https://link.springer.com/content/pdf/10.1007/s12525-022-00537-z.pdf

Artificial intelligence in E-Commerce: a bibliometric study and literature review

2-stage modified random forest model for credit risk assessment of P2P network lending to “Three Rurals” borrowers

This paper identifies challenges facing the development of contextual theory in Information Systems research. The IS literature is examined to identify the variation of approaches through which IS research accounts for contextual influences in the formation of IS phenomena. The literature review reveals issues that require methodological and theoretical attention. These concern the generalization of context-specific research findings; the partiality of theory due to trade-offs of scale and detail; the development of sociomaterial perspectives of contextual influences on IS phenomena; and the challenge posed to contextual explanation from ontologies that give primacy to processes of continuous change over existing entities. From the exploration of these issues, comparative research is suggested as a promising approach to generalization; an argument is made for research framing with explicit consideration of scale of the context domain under inquiry to allow for the comparison and complementarity of research findings; alternative theoretical perspectives of context related with theories of technology and theories of action are identified; and directions toward the development of a sociomaterial perspective of context are suggested.

Contextual Explanation: Alternative Approaches and Persistent Challenges

Market segmentation is a core marketing concept that is conceptually simple to define and understand, but inherently a multi-criteria problem that is hard to measure and computationally difficult in many aspects. This paper reviews the development of market segmentation techniques and identifies the computational issues of the applications of market segmentation. A multidimensional unified framework for market segmentation is proposed based on the relationship among segmentation variables, data measures, and the multi-objective optimization techniques implemented. We conduct an empirical comparison of two prominent methods: a concomitant finite mixture model and a multi-objective evolutionary algorithm. The result shows that the proposed framework helps to understand different segmentation models and solutions and to guide the development of new market segmentation solution techniques.

A unified framework for market segmentation and its applications

Work and Employment is a critical area that is undergoing major change influenced by the widespread connectivity and utilisation of the Internet and the rise of digital platforms. Crowdwork is an emerging new way of working that is witnessing exponential growth. It is surrounded by a fixed debate between opposite perspectives on its impact on workers. However, both perspectives adopt a static view that does not pay much attention to crowdworkers’ progress in their job over time. In this study, we seek to advance this debate by adopting a dynamic view of crowdwork to explore the trajectory of workers over time based on their own accounts. Through rich qualitative data and inductive analysis, the study unravels that crowdworkers craft what could be conceptualised as a career development path. It identifies five stages in this career path and workers’ efforts to mould their work demands and job-related resources to create a future for themselves. The discussion shows the fruitful insight that this approach brings to theory and practice. Limitations and future avenues for research are then discussed.

/pdf/digital-platforms-of-work-and-the-crafting-of-career-path-17sleg6ljq.pdf

Digital Platforms of Work and the Crafting of Career Path: The Crowdworkers’ Perspective

Crowdsourcing contests have emerged as an innovative way for firms to solve business problems by acquiring ideas from participants external to the firm As the number of participants on crowdsourcing contest platforms has increased, so has the number of tasks that are open at any time This has made it difficult for solvers to identify tasks in which to participate We present a framework to recommend tasks to solvers who wish to participate in crowdsourcing contests The existence of competition among solvers is an important and unique aspect of this environment, and our framework considers the competition a solver would face in each open task As winning a task depends on performance, we identify a theory of performance and reinforce it with theories from learning, motivation, and tournaments This augmented theory of performance guides us to variables specific to crowdsourcing contests that could impact a solver’s winning probability We use these variables as input into various probability prediction models adapted to our context, and make recommendations based on the probability or the expected payoff of the solver winning an open task We validate our framework using data from a real crowdsourcing platform The recommender system is shown to have the potential of improving the success rates of solvers across all abilities Recommendations have to be made for open tasks and we find that the relative rankings of tasks at similar stages of their time lines remain remarkably consistent when the tasks close Further, we show that deploying such a system should benefit not only the solvers, but also the seekers and the platform itself

/pdf/know-when-to-run-recommendations-in-crowdsourcing-contests-2mxhv3gk4t.pdf

Know When to Run: Recommendations in Crowdsourcing Contests

Previous research in multi-region segmentation has found that the customer segmentation derived based on the customer attributes from one region (i.e., city or country) cannot be directly adopted by another region. As a result, for a firm that operates in multiple regions, a market segmentation method that can integrate data from different regions to obtain a set of generalized segmentation rules can greatly enhance the competitiveness of the company. In this research, we applied self-organizing map (SOM) network, an unsupervised neural networks technique as both a dimension reduction and a clustering tool to market segmentation. A two-stage clustering approach, which first groups similar regions together then finds customer segmentation for each region-group, is proposed. Empirical data from one of the largest credit card issuing banks in China was collected. The data, that includes surveys of customer satisfaction attributes and credit card transaction history, is used to validate the proposed model. The results show that the two-stage clustering approach based on SOM for multi-region segmentation is an effective and efficient method compared to other approaches.

A two-stage clustering approach for multi-region segmentation

Crowdsourcing contests have emerged as an innovative way for firms to solve business problems by acquiring ideas from participants external to the firm. To facilitate such contests a number of crowdsourcing platforms have emerged in recent years. A crowdsourcing platform provides a twosided marketplace with one set of members (seekers) posting tasks, and another set of members (solvers) working on these tasks and submitting solutions. As crowdsourcing platforms attract more seekers and solvers, the number of tasks that are open at any time can become quite large. Consequently, solvers search only a limited number of tasks before deciding which one(s) to participate in, often examining only those tasks that appear on the first couple of pages of the task listings. This kind of search behavior has potentially detrimental implications for all parties involved: (i) solvers typically end up participating in tasks they are less likely to win relative some other tasks, (ii) seekers receive solutions of poorer quality compared to a situation where solvers are able to find tasks that they are more likely to win, and (iii) when seekers are not satisfied with the outcome, they may decide to leave the platform; therefore, the platform could lose revenues in the short term and market share in the long term. To counteract these concerns, platforms can provide recommendations to solvers in order to reduce their search costs for identifying the most preferable tasks. This research proposes a methodology to develop a system that can recommend tasks to solvers who wish to participate in crowdsourcing contests. A unique aspect of this environment is that it involves competition among solvers. The proposed approach explicitly models the competition that a solver would face in each open task. The approach makes recommendations based on the probability of the solver winning an open task. A multinomial logit model has been developed to estimate these winning probabilities. We have validated our approach using data from a real crowdsourcing platform.

Making Task Recommendations in Crowdsourcing Contests

An empirical investigation of sales cheating effect in E-commerce

https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1359&context=icis2016

Investigating Effects of Competing Tasks on Crowdsourcing Contest Platforms

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