading users and the corresponding innovation path [43]. Regardless of the accuracy of lead user identification, is there a good way to filter, integrate and utilize these needs and ideas efficiently in the face of a wider user group, the huge demands and possible ideas generated by these users? This is a problem that needs to be solved.With the development of science and technology, new technologies and new ideas emerge one after another. If all innovative combinations are listed by exhaustive method, it is very inefficient and impossible to develop new products. In addition, users have different demand combinations in many aspects such as product function, appearance, comfort, and service. It is inefficient to complete all the matching between supply and demand by exhaustive method, and it cannot provide guidance for innovating products that are really popular in the market. This is another problem that needs to be solved urgently.Therefore, the purpose of this study is to construct a model that can efficiently match innovations with demands, analyze innovation intentions, and then screen out the innovation intentions that are more in line with users’ needs, so as to guide product innovation and achieve the purpose of improving product innovation performance .Figure 1
Research process snapshot.


### 1.2. The Structure and the Main Work of This Study

①
This paper introduces the practical dilemma of product innovation and reviews the research on improving the efficiency of product innovation, predicting product innovation and screening product innovation ideas.②
This paper analyzes the relationship between user demand and product innovation and constructs a product innovation-demand screening system model.③
The innovation intention analysis system model was constructed by computational thinking and coded by Python. The starting point of the construction is user demand, the foothold is high-quality and high-quantity product innovation, and the entry point is the association analysis of innovation demand.④
The innovation intention analysis model code is run in Python environment to simulate the innovation intention analysis process and to prove the validity of the model. Through the simulation of the influence of min_support and min_confidence on the screening results and the simulation of the improvement of the screening efficiency of the innovation intention analysis model, the efficiency of the model for screening innovation ideas is proved. The research process is shown in Figure1.


### 1.3. Innovations of This Study

①
In this study, computational thinking was used for system analysis and model design, association algorithm was used to solve the model, and the application of information means greatly improved the operation efficiency of the established innovation intention analysis system model.②
The screening of product innovation ideas in this study is demand-oriented. Different from the research on leading users, the users mentioned in this research refer to all users that can be contacted and investigated. There is no need to identify and filter users. At the same time, this study is different from customer collaborative product innovation. This study does not require users to participate in product innovation subjectively. Users’ purchase data and usage experience can be used as auxiliary data for screening. At the same time, the target users are not limited to users of a specific product. Therefore, more user demands can be collected and more creative data can be provided. The sample of user demand is large enough to reflect market demand more objectively and reduce deviation. The huge number of “intelligence providers” achieves the “collective wisdom” in the real sense.③
The model constructed in this research is similar to the widely used Triz theory in improving innovation performance. Triz is a technique of solving contradictions in a pattern, which is generally regarded as an effective theory to solve the problems of invention and creation. It subdivides and recombines the invention process and scientific principles and provides engineers with solutions to problems and innovative ideas. The model constructed by this research also draws lessons from Triz’s idea of matching and restructuring and strives to efficiently complete the analysis and screening of innovation intentions, reduce the trial-and-error process in the process of innovation, thereby reducing unnecessary investment and avoiding the waste of resources.The model constructed in this study is different from Triz theory in solving the specific focus. Although Triz can improve the efficiency of innovation and even the efficiency of product innovation, it is more about quantity. In this study, the innovation-demand screening model should not only solve the problem of product innovation quantity but also better solve the problem of quality. That is to better combine and screen the innovations and demands. So that the products innovated according to the screened ideas can really be accepted by the market, and then really produce benefits, and truly achieve the purpose of improving innovation performance.④
The starting point of this study is different from previous studies. The screening of innovation intentions mentioned in previous studies is that there are many innovation intentions, among which the best one is selected. From the perspective of demand, this study matches demand and innovation efficiently and directly obtains the screened innovation intention by solving it through correlation algorithm. That is, the creation of ideas is completed through a model, and the screening of the generated ideas is completed at the same time, which improves the screening efficiency.At the same time, matching demand and innovation through the model can not only provide the best combination but also discover a better breakthrough point of innovation through demand. The user needs that this research focuses on are not limited to the needs of users or potential users of the products under study. This study pays more attention to the possibility of innovation caused by the demand migration of other product users. Dealing with users’ demand information in this way can not only avoid the deviation in evaluation due to the knowledge limitation of experts but also avoid paying too much attention to the needs of lead user or a certain user group and creating information cocoons for innovation.


## 1.1. Research Review on Improving the Success Rate of Product Innovation

### 1.1.1. Success Factors of Product Innovation and Influencing Factors of Product Innovation Performance

In order to solve the problem of low success rate of new product launch, many scholars have carried out research on new product success factors and product innovation performance. Specific research results are shown in Table1. Research on new product success factors/influencing factors of product innovation performance.Table 1
Research on new product success factors/influencing factors of product innovation performance.
Influence factor/key success factorStudyMarket positioning and creativityIm and Workman [16]Compare innovation with imitation strategyZhou [17]Emotion and learning abilityAkgün et al. [18]Extent of global engagement, information spillovers, and market structureLederman [19]Market oriented and entrepreneurial orientedHong et al. [20]Acquisition ability, digestion ability, transformation ability, and development abilityZhou and Li [21]Tsai [22]Service business modelsIvanka et al. [23]Customer collaborationYao et al. [24]Checks and balances effect of opportunistic behavior between integrators and key suppliersLi et al. [25]Structural holesAhuja [26],Lechner et al. [27],Ying [28]Development process factors (proficiency of marketing activities and protocols) and strategic factors (product advantage and product market strategy)Montoya-Weiss and Calantone [29]Product characteristics, firm strategy, firm process, and marketplace characteristicsHenard and Szymanski [30]Culture, market orientation, and dedicated human resourcesEvanschitzky et al. [31]It can be seen that there are many factors that affect the performance of product innovation and determine the success of product innovation. The factors change over time. Some are getting stronger and some are getting weaker [31]. In the actual product innovation process to improve the innovation performance or the success rate of new product launch to comprehensively consider all the influencing factors is not realistic. Therefore, some scholars have carried out research on product innovation prediction and product innovation decision-making on this basis.


### 1.1.2. Product Innovation Evaluation and Decision-Making Research

The relevant studies on product innovation evaluation and decision-making are summarized in Table2. At present, there are two main research methods for product innovation prediction and decision-making. The first method is to invite experts in related fields to make judgments and give suggestions. Such as the comparative creativity assessment (CCA), the multi-point creativity assessment (MPCA) [32], the consensual assessment technique (CAT), etc. [33]. Almost all evaluations are based on the standard evaluation of the judges [34]. The second approach is to conduct experiments (and/or) develop models. These two methods will encounter some problems in practical research.Table 2
Product innovation evaluation and decision-making-related research.
Evaluation and decision-making methods for product innovationStudy17 features of the new product screening system, 33 dimensions for evaluating and screening new productsUdell [37]A four C’s framework (chain/constituents/cost-revenue projections/change)Hostager et al.[38]Consensual assessment technique (CAT)Hennessey et al. [33]Comparative creativity assessment (CCA) and multipoint creativity assessment (MPCA)Oman et al. [32]Multicriteria fuzzy decision-making methods based on intuitionistic fuzzy setsLin et al. [35]Fuzzy-multiobjective genetic algorithm modelPonticelli et al. [36]Fuzzy decision-making method of analogical detection is proposed by using fuzzy analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS)Pandey et al. [40]Idea marketsSoukhoroukova et al. [39]Through contextualisation, multicriteria decision analysis (MCDA)Gabriel et al. [34]Extraction mechanism of product creative information based on basic trust assignment functionShan et al. [41]Dynamic fuzzy partial order multiattribute group decision-making in single-peak formZuo et al. [42]Lead user identificationFeng et al. [43]First of all, when relying on experts to forecast and make decisions on product innovation, they will face problems such as incomplete collection of decision information and fuzzy objectives and conditions. In reality, experts are often unable to fully express their meaning in the description and judgment of problems. For example, experts say: young, attractive, beautiful, and so on. How to define such adjectives? For example, experts mention: overwhelming, widespread, insignificant, and so on. How are these representations quantified? These are the problems existing in real research. Another practical problem is that when conducting experiments (and/or) developing models, it is inevitable that some random, uncertain changes will occur during the experiments, and systematic errors are inevitable. In order to solve the above problems, some scholars have carried out research on fuzzy decision-making. Lin et al. [35] proposed a new method to deal with multi-criteria fuzzy decision problems based on intuitionistic fuzzy sets. In order to reduce the influence of fuzzy concepts on decision-making. Ponticelli et al. [36] established a fuzzy multi-objective combined genetic algorithm model to solve the evaluation of alternative processes and the selection of alternative solutions in the manufacturing process.Second, the limitation of product innovation evaluation and screening solely relying on experts to predict new products is also shown in the following aspects: (1) The number of experts participating in evaluation is limited. (2) The limitations of what experts are good at. (3) Scoring is highly subjective, and experts’ opinions often differ. Many scholars are aware of these problems and design research programs from different research perspectives in order to reduce the limitation of making decisions only through expert evaluation. Udell and Baker [37] found in his research that the interviewed managers had different perspectives on new products due to their different focus areas. Therefore, in order to evaluate new product ideas as accurately as possible, 17 features of new product screening system and 33 dimensions of evaluation and screening of new products are proposed. And around these 33 dimensions for the screening of new product ideas. Hostager et al. [38] analyzed the value chain of new products and evaluated new products according to the profitability of members on the value chain. Soukhoroukova et al. [39] proposed the method of finding, screening, and evaluating new product concepts and technologies through the idea market. In the process of actual operation, the researcher used virtual stock to represent the idea of new product, tested the operation of market creation in the enterprise, and completed the search, screening, and evaluation of new product ideas through employees. Expanding the range of people involved in evaluating and providing product innovation ideas. Pandey et al. [40] comprehensively applied fuzzy analytic hierarchy process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) proposed a fuzzy decision-making method for analogical detection of new product prediction. Choose the best analog product from existing analog products to predict the diffusion of new products and then predict new products. Gabriel et al. [34] proposed to evaluate and select innovative ideas by selecting the best compromise between different dimensions of the problem through the contextized method and using multi-criteria decision analysis (MCDA) tool. In line with the idea of “xperts before indicators,” Shan and Du [41] realized the dynamic ordering of product ideas with the help of Internet technology. This decision not only involves the participation of experts but also introduces the majority of users to participate in the evaluation. At the same time, participants are allowed to infer only some of the indicators that they are familiar with or belong to their own professional fields, thus improving the effectiveness of decision-making information. Zuo et al. [42] considered consumer preference, introduced the single-peak preference theory in the study of multi-attribute group decision-making, and proposed the research method of dynamic fuzzy partial order multiattribute group decision-making in the form of single-peak. But the information preferences of different decision-makers are given the same weight in the study. This will skew the decision.From the research trend of product innovation prediction and product creativity evaluation and screening, more and more scholars attach importance to the discussion of new product evaluation and product innovation and creativity screening from the perspective of user preference. It is not uncommon to study product innovation performance from the perspective of customer collaborative innovation and “leading user” participation in innovation. The research on customer collaborative innovation focuses more on the mutual learning between users and R&D personnel, and the complementary advantages in knowledge composition and skill application. Alternatively, it studies the relationship among product innovation performance, customer collaborative product innovation and transformational learning [24]. The research on leading users focuses more on the identification of leading users and the corresponding innovation path [43]. Regardless of the accuracy of lead user identification, is there a good way to filter, integrate and utilize these needs and ideas efficiently in the face of a wider user group, the huge demands and possible ideas generated by these users? This is a problem that needs to be solved.With the development of science and technology, new technologies and new ideas emerge one after another. If all innovative combinations are listed by exhaustive method, it is very inefficient and impossible to develop new products. In addition, users have different demand combinations in many aspects such as product function, appearance, comfort, and service. It is inefficient to complete all the matching between supply and demand by exhaustive method, and it cannot provide guidance for innovating products that are really popular in the market. This is another problem that needs to be solved urgently.Therefore, the purpose of this study is to construct a model that can efficiently match innovations with demands, analyze innovation intentions, and then screen out the innovation intentions that are more in line with users’ needs, so as to guide product innovation and achieve the purpose of improving product innovation performance .Figure 1
Research process snapshot.


## 1.1.1. Success Factors of Product Innovation and Influencing Factors of Product Innovation Performance

In order to solve the problem of low success rate of new product launch, many scholars have carried out research on new product success factors and product innovation performance. Specific research results are shown in Table1. Research on new product success factors/influencing factors of product innovation performance.Table 1
Research on new product success factors/influencing factors of product innovation performance.
Influence factor/key success factorStudyMarket positioning and creativityIm and Workman [16]Compare innovation with imitation strategyZhou [17]Emotion and learning abilityAkgün et al. [18]Extent of global engagement, information spillovers, and market structureLederman [19]Market oriented and entrepreneurial orientedHong et al. [20]Acquisition ability, digestion ability, transformation ability, and development abilityZhou and Li [21]Tsai [22]Service business modelsIvanka et al. [23]Customer collaborationYao et al. [24]Checks and balances effect of opportunistic behavior between integrators and key suppliersLi et al. [25]Structural holesAhuja [26],Lechner et al. [27],Ying [28]Development process factors (proficiency of marketing activities and protocols) and strategic factors (product advantage and product market strategy)Montoya-Weiss and Calantone [29]Product characteristics, firm strategy, firm process, and marketplace characteristicsHenard and Szymanski [30]Culture, market orientation, and dedicated human resourcesEvanschitzky et al. [31]It can be seen that there are many factors that affect the performance of product innovation and determine the success of product innovation. The factors change over time. Some are getting stronger and some are getting weaker [31]. In the actual product innovation process to improve the innovation performance or the success rate of new product launch to comprehensively consider all the influencing factors is not realistic. Therefore, some scholars have carried out research on product innovation prediction and product innovation decision-making on this basis.


## 1.1.2. Product Innovation Evaluation and Decision-Making Research

The relevant studies on product innovation evaluation and decision-making are summarized in Table2. At present, there are two main research methods for product innovation prediction and decision-making. The first method is to invite experts in related fields to make judgments and give suggestions. Such as the comparative creativity assessment (CCA), the multi-point creativity assessment (MPCA) [32], the consensual assessment technique (CAT), etc. [33]. Almost all evaluations are based on the standard evaluation of the judges [34]. The second approach is to conduct experiments (and/or) develop models. These two methods will encounter some problems in practical research.Table 2
Product innovation evaluation and decision-making-related research.
Evaluation and decision-making methods for product innovationStudy17 features of the new product screening system, 33 dimensions for evaluating and screening new productsUdell [37]A four C’s framework (chain/constituents/cost-revenue projections/change)Hostager et al.[38]Consensual assessment technique (CAT)Hennessey et al. [33]Comparative creativity assessment (CCA) and multipoint creativity assessment (MPCA)Oman et al. [32]Multicriteria fuzzy decision-making methods based on intuitionistic fuzzy setsLin et al. [35]Fuzzy-multiobjective genetic algorithm modelPonticelli et al. [36]Fuzzy decision-making method of analogical detection is proposed by using fuzzy analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS)Pandey et al. [40]Idea marketsSoukhoroukova et al. [39]Through contextualisation, multicriteria decision analysis (MCDA)Gabriel et al. [34]Extraction mechanism of product creative information based on basic trust assignment functionShan et al. [41]Dynamic fuzzy partial order multiattribute group decision-making in single-peak formZuo et al. [42]Lead user identificationFeng et al. [43]First of all, when relying on experts to forecast and make decisions on product innovation, they will face problems such as incomplete collection of decision information and fuzzy objectives and conditions. In reality, experts are often unable to fully express their meaning in the description and judgment of problems. For example, experts say: young, attractive, beautiful, and so on. How to define such adjectives? For example, experts mention: overwhelming, widespread, insignificant, and so on. How are these representations quantified? These are the problems existing in real research. Another practical problem is that when conducting experiments (and/or) developing models, it is inevitable that some random, uncertain changes will occur during the experiments, and systematic errors are inevitable. In order to solve the above problems, some scholars have carried out research on fuzzy decision-making. Lin et al. [35] proposed a new method to deal with multi-criteria fuzzy decision problems based on intuitionistic fuzzy sets. In order to reduce the influence of fuzzy concepts on decision-making. Ponticelli et al. [36] established a fuzzy multi-objective combined genetic algorithm model to solve the evaluation of alternative processes and the selection of alternative solutions in the manufacturing process.Second, the limitation of product innovation evaluation and screening solely relying on experts to predict new products is also shown in the following aspects: (1) The number of experts participating in evaluation is limited. (2) The limitations of what experts are good at. (3) Scoring is highly subjective, and experts’ opinions often differ. Many scholars are aware of these problems and design research programs from different research perspectives in order to reduce the limitation of making decisions only through expert evaluation. Udell and Baker [37] found in his research that the interviewed managers had different perspectives on new products due to their different focus areas. Therefore, in order to evaluate new product ideas as accurately as possible, 17 features of new product screening system and 33 dimensions of evaluation and screening of new products are proposed. And around these 33 dimensions for the screening of new product ideas. Hostager et al. [38] analyzed the value chain of new products and evaluated new products according to the profitability of members on the value chain. Soukhoroukova et al. [39] proposed the method of finding, screening, and evaluating new product concepts and technologies through the idea market. In the process of actual operation, the researcher used virtual stock to represent the idea of new product, tested the operation of market creation in the enterprise, and completed the search, screening, and evaluation of new product ideas through employees. Expanding the range of people involved in evaluating and providing product innovation ideas. Pandey et al. [40] comprehensively applied fuzzy analytic hierarchy process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) proposed a fuzzy decision-making method for analogical detection of new product prediction. Choose the best analog product from existing analog products to predict the diffusion of new products and then predict new products. Gabriel et al. [34] proposed to evaluate and select innovative ideas by selecting the best compromise between different dimensions of the problem through the contextized method and using multi-criteria decision analysis (MCDA) tool. In line with the idea of “xperts before indicators,” Shan and Du [41] realized the dynamic ordering of product ideas with the help of Internet technology. This decision not only involves the participation of experts but also introduces the majority of users to participate in the evaluation. At the same time, participants are allowed to infer only some of the indicators that they are familiar with or belong to their own professional fields, thus improving the effectiveness of decision-making information. Zuo et al. [42] considered consumer preference, introduced the single-peak preference theory in the study of multi-attribute group decision-making, and proposed the research method of dynamic fuzzy partial order multiattribute group decision-making in the form of single-peak. But the information preferences of different decision-makers are given the same weight in the study. This will skew the decision.From the research trend of product innovation prediction and product creativity evaluation and screening, more and more scholars attach importance to the discussion of new product evaluation and product innovation and creativity screening from the perspective of user preference. It is not uncommon to study product innovation performance from the perspective of customer collaborative innovation and “leading user” participation in innovation. The research on customer collaborative innovation focuses more on the mutual learning between users and R&D personnel, and the complementary advantages in knowledge composition and skill application. Alternatively, it studies the relationship among product innovation performance, customer collaborative product innovation and transformational learning [24]. The research on leading users focuses more on the identification of leading users and the corresponding innovation path [43]. Regardless of the accuracy of lead user identification, is there a good way to filter, integrate and utilize these needs and ideas efficiently in the face of a wider user group, the huge demands and possible ideas generated by these users? This is a problem that needs to be solved.With the development of science and technology, new technologies and new ideas emerge one after another. If all innovative combinations are listed by exhaustive method, it is very inefficient and impossible to develop new products. In addition, users have different demand combinations in many aspects such as product function, appearance, comfort, and service. It is inefficient to complete all the matching between supply and demand by exhaustive method, and it cannot provide guidance for innovating products that are really popular in the market. This is another problem that needs to be solved urgently.Therefore, the purpose of this study is to construct a model that can efficiently match innovations with demands, analyze innovation intentions, and then screen out the innovation intentions that are more in line with users’ needs, so as to guide product innovation and achieve the purpose of improving product innovation performance .Figure 1
Research process snapshot.


## 1.2. The Structure and the Main Work of This Study

①
This paper introduces the practical dilemma of product innovation and reviews the research on improving the efficiency of product innovation, predicting product innovation and screening product innovation ideas.②
This paper analyzes the relationship between user demand and product innovation and constructs a product innovation-demand screening system model.③
The innovation intention analysis system model was constructed by computational thinking and coded by Python. The starting point of the construction is user demand, the foothold is high-quality and high-quantity product innovation, and the entry point is the association analysis of innovation demand.④
The innovation intention analysis model code is run in Python environment to simulate the innovation intention analysis process and to prove the validity of the model. Through the simulation of the influence of min_support and min_confidence on the screening results and the simulation of the improvement of the screening efficiency of the innovation intention analysis model, the efficiency of the model for screening innovation ideas is proved. The research process is shown in Figure1.


## 1.3. Innovations of This Study

①
In this study, computational thinking was used for system analysis and model design, association algorithm was used to solve the model, and the application of information means greatly improved the operation efficiency of the established innovation intention analysis system model.②
The screening of product innovation ideas in this study is demand-oriented. Different from the research on leading users, the users mentioned in this research refer to all users that can be contacted and investigated. There is no need to identify and filter users. At the same time, this study is different from customer collaborative product innovation. This study does not require users to participate in product innovation subjectively. Users’ purchase data and usage experience can be used as auxiliary data for screening. At the same time, the target users are not limited to users of a specific product. Therefore, more user demands can be collected and more creative data can be provided. The sample of user demand is large enough to reflect market demand more objectively and reduce deviation. The huge number of “intelligence providers” achieves the “collective wisdom” in the real sense.③
The model constructed in this research is similar to the widely used Triz theory in improving innovation performance. Triz is a technique of solving contradictions in a pattern, which is generally regarded as an effective theory to solve the problems of invention and creation. It subdivides and recombines the invention process and scientific principles and provides engineers with solutions to problems and innovative ideas. The model constructed by this research also draws lessons from Triz’s idea of matching and restructuring and strives to efficiently complete the analysis and screening of innovation intentions, reduce the trial-and-error process in the process of innovation, thereby reducing unnecessary investment and avoiding the waste of resources.The model constructed in this study is different from Triz theory in solving the specific focus. Although Triz can improve the efficiency of innovation and even the efficiency of product innovation, it is more about quantity. In this study, the innovation-demand screening model should not only solve the problem of product innovation quantity but also better solve the problem of quality. That is to better combine and screen the innovations and demands. So that the products innovated according to the screened ideas can really be accepted by the market, and then really produce benefits, and truly achieve the purpose of improving innovation performance.④
The starting point of this study is different from previous studies. The screening of innovation intentions mentioned in previous studies is that there are many innovation intentions, among which the best one is selected. From the perspective of demand, this study matches demand and innovation efficiently and directly obtains the screened innovation intention by solving it through correlation algorithm. That is, the creation of ideas is completed through a model, and the screening of the generated ideas is completed at the same time, which improves the screening efficiency.At the same time, matching demand and innovation through the model can not only provide the best combination but also discover a better breakthrough point of innovation through demand. The user needs that this research focuses on are not limited to the needs of users or potential users of the products under study. This study pays more attention to the possibility of innovation caused by the demand migration of other product users. Dealing with users’ demand information in this way can not only avoid the deviation in evaluation due to the knowledge limitation of experts but also avoid paying too much attention to the needs of lead user or a certain user group and creating information cocoons for innovation.


## 2. Research Design

In this paper, we apply computational thinking to analyze product innovation systems. Based on the comprehensive use of algorithmic thinking and system thinking in computational thinking, the Apriori algorithm that can carry out association analysis is applied to build the analysis model of product innovation intention in the Python environment, which provides technical support for the screening of product innovation system. Finally, in the Python environment, multiple groups of constraints are selected to simulate the simulated data, the operation results of the intention analysis model are displayed, and the multiple groups of results are compared and analyzed to show that the system model constructed is effective and efficient.Computational thinking is a kind of interdisciplinary thinking that integrates mathematics, engineering, and scientific thinking. Computational thinking first appeared in Professor Papert’s Mind-Storms: Children, Computers, and Powerful Ideas [44]. Although the publication of An Exploration in the Space of Mathematics Educations in 1996 referred to computational thinking again, it does not define computational thinking.Professor Jeannette M. Wing of Carnegie Mellon University (CMU) proposed the concept of computational thinking for the first time in the ACM journal in 2006. It refers to the application of thinking logic and methods from the field of computer science to design systems, understand human behavior, and solve problems. Since this kind of thinking can be transferred to a new situation, it also provides a new perspective for the study of natural, social, and other phenomena and a new idea for solving problems [45].Association analysis is to explore the relationship between data in a large amount of data. The Apriori algorithm used for association analysis in this study is the original algorithm jointly proposed by R. Agrawal and R. Srikant in 1994. In this algorithm, frequent item sets can be screened by iterative layer search, and strong association rules can be further obtained [46]. According to the nature of Apriori algorithm, “all non-empty subsets belonging to frequent item sets are frequent,” which can compress the search space. Therefore, the advantage of Apriori algorithm is that it can improve the efficiency of mining frequent sets. The algorithm combined with information technology makes it easy and efficient to deal with huge innovation and demand data.


## 3. Innovation-Demand Screening System Model

A new product will integrate several functions, styles, and supplementary service items. A new product can be a combination of different functions, appearance styles, and supporting services, or it can be a partial or complete innovation in functions, appearance styles, and supporting services. The number of theoretically possible product innovations is inestimable, but in fact, the number of new products truly accepted by users and the market and even sought after is very low compared with the number of theoretically possible innovative products. Therefore, an innovation-demand screening system model is established from the perspective of user needs to efficiently complete the screening of user needs and provide direction for product innovation.Figure2 is a product innovation-demand screening model constructed based on the analysis of the relationship between demand-innovation-product innovation. Among them, innovation needs can be regarded as composed of two parts. One is the inherent needs of users. That is, the needs of users for a product from their perspective. The second is the demand stimulated by user experience innovation. This experience is not limited to one product or category. Some experiences may be obtained by users in Class A products and leave a good experience, but this innovation has not been applied in Class B products. Although the two products A and B are completely different, and even belong to different categories, the demand will migrate through the medium of users. Therefore, the inherent needs of users and the needs inspired by user experience innovation together constitute innovation needs.Figure 2
Product innovation-demand screening model.Both the need for innovation and technological progress can foster innovation. The resulting innovation can be applied to new products in two ways. First, these innovations feed directly into inefficient product innovations. As many innovations are not screened and even if they are applied to new products, the new products may face the fate of failure as mentioned in the introduction. In the second path, innovation selects and combines re-supply product innovation through the innovation intention analysis system, and the new product formed has a higher matching degree with the user’s demand. Therefore, product innovation at this time is efficient. This efficiency is not only reflected in the rapid selection of product innovation intentions that are more in line with user needs from the massive innovation portfolio but also reflected in the fact that new products that meet user needs are more easily accepted by users and the market. Improving the success rate of the launch and landing of new products is an embodiment of efficiency. Users will have a new experience after embodying the new product. Some good experiences will re-ignite demand, creating a cycle.All users’ innovation needs and innovation points will be collected into the innovation intention analysis system for screening and integration to point out the direction of product innovation.


## 4. Innovation Intention Analysis Model

### 4.1. Innovation Intention Analysis Model Modeling Principle

Users may have some preferences or needs when they choose goods. These demands are the data that will be fed into the intention analysis system. Assume that the set of functional types isA = {a1, a2, a3, … , an}; the appearance category set is: B = {b1, b2, b3, … , bn}; the service type set is: C = {c1, c2, c3, … , cn}. Therefore, the functional demand set is D, the appearance demand set is E, and the service demand set is F. The existing function set is G, the existing design set is H, and the existing service form set is J. The relationship between sets A, B, C, D, E, F, G, H, and J are shown in Figure 3: D ⊆ A, G ⊆ A, E ⊆ B, H ⊆ B, F ⊆ C, and J ⊆ C.Figure 3
Innovation-demand factor relationship of product innovation screening system: (a) functional innovation-demand element set, (b) appearance innovation-demand element set, and (c) service innovation-demand element set.
(a)(b)(c)Table3 is an example of a list of user demands. The set of innovation demands presented by each user in Table 1 is equivalent to one “transaction” in the association analysis. Each different element in the demands transaction is equivalent to an “item” in association analysis. A set composed of different innovation demand elements is an “item set.” The market acceptance of new products developed to meet different sets of innovation demands is different. This also determines the success of product innovation. Therefore, the frequency of the innovation demand item set (innovation intention) in the transaction (survey of user demand intention) is expressed by the support degree.Table 3
Sample list of user demands.
UserInnovation-demand1a1, a2, a3, b1, c2, c32a2, a5, b3, c1, c2, c33a1, a5, b3, b5, c2, c34a1, a5, b1, b5, c1,……If {M} is used to represent an innovation demand item set, P is the frequency of occurrence of {M} in innovation demand transactions, Q is the total number of innovation demand transactions, and S is the support degree of innovation demand item set {M}, then(1)S=PQ.As not all innovation demand item sets have sufficient support, not all innovation demand item sets are meaningful, that is, not all innovation points or combinations will be welcomed by users. Therefore, minimum supportSmin needs to be set to screen out frequent demand item sets.Users also place varying degrees of importance on functionality, appearance, and service. So the focus on innovation is not the same. Take a product like a mobile phone, which integrates many technological innovations. Some users tend to choose products with an aesthetic appearance from products that meet their own use needs and give up the product with more functional advantages. So functional advantages are not necessarily the most attractive to some consumers. The same applies to appearance and services. Another example is a garment that uses a fabric with fine sparkle and long velvet texture on the surface. Although it is beautiful, it is not easy to take care of, and many users will be deterred. Users will be happy to buy if they are promised free services such as cleaning and grooming. Therefore, new products using this new fabric will be successfully accepted by users. In the process of product innovation, there are some small innovations in the function, appearance, and service of the product itself. To discover the relational relationships in these innovations, further association analysis is needed.In association analysis, association rules can be used to express the relationship between data. Let the innovation demand item set in front be {Mf} and the innovation demand item set in back be {Mb} when performing the innovation demand association analysis. Let the “confidence” of innovation demand association rules be T, then the confidence of {Mf} ⟶ {Mb} is Tfb, and the support of {Mf, Mb} and {Mf} is Sfb and Sf, respectively. Then there is:(2)Tfb=SfbSf.Not all innovations demand connection strengths between item sets are consistent. Association rules with high confidence are more reliable. Therefore, the minimum confidenceTmin is set to screen out strong association rules.The lifting degreeL can be regarded as a complementary index of confidence, which can further measure the reliability of association rules. Let the lifting degree of {Mf}⟶{Mb} be Lfb. There are:(3)Lfb=TfbSb=SfbSf∗Sb.WhenLfb > 1, it means that the demand item set {Mf} can promote the demand item set {Mb}.whenLfb = 1, it means that demand item set {Mf} is independent of demand item set {Mb}.whenLfb < 1, it means that demand item set {Mf} has a restraining effect on demand item set {Mb}.


### 4.2. Innovation Intention Analysis Model Building

The model of “innovation intention analysis” is to add “intention analysis” to the process of product innovation, so as to improve the efficiency of product innovation and the success rate of new products to market. The operation model of the whole innovation intention analysis system is shown in Figure4. First, the user’s demand for products is collected to form a number of innovative demand transactions. Then, according to the actual situation, the Smin and Tmin of the innovation demand item set are adjusted and determined, and the association rules of the innovation demand item set after preliminary screening are obtained. Judge the preliminary screening results. If you are satisfied with the preliminary screening results, continue to calculate the lifting degree, and further screen the results to obtain the finally screened association rules; if you are not satisfied with the preliminary screening result, you need to go back to the step of “adjusting and determining Smin and Tmin of the innovation demand item set” until you get a satisfactory screening result.Figure 4
Operation model of innovation intention analysis system.As the number of innovation demands “items” increases, the number of corresponding innovation demands “frequent item sets” and corresponding innovation demands “association rules” will also increase exponentially. In reality, there are a large number of “items” of users’ innovation demand. Therefore, in order to improve the efficiency of screening, the application of the Apriori algorithm to build an intention analysis model to analyze the data will greatly improve the efficiency of screening.The innovation intention analysis model is coded as follows:Algorithm 1:Innovation intention analysis model coding.
(1)
from apyori import apriori(2)
demand = [[{M1}], [{M2}], …, [{Mn}]](3)
relations = apriori(demand, min_support =s, min_confidence = t)(4)
for relation in relations:(5)
support = round(relation.support,m)(6)
for regulation in relation.ordered_statistics:(7)
front = list(regulation.items_base)(8)
back = list(regulation.items_add)(9)
if front = = []:(10)
continue(11)
related_list = str(front)+“⟶”+str(back)(12)
confidence = round(regulation.confidence,r)(13)
lift = round(regulation.lift,g)(14)
print(related_list, support, confidence, lift)The aforementioned code indicates: Import the apriori function in the environment of python, and entern transactions of user innovation demands. Make the minimum support: min_support = s, minimum confidence: min_confidence = t, where 0 < s < 1 and 0 < t < 1. Support is expressed in “support,” and the final numerical keep m decimal places. Confidence is expressed in “confidence” and the final numerical keep r decimal places. Lift is expressed in “lift” and the final numerical keep g decimal places. Use “front” to represent the front innovation demand item set, and use “back” to represent the back innovation demand item set. The strong association rule is expressed in the form of “the front innovation demand item set ⟶ the back innovation demand item set.” The final output is the filtered strong association rules, as well as support, confidence and lift.


## 4.1. Innovation Intention Analysis Model Modeling Principle

Users may have some preferences or needs when they choose goods. These demands are the data that will be fed into the intention analysis system. Assume that the set of functional types isA = {a1, a2, a3, … , an}; the appearance category set is: B = {b1, b2, b3, … , bn}; the service type set is: C = {c1, c2, c3, … , cn}. Therefore, the functional demand set is D, the appearance demand set is E, and the service demand set is F. The existing function set is G, the existing design set is H, and the existing service form set is J. The relationship between sets A, B, C, D, E, F, G, H, and J are shown in Figure 3: D ⊆ A, G ⊆ A, E ⊆ B, H ⊆ B, F ⊆ C, and J ⊆ C.Figure 3
Innovation-demand factor relationship of product innovation screening system: (a) functional innovation-demand element set, (b) appearance innovation-demand element set, and (c) service innovation-demand element set.
(a)(b)(c)Table3 is an example of a list of user demands. The set of innovation demands presented by each user in Table 1 is equivalent to one “transaction” in the association analysis. Each different element in the demands transaction is equivalent to an “item” in association analysis. A set composed of different innovation demand elements is an “item set.” The market acceptance of new products developed to meet different sets of innovation demands is different. This also determines the success of product innovation. Therefore, the frequency of the innovation demand item set (innovation intention) in the transaction (survey of user demand intention) is expressed by the support degree.Table 3
Sample list of user demands.
UserInnovation-demand1a1, a2, a3, b1, c2, c32a2, a5, b3, c1, c2, c33a1, a5, b3, b5, c2, c34a1, a5, b1, b5, c1,……If {M} is used to represent an innovation demand item set, P is the frequency of occurrence of {M} in innovation demand transactions, Q is the total number of innovation demand transactions, and S is the support degree of innovation demand item set {M}, then(1)S=PQ.As not all innovation demand item sets have sufficient support, not all innovation demand item sets are meaningful, that is, not all innovation points or combinations will be welcomed by users. Therefore, minimum supportSmin needs to be set to screen out frequent demand item sets.Users also place varying degrees of importance on functionality, appearance, and service. So the focus on innovation is not the same. Take a product like a mobile phone, which integrates many technological innovations. Some users tend to choose products with an aesthetic appearance from products that meet their own use needs and give up the product with more functional advantages. So functional advantages are not necessarily the most attractive to some consumers. The same applies to appearance and services. Another example is a garment that uses a fabric with fine sparkle and long velvet texture on the surface. Although it is beautiful, it is not easy to take care of, and many users will be deterred. Users will be happy to buy if they are promised free services such as cleaning and grooming. Therefore, new products using this new fabric will be successfully accepted by users. In the process of product innovation, there are some small innovations in the function, appearance, and service of the product itself. To discover the relational relationships in these innovations, further association analysis is needed.In association analysis, association rules can be used to express the relationship between data. Let the innovation demand item set in front be {Mf} and the innovation demand item set in back be {Mb} when performing the innovation demand association analysis. Let the “confidence” of innovation demand association rules be T, then the confidence of {Mf} ⟶ {Mb} is Tfb, and the support of {Mf, Mb} and {Mf} is Sfb and Sf, respectively. Then there is:(2)Tfb=SfbSf.Not all innovations demand connection strengths between item sets are consistent. Association rules with high confidence are more reliable. Therefore, the minimum confidenceTmin is set to screen out strong association rules.The lifting degreeL can be regarded as a complementary index of confidence, which can further measure the reliability of association rules. Let the lifting degree of {Mf}⟶{Mb} be Lfb. There are:(3)Lfb=TfbSb=SfbSf∗Sb.WhenLfb > 1, it means that the demand item set {Mf} can promote the demand item set {Mb}.whenLfb = 1, it means that demand item set {Mf} is independent of demand item set {Mb}.whenLfb < 1, it means that demand item set {Mf} has a restraining effect on demand item set {Mb}.


## 4.2. Innovation Intention Analysis Model Building

The model of “innovation intention analysis” is to add “intention analysis” to the process of product innovation, so as to improve the efficiency of product innovation and the success rate of new products to market. The operation model of the whole innovation intention analysis system is shown in Figure4. First, the user’s demand for products is collected to form a number of innovative demand transactions. Then, according to the actual situation, the Smin and Tmin of the innovation demand item set are adjusted and determined, and the association rules of the innovation demand item set after preliminary screening are obtained. Judge the preliminary screening results. If you are satisfied with the preliminary screening results, continue to calculate the lifting degree, and further screen the results to obtain the finally screened association rules; if you are not satisfied with the preliminary screening result, you need to go back to the step of “adjusting and determining Smin and Tmin of the innovation demand item set” until you get a satisfactory screening result.Figure 4
Operation model of innovation intention analysis system.As the number of innovation demands “items” increases, the number of corresponding innovation demands “frequent item sets” and corresponding innovation demands “association rules” will also increase exponentially. In reality, there are a large number of “items” of users’ innovation demand. Therefore, in order to improve the efficiency of screening, the application of the Apriori algorithm to build an intention analysis model to analyze the data will greatly improve the efficiency of screening.The innovation intention analysis model is coded as follows:Algorithm 1:Innovation intention analysis model coding.
(1)
from apyori import apriori(2)
demand = [[{M1}], [{M2}], …, [{Mn}]](3)
relations = apriori(demand, min_support =s, min_confidence = t)(4)
for relation in relations:(5)
support = round(relation.support,m)(6)
for regulation in relation.ordered_statistics:(7)
front = list(regulation.items_base)(8)
back = list(regulation.items_add)(9)
if front = = []:(10)
continue(11)
related_list = str(front)+“⟶”+str(back)(12)
confidence = round(regulation.confidence,r)(13)
lift = round(regulation.lift,g)(14)
print(related_list, support, confidence, lift)The aforementioned code indicates: Import the apriori function in the environment of python, and entern transactions of user innovation demands. Make the minimum support: min_support = s, minimum confidence: min_confidence = t, where 0 < s < 1 and 0 < t < 1. Support is expressed in “support,” and the final numerical keep m decimal places. Confidence is expressed in “confidence” and the final numerical keep r decimal places. Lift is expressed in “lift” and the final numerical keep g decimal places. Use “front” to represent the front innovation demand item set, and use “back” to represent the back innovation demand item set. The strong association rule is expressed in the form of “the front innovation demand item set ⟶ the back innovation demand item set.” The final output is the filtered strong association rules, as well as support, confidence and lift.


## 5. Product Innovation Intention Analysis Model Simulation and Analysis

### 5.1. Simulation of the Influence of Min_Support and Min_Confidence on Screening Results

Create four pieces of user innovation demand data according to the contents of the first four groups of users’ innovation demand in Table3. In the coding of the innovation intention analysis model, there are: demand = [[“a1,” “a2,” a3,” “b1,” “c2,” “c3”], [“a2,” “a5,” “b3,” “c1,” “c2,” “c3”], [“a1,” “a5,” “b3,” “b5,” “c2,” “c3”], [“a1,” “a5,” “b1”, “b5,” “c1”]]. In the code, m = 3 and r = 3, g = 3. The five groups of min_support and min_confidence values in Table 4 are respectively brought into coding, and the coding analysis results are run.Table 4
Five groups of min_support and min_confidence for simulation analysis.
S/NMin_support (s)Min_confidence (t)“Support” retains decimal places (m)“Confidence” retains decimal places (r)“Lift” retains decimal places (g)10.20.333320.20.533330.20.733340.40.333350.60.3333When the first group of simulation data in Table4 is run, the system runs to get 1899 strong association rules, of which there are 1515 items when lift > 1. Lift = 1.333 with 540 items. Lift = 2, with 699 items; lift = 4, there are 276 items. The results show that the confidence level corresponding to the association rule with a lift of 4 is also extremely high, reaching 1. The encoding is as follows:Algorithm 2: Innovation intention analysis model coding operation simulation.
(1)
from apyori import apriori(2)
demand = [[“a1,” “a2,” a3,” “b1,” “c2,” “c3”], [“a2,” “a5,” “b3,” “c1,” “c2,” “c3”], [“a1,” “a5,” “b3,” “b5,” “c2,” “c3”], [“a1,” “a5,” “b1”, “b5,” “c1”]](4)
relations = apriori(demand, min_support = 0.2, min_confidence = 0.3)(5)
for relation in relations:(6)
support = round(relation.support, 3)(7)
for regulation in relation.ordered_statistics:(8)
front = list(regulation.items_base)(9)
back = list(regulation.items_add)(10)
if front = = []:(11)
continue(12)
related_list = str(front)+“⟶“+str(back)(13)
confidence = round(regulation.confidence, 3)(14)
lift = round(regulation.lift, 3)(15)
print(related_list, support, confidence, lift)When the second group of simulation data in Table4 is run, the system runs to get 1605 strong association rules, of which there are 1274 items when lift > 1. Lift = 1.333 has 300 items. Lift = 2, with 698 items; lift = 4, there are 276 items. The results show that the confidence level corresponding to the association rule with a lift of four is also extremely high, reaching 1.When the third group of simulation data in Table4 is run, the system runs to get 902 strong association rules, of which there are 902 items when lift > 1. That is to say, among all the strong association rules screened out, the former innovation demand item set has a promoting effect on the latter innovation demand item set. In the filtered strong association rules: lift = 1.333 has 271 items. Lift = 2, with 355 items; lift = 4, there are 276 items. The results show that the corresponding confidence levels of all association rules are extremely high, reaching 1.When the 4th group of simulation data in Table4 is run, the system runs to get 87 strong association rules, of which there are 73 items when lift > 1. Lift = 1.333 has 61 items. Lift = 2, with 12 items. The results show that the confidence level corresponding to the association rule with a lift of 2 is also extremely high, reaching 1.When the 5th group of simulation data in Table4 is run, the system runs to get 2 strong association rules. Its corresponding confidence is 1, and the lift is 1.333.It can be seen that increasing support and confidence can narrow the screening range. The number of strong association rules is more sensitive to the adjustment of support. Applying this model to adjust the support and confidence to appropriate values is very helpful to improve the screening efficiency of innovation portfolio intention.As shown in Table5, it is a partial result of input min _ support = 0.2 and min _ confidence = 0.3 into the innovation intention analysis model. Among them, the confidence degree of association Rule 3 is 0.333 and the lift is 1.333 > 1. The confidence degree of association Rule 5 is 0.667 and the lift is 0.8891. Although the confidence of association rule 3 is lower than that of association rule 5, the lift of association rule 3 is higher than that of association rule 5. Before and after association rule 3, there is a promoting relations between the innovation demand item sets, while before and after association rule 5, there is a restraining relationship between the innovation demand item sets. It can be seen that lift discrimination is very effective as a supplement to confidence discrimination.Table 5
Strong association rules (min_support = 0.2, min_confidence = 0.3).
S/NAssociation ruleSupportConfidenceLift1[“a1”] ⟶ [“a2”]0.250.3330.6672[“a2”] ⟶ [“a1”]0.250.50.6673[“a1”] ⟶ [“a3”]0.250.3331.3334[“a3”] ⟶ [“a1”]0.2511.3335[“a1”] ⟶ [“a5”]0.50.6670.8896[“a5”] ⟶ [“a1”]0.50.6670.8897[“a1”] ⟶ [“b1”]0.50.6671.333⋮⋮⋮⋮⋮80[“a2”] ⟶ [“a1”, “a3”]0.250.5281[“a3”] ⟶ [“a1”, “a2”]0.2514⋮⋮⋮⋮⋮1895[“c2,” “c3,” “b3,” “a5,” “a2”] ⟶ [“c1”]0.25121896[“c1,” “c3,” “c2,” “a5,” “a2”] ⟶ [“b3”]0.25121897[“c1,” “c3,” “c2, “b3,” “a2”] ⟶ [“a5”]0.2511.3331898[“c1,” “c2,” “c3,” “b3,” “a5”] ⟶ [“a2”]0.25121899[“c1,” “c2,” “c3,” “b3,” “a5”] ⟶ [“a2”]0.2512Table6 shows the association rules that contain both a1 and b1 items in the preceding item set of some demands in the association rules obtained by min_support = 0.2 and min_confidence = 0.3. Among them, the a1 item, the b1 item and the c3 item appear in the association rule 1 and the association rule 24. From association rule 24, we can see that the innovation item set [“a1,” “b1”] and the innovation item set [“c3”] are mutually inhibiting relationships. However, in association rule 1, the innovative item sets [“a1,” “b1,” “c3”] and the innovative item sets [“c2,” “a3”] have a very obvious role in promoting each other. In the same situation, we see that association rule 9, association rule 11, and association rule 12 have the same confidence, and they all contain: a1 item, b1 item and a5 item. However, from the perspective of the corresponding lift value, the relations between item sets are inhibition, promotion, and irrelevance. Through the analysis of the above two sets of examples, it also shows the impact of changes in the combination of similar clothing innovation elements proposed in Section 4.1 of this paper on user purchases from another perspective. That is, adjusting or adding some innovations will make innovative products popular that users would not choose in the past. It can be seen that the scientific selection and combination of innovation needs are the key to the popularity of new products.Table 6
Partial association rules of demand antecedent set containing botha1 and b1 items (min_support = 0.2, min_confidence = 0.3).
S/NAssociation ruleSupportConfidenceLift1[“a1,” “b1”, “c3”] ⟶ [“c2,” “a3”]0.25142[“a1,” “b1,” “c3”] ⟶ [“c2”]0.2511.3333[“a1,” “b1”] ⟶ [“a2”, “c2,” “a3,” “c3”]0.250.524[“a1,” “b1”] ⟶ [“a2,” “c2,” “c3”]0.250.515[“a1,” “b1”] ⟶ [“a2”]0.250.516[“a1,” “b1”] ⟶ [“a3,” “a2”]0.250.527[“a1,” “b1”] ⟶ [“a3”]0.250.528[“a1,” “b1”] ⟶ [“a5,” “b5”]0.250.519[“a1,” “b1”] ⟶ [“a5”]0.250.50.66710[“a1,” “b1”] ⟶ [“b5”]0.250.5111[“a1,” “b1”] ⟶ [“c1,” “a5,” “b5”]0.250.5212[“a1,” “b1”] ⟶ [“c1,” “a5”]0.250.5113[“a1,” “b1’] ⟶ [“c1,” “b5”]0.250.5214[“a1,” “b1”] ⟶ [“c1”]0.250.5115[“a1,” “b1”] ⟶ [“c2,” “a2”]0.250.5116[“a1,” “b1”] ⟶ [“c2,” “a3,” “a2”]0.250.5217[“a1,” “b1”] ⟶ [“c2,” “a3,” “c3”]0.250.5218[“a1,” “b1”] ⟶ [“c2,” “a3”]0.250.5219[“a1,” “b1”] ⟶ [“c2,” “c3”]0.250.50.66720[“a1,” “b1”] ⟶ [“c2”]0.250.50.66721[“a1,” “b1”] ⟶ [“c3,” “a2”]0.250.5122[“a1,” “b1”] ⟶ [“c3,” “a3,” “a2”]0.250.5223[“a1,” “b1”] ⟶ [“c3,” “a3”]0.250.5224[“a1,” b1”] ⟶ [“c3”]0.250.50.667


### 5.2. Innovation Intention Analysis Model Screening Efficiency Simulation

According to the definitions of frequent sets and association rules, we can find the solution formulas of the number U of nonempty frequent item sets and the number V of association rules, wherex represents the number of items. The number of items in Table 3 is 10, so x = 10 is used for simulation.Number of non-empty frequent item sets:U = 2x − 1.Whenx = 10, U = 210–1 = 1023.Number of association rules:V = 3x − 2x + 1 + 1.Whenx = 10, V = 310 − 210 + 1+1 = 57,002.During the simulation run, only four demands transactions and 10 demands items were entered. Around 1,023 nonempty frequent item sets and 57,002 association rules will be generated. But in reality, the numbers will be much bigger.As can be seen from Table7, by adjusting the min_support and the min_confidence, and choosing the appropriate lift degree, the ratio of the number of strong association rules to the number of original rules can be easily reduced to a few parts per 100,000 after being screened by the innovation intention analysis model. A modest adjustment can also bring the ratio even lower.Table 7
Innovative intention analysis model screening efficiency performance.
S/NScreening conditionVsR (%)1min_support = 0.2, min_confidence = 0.3, lift > 115152.65782min_support = 0.2, min_confidence = 0.5, lift>112742.23503min_support = 0.2, min_confidence = 0.7, lift > 19021.58244min_support = 0.4, min_confidence = 0.3, lift > 1730.12815min_support = 0.6, min_confidence = 0.3, lift > 120.0035Notes: ① The number of strong association rules after filtering is represented byVs. ② The ratio of the number of strong association rules to the number of original rules filtered by the innovation intention analysis model is denoted by R.It can be seen that this model can greatly improve the screening efficiency. But we also see that when the min_support and min_confidence are too high, many useful association rules will be filtered out. Therefore, the application of innovation intention analysis system, according to the actual situation and the experience of managers to develop appropriate min_support and min_confidence can be very efficient to complete the screening process.


## 5.1. Simulation of the Influence of Min_Support and Min_Confidence on Screening Results

Create four pieces of user innovation demand data according to the contents of the first four groups of users’ innovation demand in Table3. In the coding of the innovation intention analysis model, there are: demand = [[“a1,” “a2,” a3,” “b1,” “c2,” “c3”], [“a2,” “a5,” “b3,” “c1,” “c2,” “c3”], [“a1,” “a5,” “b3,” “b5,” “c2,” “c3”], [“a1,” “a5,” “b1”, “b5,” “c1”]]. In the code, m = 3 and r = 3, g = 3. The five groups of min_support and min_confidence values in Table 4 are respectively brought into coding, and the coding analysis results are run.Table 4
Five groups of min_support and min_confidence for simulation analysis.
S/NMin_support (s)Min_confidence (t)“Support” retains decimal places (m)“Confidence” retains decimal places (r)“Lift” retains decimal places (g)10.20.333320.20.533330.20.733340.40.333350.60.3333When the first group of simulation data in Table4 is run, the system runs to get 1899 strong association rules, of which there are 1515 items when lift > 1. Lift = 1.333 with 540 items. Lift = 2, with 699 items; lift = 4, there are 276 items. The results show that the confidence level corresponding to the association rule with a lift of 4 is also extremely high, reaching 1. The encoding is as follows:Algorithm 2: Innovation intention analysis model coding operation simulation.
(1)
from apyori import apriori(2)
demand = [[“a1,” “a2,” a3,” “b1,” “c2,” “c3”], [“a2,” “a5,” “b3,” “c1,” “c2,” “c3”], [“a1,” “a5,” “b3,” “b5,” “c2,” “c3”], [“a1,” “a5,” “b1”, “b5,” “c1”]](4)
relations = apriori(demand, min_support = 0.2, min_confidence = 0.3)(5)
for relation in relations:(6)
support = round(relation.support, 3)(7)
for regulation in relation.ordered_statistics:(8)
front = list(regulation.items_base)(9)
back = list(regulation.items_add)(10)
if front = = []:(11)
continue(12)
related_list = str(front)+“⟶“+str(back)(13)
confidence = round(regulation.confidence, 3)(14)
lift = round(regulation.lift, 3)(15)
print(related_list, support, confidence, lift)When the second group of simulation data in Table4 is run, the system runs to get 1605 strong association rules, of which there are 1274 items when lift > 1. Lift = 1.333 has 300 items. Lift = 2, with 698 items; lift = 4, there are 276 items. The results show that the confidence level corresponding to the association rule with a lift of four is also extremely high, reaching 1.When the third group of simulation data in Table4 is run, the system runs to get 902 strong association rules, of which there are 902 items when lift > 1. That is to say, among all the strong association rules screened out, the former innovation demand item set has a promoting effect on the latter innovation demand item set. In the filtered strong association rules: lift = 1.333 has 271 items. Lift = 2, with 355 items; lift = 4, there are 276 items. The results show that the corresponding confidence levels of all association rules are extremely high, reaching 1.When the 4th group of simulation data in Table4 is run, the system runs to get 87 strong association rules, of which there are 73 items when lift > 1. Lift = 1.333 has 61 items. Lift = 2, with 12 items. The results show that the confidence level corresponding to the association rule with a lift of 2 is also extremely high, reaching 1.When the 5th group of simulation data in Table4 is run, the system runs to get 2 strong association rules. Its corresponding confidence is 1, and the lift is 1.333.It can be seen that increasing support and confidence can narrow the screening range. The number of strong association rules is more sensitive to the adjustment of support. Applying this model to adjust the support and confidence to appropriate values is very helpful to improve the screening efficiency of innovation portfolio intention.As shown in Table5, it is a partial result of input min _ support = 0.2 and min _ confidence = 0.3 into the innovation intention analysis model. Among them, the confidence degree of association Rule 3 is 0.333 and the lift is 1.333 > 1. The confidence degree of association Rule 5 is 0.667 and the lift is 0.8891. Although the confidence of association rule 3 is lower than that of association rule 5, the lift of association rule 3 is higher than that of association rule 5. Before and after association rule 3, there is a promoting relations between the innovation demand item sets, while before and after association rule 5, there is a restraining relationship between the innovation demand item sets. It can be seen that lift discrimination is very effective as a supplement to confidence discrimination.Table 5
Strong association rules (min_support = 0.2, min_confidence = 0.3).
S/NAssociation ruleSupportConfidenceLift1[“a1”] ⟶ [“a2”]0.250.3330.6672[“a2”] ⟶ [“a1”]0.250.50.6673[“a1”] ⟶ [“a3”]0.250.3331.3334[“a3”] ⟶ [“a1”]0.2511.3335[“a1”] ⟶ [“a5”]0.50.6670.8896[“a5”] ⟶ [“a1”]0.50.6670.8897[“a1”] ⟶ [“b1”]0.50.6671.333⋮⋮⋮⋮⋮80[“a2”] ⟶ [“a1”, “a3”]0.250.5281[“a3”] ⟶ [“a1”, “a2”]0.2514⋮⋮⋮⋮⋮1895[“c2,” “c3,” “b3,” “a5,” “a2”] ⟶ [“c1”]0.25121896[“c1,” “c3,” “c2,” “a5,” “a2”] ⟶ [“b3”]0.25121897[“c1,” “c3,” “c2, “b3,” “a2”] ⟶ [“a5”]0.2511.3331898[“c1,” “c2,” “c3,” “b3,” “a5”] ⟶ [“a2”]0.25121899[“c1,” “c2,” “c3,” “b3,” “a5”] ⟶ [“a2”]0.2512Table6 shows the association rules that contain both a1 and b1 items in the preceding item set of some demands in the association rules obtained by min_support = 0.2 and min_confidence = 0.3. Among them, the a1 item, the b1 item and the c3 item appear in the association rule 1 and the association rule 24. From association rule 24, we can see that the innovation item set [“a1,” “b1”] and the innovation item set [“c3”] are mutually inhibiting relationships. However, in association rule 1, the innovative item sets [“a1,” “b1,” “c3”] and the innovative item sets [“c2,” “a3”] have a very obvious role in promoting each other. In the same situation, we see that association rule 9, association rule 11, and association rule 12 have the same confidence, and they all contain: a1 item, b1 item and a5 item. However, from the perspective of the corresponding lift value, the relations between item sets are inhibition, promotion, and irrelevance. Through the analysis of the above two sets of examples, it also shows the impact of changes in the combination of similar clothing innovation elements proposed in Section 4.1 of this paper on user purchases from another perspective. That is, adjusting or adding some innovations will make innovative products popular that users would not choose in the past. It can be seen that the scientific selection and combination of innovation needs are the key to the popularity of new products.Table 6
Partial association rules of demand antecedent set containing botha1 and b1 items (min_support = 0.2, min_confidence = 0.3).
S/NAssociation ruleSupportConfidenceLift1[“a1,” “b1”, “c3”] ⟶ [“c2,” “a3”]0.25142[“a1,” “b1,” “c3”] ⟶ [“c2”]0.2511.3333[“a1,” “b1”] ⟶ [“a2”, “c2,” “a3,” “c3”]0.250.524[“a1,” “b1”] ⟶ [“a2,” “c2,” “c3”]0.250.515[“a1,” “b1”] ⟶ [“a2”]0.250.516[“a1,” “b1”] ⟶ [“a3,” “a2”]0.250.527[“a1,” “b1”] ⟶ [“a3”]0.250.528[“a1,” “b1”] ⟶ [“a5,” “b5”]0.250.519[“a1,” “b1”] ⟶ [“a5”]0.250.50.66710[“a1,” “b1”] ⟶ [“b5”]0.250.5111[“a1,” “b1”] ⟶ [“c1,” “a5,” “b5”]0.250.5212[“a1,” “b1”] ⟶ [“c1,” “a5”]0.250.5113[“a1,” “b1’] ⟶ [“c1,” “b5”]0.250.5214[“a1,” “b1”] ⟶ [“c1”]0.250.5115[“a1,” “b1”] ⟶ [“c2,” “a2”]0.250.5116[“a1,” “b1”] ⟶ [“c2,” “a3,” “a2”]0.250.5217[“a1,” “b1”] ⟶ [“c2,” “a3,” “c3”]0.250.5218[“a1,” “b1”] ⟶ [“c2,” “a3”]0.250.5219[“a1,” “b1”] ⟶ [“c2,” “c3”]0.250.50.66720[“a1,” “b1”] ⟶ [“c2”]0.250.50.66721[“a1,” “b1”] ⟶ [“c3,” “a2”]0.250.5122[“a1,” “b1”] ⟶ [“c3,” “a3,” “a2”]0.250.5223[“a1,” “b1”] ⟶ [“c3,” “a3”]0.250.5224[“a1,” b1”] ⟶ [“c3”]0.250.50.667


## 5.2. Innovation Intention Analysis Model Screening Efficiency Simulation

According to the definitions of frequent sets and association rules, we can find the solution formulas of the number U of nonempty frequent item sets and the number V of association rules, wherex represents the number of items. The number of items in Table 3 is 10, so x = 10 is used for simulation.Number of non-empty frequent item sets:U = 2x − 1.Whenx = 10, U = 210–1 = 1023.Number of association rules:V = 3x − 2x + 1 + 1.Whenx = 10, V = 310 − 210 + 1+1 = 57,002.During the simulation run, only four demands transactions and 10 demands items were entered. Around 1,023 nonempty frequent item sets and 57,002 association rules will be generated. But in reality, the numbers will be much bigger.As can be seen from Table7, by adjusting the min_support and the min_confidence, and choosing the appropriate lift degree, the ratio of the number of strong association rules to the number of original rules can be easily reduced to a few parts per 100,000 after being screened by the innovation intention analysis model. A modest adjustment can also bring the ratio even lower.Table 7
Innovative intention analysis model screening efficiency performance.
S/NScreening conditionVsR (%)1min_support = 0.2, min_confidence = 0.3, lift > 115152.65782min_support = 0.2, min_confidence = 0.5, lift>112742.23503min_support = 0.2, min_confidence = 0.7, lift > 19021.58244min_support = 0.4, min_confidence = 0.3, lift > 1730.12815min_support = 0.6, min_confidence = 0.3, lift > 120.0035Notes: ① The number of strong association rules after filtering is represented byVs. ② The ratio of the number of strong association rules to the number of original rules filtered by the innovation intention analysis model is denoted by R.It can be seen that this model can greatly improve the screening efficiency. But we also see that when the min_support and min_confidence are too high, many useful association rules will be filtered out. Therefore, the application of innovation intention analysis system, according to the actual situation and the experience of managers to develop appropriate min_support and min_confidence can be very efficient to complete the screening process.


## 6. Conclusion

①
By constructing the model of an innovation-demand screening system and analyzing the system, we can see that the relationship between demand and innovation is close, and the relationship between demand and innovation is not a one-way relationship, but a circular relationship. Demand is updated by innovation, and innovation is expanded by demand. Innovations and demands are mutually reinforcing and transforming.②
Through the analysis of the “Innovation Intention Analysis” model, we can not only combine the existing innovative technologies scientifically to produce innovative products that meet the needs of users but also make clear the direction of technological innovation and product innovation through the analysis of the needs, thus solving the problems of “quality” and “quantity.” It not only promotes the generation of new products but also enables new products to truly land, be accepted by users, and truly create value.③
Through simulation analysis, it can be proved that it is effective and efficient to apply computational thinking and correlation analysis to solve the problem of innovation combination and screening. It is very effective to use the degree of lift as a supplement to the degree of confidence. Increasing the min_support and min_confidence can narrow the screening range. The number of strong association rules is more sensitive to the adjustment of min_support. However, too high min_support and too high min_confidence settings can filter out useful data. Therefore, according to the actual situation and the experience of managers, the formulation of appropriate min_support and min_confidence is conducive to the efficient completion of innovation intention analysis. Adjusting the innovation mix according to the results of the innovation intention analysis can make new products more popular. It can reduce the trial-and-error cost of innovation, improve the quality and quantity of product innovation, and improve economic performance.


## 7. Deficiencies of This Study

①
No consideration is given to the cost of new products produced by the screened innovative ideas.②
The tradeoff between “satisfying demand” and “satisfactory price” is not discussed. How to find the equilibrium point of “satisfying demand” and “satisfactory price” among different consumer groups for different products can be the direction of future research.



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*Source: 2543872-2022-07-22.xml*