Business model innovation faces multiple tests of legitimacy.Most extant research in this area has been conducted from institutional and strategic perspectives while paying insufficient attention to the perspective of...Business model innovation faces multiple tests of legitimacy.Most extant research in this area has been conducted from institutional and strategic perspectives while paying insufficient attention to the perspective of evaluators.Based on the institutionalization of China's online car-hailing industry from 2012 to 2018,this paper analyzes the legitimacy judgment of the stakeholders from the perspective of evaluator categorization and explores the legitimation mechanism of business model innovation.It finds that evaluators judge the legitimacy of business models based on category cognition.Therefore,to achieve the bridging,spillover,and accumulation effects of legitimacy,the legitimation strategy of online car-hailing platforms should dynamically adapt to different evaluators,judgment models,and categorization standards.Ultimately,as quantitative changes lead to qualitative changes,the legitimation of innovative business models is achieved in this way.In this process,stakeholders categorize and evaluate online car-hailing based on prototypes and value goals,and establish a two-way interactive mechanism,which is from behavior guided by cognition to cognition given feedback by behavior.This paper combines the legitimacy judgment with category theory to explain how individual cognition drives the emergence of new categories and identifies a series of legitimacy strategies based on categorization,thus providing theoretical support and practical inspiration for exploring the legitimation of business model innovation.展开更多
暂时性感官支配法(temporal dominance of sensations,TDS)是一种时间多维动态感官分析方法。该方法向评估者提供一份描述产品属性的列表,评价员在品尝的每个时刻选择他们认为占支配地位的感知,以此获得动态、多属性的感官评价结果。本...暂时性感官支配法(temporal dominance of sensations,TDS)是一种时间多维动态感官分析方法。该方法向评估者提供一份描述产品属性的列表,评价员在品尝的每个时刻选择他们认为占支配地位的感知,以此获得动态、多属性的感官评价结果。本文对TDS的概念、评价流程和其在食品感官评价中的应用进展进行综述,并对TDS的改进方向进行总结,以期从理解人们在消费食物过程中感官感知动态变化角度为产品研发提供新思路。展开更多
In software testing,the quality of test cases is crucial,but manual generation is time-consuming.Various automatic test case generation methods exist,requiring careful selection based on program features.Current evalu...In software testing,the quality of test cases is crucial,but manual generation is time-consuming.Various automatic test case generation methods exist,requiring careful selection based on program features.Current evaluation methods compare a limited set of metrics,which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment.To address this,we propose an evaluation tool,the Test Case Generation Evaluator(TCGE),based on the learning to rank(L2R)algorithm.Unlike previous approaches,our method comprehensively evaluates algorithms by considering multiple metrics,resulting in a more reasoned assessment.The main principle of the TCGE is the formation of feature vectors that are of concern by the tester.Through training,the feature vectors are sorted to generate a list,with the order of the methods on the list determined according to their effectiveness on the tested assembly.We implement TCGE using three L2R algorithms:Listnet,LambdaMART,and RFLambdaMART.Evaluation employs a dataset with features of classical test case generation algorithms and three metrics—Normalized Discounted Cumulative Gain(NDCG),Mean Average Precision(MAP),and Mean Reciprocal Rank(MRR).Results demonstrate the TCGE’s superior effectiveness in evaluating test case generation algorithms compared to other methods.Among the three L2R algorithms,RFLambdaMART proves the most effective,achieving an accuracy above 96.5%,surpassing LambdaMART by 2%and Listnet by 1.5%.Consequently,the TCGE framework exhibits significant application value in the evaluation of test case generation algorithms.展开更多
基金supported by the National Natural Science Foundation of China(No.72072008)the special fund for first-class discipline construction of Beijing University of Chemical Technology(No.XK1802-5).
文摘Business model innovation faces multiple tests of legitimacy.Most extant research in this area has been conducted from institutional and strategic perspectives while paying insufficient attention to the perspective of evaluators.Based on the institutionalization of China's online car-hailing industry from 2012 to 2018,this paper analyzes the legitimacy judgment of the stakeholders from the perspective of evaluator categorization and explores the legitimation mechanism of business model innovation.It finds that evaluators judge the legitimacy of business models based on category cognition.Therefore,to achieve the bridging,spillover,and accumulation effects of legitimacy,the legitimation strategy of online car-hailing platforms should dynamically adapt to different evaluators,judgment models,and categorization standards.Ultimately,as quantitative changes lead to qualitative changes,the legitimation of innovative business models is achieved in this way.In this process,stakeholders categorize and evaluate online car-hailing based on prototypes and value goals,and establish a two-way interactive mechanism,which is from behavior guided by cognition to cognition given feedback by behavior.This paper combines the legitimacy judgment with category theory to explain how individual cognition drives the emergence of new categories and identifies a series of legitimacy strategies based on categorization,thus providing theoretical support and practical inspiration for exploring the legitimation of business model innovation.
文摘暂时性感官支配法(temporal dominance of sensations,TDS)是一种时间多维动态感官分析方法。该方法向评估者提供一份描述产品属性的列表,评价员在品尝的每个时刻选择他们认为占支配地位的感知,以此获得动态、多属性的感官评价结果。本文对TDS的概念、评价流程和其在食品感官评价中的应用进展进行综述,并对TDS的改进方向进行总结,以期从理解人们在消费食物过程中感官感知动态变化角度为产品研发提供新思路。
文摘In software testing,the quality of test cases is crucial,but manual generation is time-consuming.Various automatic test case generation methods exist,requiring careful selection based on program features.Current evaluation methods compare a limited set of metrics,which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment.To address this,we propose an evaluation tool,the Test Case Generation Evaluator(TCGE),based on the learning to rank(L2R)algorithm.Unlike previous approaches,our method comprehensively evaluates algorithms by considering multiple metrics,resulting in a more reasoned assessment.The main principle of the TCGE is the formation of feature vectors that are of concern by the tester.Through training,the feature vectors are sorted to generate a list,with the order of the methods on the list determined according to their effectiveness on the tested assembly.We implement TCGE using three L2R algorithms:Listnet,LambdaMART,and RFLambdaMART.Evaluation employs a dataset with features of classical test case generation algorithms and three metrics—Normalized Discounted Cumulative Gain(NDCG),Mean Average Precision(MAP),and Mean Reciprocal Rank(MRR).Results demonstrate the TCGE’s superior effectiveness in evaluating test case generation algorithms compared to other methods.Among the three L2R algorithms,RFLambdaMART proves the most effective,achieving an accuracy above 96.5%,surpassing LambdaMART by 2%and Listnet by 1.5%.Consequently,the TCGE framework exhibits significant application value in the evaluation of test case generation algorithms.
