Consumer product valuation and interorganizational collaboration and innovation in small manufacturing

本项目将解释消费者评级在小规模制造公司如何选择合作伙伴以及由此产生的合作模式如何随着时间的推移塑造行业方面所起的作用。协作是企业相互学习和创新的重要途径。与此同时，消费者通过评级平台发出对创新的认可信号，比如在线零售网站上的评级平台。手工酿造行业是一个战略研究场所，用于在小规模制造的背景下研究这种行业-消费者动态。跨组织协作在该行业中很常见。这个行业也有一个消费者社区，他们在公共网站上积极为产品打分。与许多行业的组织一样，小型制造商的合作策略各不相同：一些与许多其他公司合作；另一些与同一家公司重复合作；还有一些只合作一次，再也不会。随着时间的推移，组织间协作的模式构成了行业结构。该项目将构建和分析来自两个产品评级网站的数据集，以模拟美国三个州制造商之间的合作网络。结合对数据集中随机抽样的组织成员的采访，分析将展示消费者评级如何影响小规模制造商之间的合作决策，并最终塑造行业。研究结果将汇集并有助于经济社会学中的估值和组织科学中的协作的文献，以及为管理者提供信息？关于协作及其预期结果的战略决策。这个项目确定并解释了构建行业的一个关键机制：消费者评级和组织间合作之间的递归关系。当组织以非层级关系进行合作，以共享知识和资源，并以风险太大而无法独立完成的方式进行创新时，就会发生组织间协作。在公司选择与某些人合作的原因中，地位起着重要作用。消费者社区越来越多地利用在线平台对生产商和产品进行评级，创造了一种衡量公司和产品估值和地位的替代指标。这个项目将通过确定和解释消费者评级在关于协作的战略决策中的作用来汇集关于组织间协作和估值的文献。该项目的数据将来自对加州、科罗拉多州和伊利诺伊州三个州2009至2016年间小型制造业的混合方法研究。选择这些州是为了提供可靠的合作样本。这一时间框架反映了该行业在全国范围内的指数增长。量化数据将来自两个评级网站，它们将提供合作的证据，以及消费者对合作产生的产品的评价。对这些数据的分析将提供该行业的动态网络模型以及企业地位的具体衡量标准。这些数据将由从样本中的组织成员收集的主要数据补充。调查人员还将对关键线人进行随机抽样面谈，以确定合作者选择背后的地位和动机的不可测量的特征。

This project will explain the role that consumer ratings play in how small-scale manufacturing firms choose partners with whom to collaborate and how resultant patterns of collaboration shape an industry over time. Collaboration is an important way for firms to learn from one another and innovate. At the same time, consumers signal approval of innovations through ratings platforms, such as those found on online retail websites. The craft brewing industry is a strategic research site for examining such industry-consumer dynamics in the context of small-scale manufacturing. Interorganizational collaboration is common within this industry. This industry also has a community of consumers who actively rate products on public websites. Like organizations in many industries, small-scale manufactures vary in their strategies for collaborating: some collaborate with many other firms; others with the same firm repeatedly; still others collaborate once and never again. Over time, patterns of interorganizational collaboration structure industries. This project will construct and analyze a dataset from two product rating sites to model the network of collaborations among manufactures in three states in the U.S. Combined with interviews with a random sample of organizational members from the dataset, analyses will show how consumer ratings influence decisions to collaborate among small-scale manufactures and ultimately shape the industry. Findings will bring together and contribute to the literatures on valuation in economic sociology and collaboration in organization science as well as inform managers? strategic decisions regarding collaborations and their prospective outcomes. This project identifies and explains a key mechanism by which industries are structured: the recursive relationship between consumer ratings and interorganizational collaborations. Interorganizational collaboration takes place when organizations cooperate in a non-hierarchical relationship to share knowledge and resources and to innovate in ways that would be too risky to do independently. Status plays an important role in why firms choose certain others with whom to collaborate. Communities of consumers increasingly take advantage of online platforms to rate producers and products, creating an alternative measure of firm and product valuation and status. This project will bring together the literatures on interorganizational collaborations and valuation by determining and explaining the role of consumer ratings in strategic decisions about collaboration. Data for this project will come from a mixed methods study of the small manufacturing industry in three states: California, Colorado, and Illinois between 2009 and 2016. These states have been selected to provide robust samples of collaborations. The time frame reflects the exponential growth in the industry nationally. Quantitative data will come from two rating websites, which will provide evidence of collaborations along with consumer valuations of the products resulting from collaborations. Analyses of this data will provide dynamic network models of the industry as well as concrete measures of status among firms. This data will be supplemented with primary data collected from organizational members in the sample. Investigators will also conduct key informant interviews with a random sample to identify non-measurable features of status and motivations behind choices of collaborators.