Researchers have increasingly turned to crowdfunding platforms to gain insights into entrepreneurial activity and dynamics. While previous studies have explored various factors influencing ...crowdfunding success, such as technology, communication, and marketing strategies, the role of visual elements that can be automatically extracted from images has received less attention. This is surprising, considering that crowdfunding platforms emphasize the importance of attention‐grabbing and high‐resolution images, and previous research has shown that image characteristics can significantly impact product evaluations. Indeed, a comprehensive review of empirical articles (n = 202) utilized Kickstarter data, focusing on the incorporation of visual information in their analyses. Our findings reveal that only 29.70% controlled for the number of images, and less than 12% considered any image details. In this manuscript, we contribute to the existing literature by emphasizing the significance of visual characteristics as essential variables in empirical investigations of crowdfunding success. We review the literature on image processing and its relevance to the business domain, highlighting two types of visual variables: visual counts (number of pictures and number of videos) and image details. Building upon previous work that discussed the role of color, composition, and figure–ground relationships, we introduce visual scene elements that have not yet been explored in crowdfunding, including the number of faces, the number of concepts depicted, and the ease of identifying those concepts. To demonstrate the predictive value of visual counts and image details, we analyze Kickstarter data using flexible machine learning models (Lasso, Ridge, Bayesian additive regression trees, and eXtreme Gradient Boosting). Our results highlight that visual count features are two of the top three predictors of success and highlight the ease at which researchers can incorporate some information about visual information. Our results also show that simple image detail features such as color matter a lot, and our proposed measures of visual scene elements can also be useful. By supplementing our article with R and Python codes that help authors extract image details (https://osf.io/ujnzp/), we hope to stimulate scholars in various disciplines to consider visual information data in their empirical research and enhance the impact of visual cues on crowdfunding success.
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BFBNIB, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Research can be characterized in terms of three domains: (1) the substantive (the real-world problem of focus in the research); (2) the conceptual (the theoretical representation of some aspect of ...reality); and (3) the methodological (the approach taken to investigate a real-world problem or test theory).
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IZUM, KILJ, NUK, OILJ, PILJ, SAZU, UKNU, UL, UM, UPUK
The card-sorting task is a flexible research tool that is widely used across many of the subfields of psychology. Yet this same great flexibility requires researchers to make several (seemingly ...arbitrary) decisions in their designs, such as fixing a sufficient number of objects to sort, setting task requirements, and creating task instructions for participants. In the present research, we provide a systematic empirical investigation of the consequences of typical researcher design choices while administering sorting tasks. Specifically, we studied the effects of seven sorting task design factors by collecting data from over 1,000 online participants assigned to one of 36 sorting tasks, as part of a fractional factorial experimental design. Analyses show the effects of the various researcher decisions on the probability that participants would quit the task, the amount of time spent on the task, the number of piles made, and posttask measures such as satisfaction and depletion. Research design recommendations are provided.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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•Franchisors strategically modify contracts for new franchisees (contractual discrimination).•Positive discrimination reduces existing franchisees perceptions of equity.•Reduced ...perception of equity increases freeriding and hurts performance.•Greater number of existing franchisees worsens franchisor performance.•Negative discrimination reduces freeriding, increases performance only for more franchisees.
Franchisors often modify the contract terms offered to prospective (new) franchisees – to incentivize growth in the number of franchisees, to access capital, or to improve their financial performance. We argue that changes in contract terms offered to new franchisees (contractual discrimination across franchisees) can alter existing franchisees’ perceived equity in their relationship with the franchisor, and affect their freeriding. Specifically, we hypothesize, and show, that positive (negative) discrimination towards new franchisees reduces (maintains) existing franchisees’ perceived equity in their relationship with the franchisor, motivating existing franchisees to increase (eschew) freeriding – with impact on franchisors’ performance. To do so, we first take advantage of an exogenous event (the great recession of 2007-09) to study how 120 restaurant franchisors changed their contract terms to new franchisees and how that affected their post-recession net income (Study 1). We show that changes in contracts for new franchisees impact franchisors’ post-crisis performance, as a function of the number of existing franchisees. Second, with two experiments (Studies 2 and 3) with entrepreneurs and franchisees, we document that the observed changes in performance occur because contractual discrimination affects existing franchisees’ perceived equity and their intentions to free-ride. Thus, we contribute to the literature on equity in franchising relationships, on contract evolution in franchising, and its impact on financial performance.
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CEKLJ, GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This research challenges the entrenched belief that financial vulnerability affects only low-income consumers. Instead, most consumers across the socioeconomic spectrum experience varying degrees of ...financial vulnerability at different points during their lives, whether sporadically or chronically; vulnerability is dynamic and heterogeneous. The authors propose a novel, theory-driven definition of consumer financial vulnerability (CFV) as the risk of incurring future harm, given the consumer's current access to various financial resources. A new conceptual framework decouples “vulnerability” from “harm” to distinguish the state of CFV, its determinants (access to various interdependent financial resources), and the constructs it foreshadows (multiple interconnected forms of realized harm). Five research propositions follow: (1) financial resource volatility plays a vital role in CFV, (2) recovering from harm requires more financial resources than preventing harm, (3) a multiperiod lens is needed to assess CFV accurately, (4) greater financial resource access can increase CFV, and (5) generalized financial literacy is not a panacea for mitigating CFV. The propositions and their implications for marketing strategy, public policy, and consumer well-being offer a rich research agenda. The authors propose a measure of CFV—the probability that financial resources are insufficient to meet or exceed a harm threshold—for future empirical investigations.
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Clustering algorithms help identify homogeneous subgroups from data. In some cases, additional information about the relationship among some subsets of the data exists. When using a semi-supervised ...clustering algorithm, an expert may provide additional information to constrain the solution based on that knowledge and, in doing so, guide the algorithm to a more useful and meaningful solution. Such additional information often takes the form of a cannot-link constraint (i.e., two data points cannot be part of the same cluster) or a must-link constraint (i.e., two data points must be part of the same cluster). A key challenge for users of such constraints in semi-supervised learning algorithms, however, is that the addition of inaccurate or conflicting constraints can decrease accuracy and little is known about how to detect whether expert-imposed constraints are likely incorrect. In the present work, we introduce a method to score each must-link and cannot-link pairwise constraint as likely incorrect. Using synthetic experimental examples and real data, we show that the resulting impact score can successfully identify individual constraints that should be removed or revised.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
8.
Locational Choices Blanchard, Simon J.; Dyachenko, Tatiana L.; Kettle, Keri L.
Journal of marketing research,
10/2020, Volume:
57, Issue:
5
Journal Article
Peer reviewed
This article proposes a measurement approach to determine how consumers prefer to locate themselves in proximity to others during consumption experiences, such as when they purchase reserved seating ...tickets to a performance. Applied to data from locational choice experiments that simulate reserved seating assortments, administered to more than 2,000 participants, this approach reveals the importance of modeling proximity to others when studying locational choices. It also emphasizes the degree to which consumers are heterogeneous in their preferences for proximity to both focal elements (e.g., stage, screen, aisles) and other consumers. Therefore, event operators should collect data beyond purchase ticket logs and also include consumers who did not purchase. Furthermore, this study illustrates how managers can use fitted, individual-level parameters and an optimization model to make more effective seat-level availability decisions. In addition to these recommendations for managers of reserved seating venues, this article offers novel contributions to research related to advance selling, spatial models, and personal space.
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•We present a new model for clustering data from heterogeneous dissimilarity matrices.•The model is robust and insensitive to various types of clustering data.•We propose a VNS that outperforms ...general purpose exact solvers in all tested cases.
Clustering algorithms partition a set of n objects into p groups (called clusters), such that objects assigned to the same groups are homogeneous according to some criteria. To derive these clusters, the data input required is often a single n × n dissimilarity matrix. Yet for many applications, more than one instance of the dissimilarity matrix is available and so to conform to model requirements, it is common practice to aggregate (e.g., sum up, average) the matrices. This aggregation practice results in clustering solutions that mask the true nature of the original data. In this paper we introduce a clustering model which, to handle the heterogeneity, uses all available dissimilarity matrices and identifies for groups of individuals clustering objects in a similar way. The model is a nonconvex problem and difficult to solve exactly, and we thus introduce a Variable Neighborhood Search heuristic to provide solutions efficiently. Computational experiments and an empirical application to perception of chocolate candy show that the heuristic algorithm is efficient and that the proposed model is suited for recovering heterogeneous data. Implications for clustering researchers are discussed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
In a sorting task, consumers receive a set of representational items (e.g., products, brands) and sort them into piles such that the items in each pile "go together." The sorting task is flexible in ...accommodating different instructions and has been used for decades in exploratory marketing research in brand positioning and categorization. However, no general analytic procedures yet exist for analyzing sorting task data without performing arbitrary transformations to the data that influence the results and insights obtained. This manuscript introduces a flexible framework for analyzing sorting task data, as well as a new optimization approach to identify summary piles, which provide an easy way to explore associations consumers make among a set of items. Using two Monte Carlo simulations and an empirical application of single-serving snacks from a local retailer, the authors demonstrate that the resulting procedure is scalable, can provide additional insights beyond those offered by existing procedures, and requires mere minutes of computational time.
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