The growth of digital platforms has led to the proliferation of Online Communities, providing individuals with opportunities to seek help and share knowledge. A key challenge of help-related ...platforms that address technical questions (i.e., utilitarian, rather than opinion or supportive) is to ensure the contributions address seekers' specific information needs. Despite growing academic interest in such platforms, research has mainly focused on factors that influence the quantity of contributions, ignoring whether these contributions effectively helped the seekers. To fill this research gap, this study draws upon theories of self-determination and motivation crowding to examine contributing behaviors that result in successful helping. By analyzing a rich dataset collected from an online Q&A platform, we find that gains in a help provider's past rewards positively influence the success of contribution. Further, while previous studies suggest that external rewards result in a high quantity of contribution, our findings show that an inflated frequency of contribution leads to a crowding-out effect. Specifically, the contribution frequency has a curvilinear relationship with the success of the contribution. Taken together, these findings demonstrate there is a need to revisit the gamification mechanism on help-related platforms to ensure the success of knowledge contribution. This is crucial for the sustainability of these platforms as low-quality answers can lead users to mistrust and eventually leave the platform.
Algorithmic decision-making has become ubiquitous in our societal and economic lives. With more and more decisions being delegated to algorithms, we have also encountered increasing evidence of ...ethical issues with respect to biases and lack of fairness pertaining to algorithmic decision-making outcomes. Such outcomes may lead to detrimental consequences to minority groups in terms of gender, ethnicity, and race. As a response, recent research has shifted from design of algorithms that merely pursue purely optimal outcomes with respect to a fixed objective function into ones that also ensure additional fairness properties. In this study, we aim to provide a broad and accessible overview of the recent research endeavor aimed at introducing fairness into algorithms used in automated decision-making in three principle domains, namely, multi-winner voting, machine learning, and recommender systems. Even though these domains have developed separately from each other, they share commonality with respect to decision-making as an application, which requires evaluation of a given set of alternatives that needs to be ranked with respect to a clearly defined objective function. More specifically, these relate to tasks such as (1) collectively selecting a fixed number of winner (or potentially high valued) alternatives from a given initial set of alternatives; (2) clustering a given set of alternatives into disjoint groups based on various similarity measures; or (3) finding a consensus ranking of entire or a subset of given alternatives. To this end, we illustrate a multitude of fairness properties studied in these three streams of literature, discuss their commonalities and interrelationships, synthesize what we know so far, and provide a useful perspective for future research.
•Decision-making in organizations can be augmented with Deep Learning algorithms.•Deep Learning algorithms find application in i) targeting ii) monitoring and iii) scheduling within ...organizations.•Deep Learning also feature various economic and organizational challenges.
The current expansion of theory and research on artificial intelligence in management and organization studies has revitalized the theory and research on decision-making in organizations. In particular, recent advances in deep learning (DL) algorithms promise benefits for decision-making within organizations, such as assisting employees with information processing, thereby augment their analytical capabilities and perhaps help their transition to more creative work. We conceptualize the decision-making process in organizations augmented with DL algorithm outcomes (such as predictions or robust patterns from unstructured data) as deep learning–augmented decision-making (DLADM). We contribute to the understanding and application of DL for decision-making in organizations by (a) providing an accessible tutorial on DL algorithms and (b) illustrating DLADM with two case studies drawing on image recognition and sentiment analysis tasks performed on datasets from Zalando, a European e-commerce firm, and Rotten Tomatoes, a review aggregation website for movies, respectively. Finally, promises and challenges of DLADM as well as recommendations for managers in attending to these challenges are also discussed.
Across many fields of social science, machine learning (ML) algorithms are rapidly advancing research as tools to support traditional hypothesis testing research (e.g., through data reduction and ...automation of data coding or for improving matching on observable features of a phenomenon or constructing instrumental variables). In this paper, we argue that researchers are yet to recognize the value of ML techniques for theory building from data. This may be in part because of scholars’ inherent distaste for
predictions without explanations
that ML algorithms are known to produce. However, precisely because of this property, we argue that ML techniques can be very useful in theory construction during a key step of inductive theorizing—pattern detection. ML can facilitate
algorithm supported induction
, yielding conclusions about patterns in data that are likely to be robustly replicable by other analysts and in other samples from the same population. These patterns can then be used as inputs to abductive reasoning for building or developing theories that explain them. We propose that algorithm-supported induction is valuable for researchers interested in using quantitative data to both develop and test theories in a transparent and reproducible manner, and we illustrate our arguments using simulations.
How does organizational decision-making change with the advent of artificial intelligence (AI)-based decision-making algorithms? This article identifies the idiosyncrasies of human and AI-based ...decision making along five key contingency factors: specificity of the decision search space, interpretability of the decision-making process and outcome, size of the alternative set, decision-making speed, and replicability. Based on a comparison of human and AI-based decision making along these dimensions, the article builds a novel framework outlining how both modes of decision making may be combined to optimally benefit the quality of organizational decision making. The framework presents three structural categories in which decisions of organizational members can be combined with AI-based decisions: full human to AI delegation; hybrid—human-to-AI and AI-to-human—sequential decision making; and aggregated human–AI decision making.
An “ensemble” approach to decision-making involves aggregating the results from different decision makers solving the same problem (i.e., a division of labor without specialization). We draw on the ...literatures on machine learning-based Artificial Intelligence (AI) as well as on human decision-making to propose conditions under which human-AI ensembles can be useful. We argue that human and AI-based algorithmic decision-making can be usefully ensembled even when neither has a clear advantage over the other in terms of predictive accuracy, and even if neither alone can attain satisfactory accuracy in absolute terms. Many managerial decisions have these attributes, and collaboration between humans and AI is usually ruled out in such contexts because the conditions for specialization are not met. However, we propose that human-AI collaboration through ensembling is still a possibility under the conditions we identify.
Artificial intelligence (AI) can support managers by effectively delegating management decisions to AI. There are, however, many organizational and technical hurdles that need to be overcome, and we ...offer a first step on this journey by unpacking the core factors that may hinder or foster effective decision delegation to AI.
We study the complexity of the constructive/destructive bribery problem with distance restrictions. In the constructive/destructive bribery problem, we are given an election and a distinguished ...candidate p, and are asked whether we can make p a winner/loser by bribing a limited number of voters to recast their votes. In the constructive/destructive bribery problem with distance restrictions, we require that the votes recast by the bribed voters are close to their original votes. In particular, we measure the closeness of two votes by the minimum number of swaps of candidates needed to transform one vote into the other. We consider both the case where swaps only take place between consecutive candidates, and the case where swaps can take place between any arbitrary candidates. We achieve a wide range of complexity results for the voting correspondences Borda, Condorcet, Copelandα for every rational number 0≤α≤1, and Maximin.
Non-fungible tokens (NFTs) are digital assets based on blockchain technology that are increasingly being used for various applications in organizations. Given NFTs’ unique technological features, we ...posit that traditional, centralized organizations can adopt them to introduce novel solutions to the fundamental problems of organizing, namely, division of labor and integration of efforts. We examine the prospects and promises of NFT-enabled organization design and suggest how organizations can navigate its potential hurdles. We discuss critical boundary conditions for the deployment of NFTs in organization design and conclude by articulating how our
Point of View
article contributes to scholarship on blockchain technology and decentralized autonomous organizations (DAOs).
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