Network Centralization and Collective Adaptability to a Shifting Environment

2023 | Organization Science | Citations: 1

Authors: Bernstein, Ethan S.; Shore, Jesse C.; Jang, Alice J.

Abstract: We study the connection between communication network structure and an organizat ... Expand

Abstract: We study the connection between communication network structure and an organization’s collective adaptability to a shifting environment. Research has shown that network centralization—the degree to which communication flows disproportionately through one or more members of the organization rather than being more equally distributed—interferes with collective problem-solving by obstructing the integration of existing ideas, information, and solutions in the network. We hypothesize that the mechanisms responsible for that poor integration of ideas, information, and solutions would nevertheless prove beneficial for problems requiring adaptation to a shifting environment. We conducted a 1,620-subject randomized online laboratory experiment, testing the effect of seven network structures on problem-solving success. To simulate a shifting environment, we designed a murder mystery task and manipulated when each piece of information could be found: early information encouraged an inferior consensus, requiring a collective shift of solution after more information emerged. We find that when the communication network within an organization is more centralized, it achieves the benefits of connectivity (spread of novel better solutions) without the costs (getting stuck on an existing inferior solution). We also find, however, that these benefits of centralization only materialize in networks with two-way flow of information and not when information only flows from the center of the network outward (as can occur in hierarchical structures or digitally mediated communication). We draw on these findings to reconceptualize theory on the impact of centralization—and how it affects conformity pressure (lock-in) and awareness of diverse ideas (learning)—on collective problem-solving that demands adaptation.History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.Funding: The authors acknowledge financial support from the Division of Research at the Harvard Business School for making this research possible. Collapse

Topics: social influence problem solving internet technology social network debugging

Methods: experiment machine learning simulation Poisson regression logistic regression

Theories: organizational theory

Valuing Flexibility in Low Carbon Electricity Portfolios

2023 | HICSS | Citations: 0

Authors: Blumsack, S.

Abstract: With the increased focus on responding to climate change by accelerating a tran ... Expand

Abstract: With the increased focus on responding to climate change by accelerating a transition to a low carbon energy system, differing views remain on the combination of energy technologies that will best achieve this goal. Identifying technological pathways is complicated by wide uncertainties in economic and technological factors. Analyses that neglect these uncertainties can produce pathways for a low carbon energy future that are highly granular and specific, but which are based on a particular assumption about future conditions and imply a need to make specific technology commitments over a long period of time. We frame the energy transition problem as the identification of one near-term investment strategy that is flexible across a wide range of possible future costs, followed by many alternative subsequent investment plans, each of which responds to realized future costs to achieve an aggressive emissions reduction target. Using an example of planning a low carbon power system under uncertainty, we demonstrate the option value of not ruling out some energy technologies in the near-term. Collapse

Topics: energy information system IT investment technological change

Methods: support vector machine case study machine learning stochastic model descriptive statistic

Dynamics of Human-AI Delegation in Organizational Routines

2023 | International Conference on Information Systems | Citations: 0

Authors: Grisold, Thomas; Schneider, Johannes

Abstract: Human-AI delegation occurs when a human delegates work to an autonomous AI-base ... Expand

Abstract: Human-AI delegation occurs when a human delegates work to an autonomous AI-based system. We report on a simulation study to examine how human-AI delegation dynamically changes in an organizational routine as it is enacted over and over again. We build on findings from previous research and examine the interaction of various human and AI-related factors. We compute the resulting dynamics in terms of complexity representing the degree of uncertainty as to whether delegation takes place. We find that online and offline learning capabilities interact with human willingness in various ways which leads to different, even non-linear changes in the dynamics of human-AI delegation over time. Our study yields implications for research on human-AI delegation, routine dynamics and business process management. We point to a number of practical implications and avenues for future research. Collapse

Topics: artificial intelligence online learning system business process management accounting task productivity

Methods: simulation experiment machine learning stochastic model descriptive statistic

The Currency of Wiki Articles – A Language Model-based Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: Heinrich, Bernd; Huber, Maximilian; Krapf, Thomas; Schiller, Alexander

Abstract: Wikis are ubiquitous in organisational and private use and provide a wealth of ... Expand

Abstract: Wikis are ubiquitous in organisational and private use and provide a wealth of textual data. Maintaining the currency of this textual data is important and difficult, requiring large manual efforts. Previous approaches from literature provide valuable contributions for assessing the currency of structured data or whole wiki articles but are unsuitable for textual wiki data like single sentences. Thus, we propose a novel approach supporting the assessment and improvement of the currency of textual wiki data in an automated manner. Grounded on a theoretical model, our approach makes use of data retrieved from recently published news articles and a language model to determine the currency of fact-based wiki sentences and suggest possible updates. Our evaluation conducted on 543 sentences from six wiki domains shows that the approach yields promising results with accuracies over 80% and thus is well-suited to support assessment and improvement of the currency of textual wiki data. Collapse

Topics: wiki Wikipedia application programming interface implicit knowledge document management system

Methods: language model BERT literature sample stochastic model

Maximizing Online Revisiting and Purchasing: A Clickstream-Based Approach to Enhancing Customer Lifetime Value

2023 | Journal of Management Information Systems | Citations: 0

Authors: Jabr, Wael; Ghoshal, Abhijeet; Cheng, Yichen; Pavlou, Paul

Abstract: Online retailers are increasingly focused on maintaining a long-term relationshi ... Expand

Abstract: Online retailers are increasingly focused on maintaining a long-term relationship with customers, encouraging repeat visits rather than single-time purchases to increase customer lifetime value. To help retailers maximize the probabilities of customers' revisiting and purchasing, we develop a two-stage model to better characterize and predict these two fundamental customer activities. In the first stage, we characterize the propensity of a customer revisiting the retailer's website. In the second stage, we develop a stochastic model that predicts revisits while also incorporating individual customer heterogeneity in exerted search effort during repeated visits. This heterogeneity is based on individual customer preferences in the choice of consideration sets, product information, pricing, and the search environment. Using customer level clickstream data, we show that our approach is not only better at predicting repeat customer visits, compared to existing methods, but also explainable and managerially interpretable. Most importantly, using computationally efficient simulation-based prescriptive analytics, we leverage our modeling approach to propose practical intervention strategies that maximize the joint likelihoods of customers revisiting and purchasing at the individual customer level. Collapse

Topics: website price management mobile system accounting logistics management

Methods: simulation logistic regression proportional hazards model cluster analysis machine learning

Effects of Social Information on Risk Taking and Performance: Understanding Others’ Decisions vs. Comparing Oneself with Others in Short-Term Performance

2023 | Organization Science | Citations: 0

Authors: Pittnauer, Sabine; Hohnisch, Martin; Ostermaier, Andreas; Pfingsten, Andreas

Abstract: When a problem leaves decision makers uncertain as to how to approach it, observ ... Expand

Abstract: When a problem leaves decision makers uncertain as to how to approach it, observing others’ decisions can improve one’s own decisions by promoting more accurate judgments and a better insight into the problem. However, observing others’ decisions may also activate motives that prevent this potential from being realized, for instance, ego concerns that prompt excessive risk taking. Our experimental study investigates how two features of the social environment influence the effect of observing others’ decisions on individual risk taking and performance. We manipulated (1) the psychological distance to others whose decisions could be observed (and thereby the tendency to seek self-enhancing social comparison) and (2) the opportunity for interaction (and thereby for a cumulative effect of any such tendency on decisions over time and for an effect on social information itself). Because the two features covary in real-world settings, we designed two treatments corresponding to the two natural combinations. Both treatments provided participants with two other participants’ period decisions in a multiperiod problem under uncertainty. No new objective information about the problem could be inferred from these decisions. We predicted that participants who observed the decisions of distant others (who had solved the same problem earlier) would perform better than participants in a control sample without any information about others’ decisions and that participants who observed the decisions of proximal others (with whom interaction could arise) would take more risk and perform worse than those who observed distant others’ decisions. The data corroborate our predictions. We discuss implications for organizational learning.History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory.Funding: The research was made possible by funding from the Nordrhein-Westfälische Akademie der Wissenschaften und der Künste through the Akademienprogramm of the German Academies of Sciences and Humanities (2006–2015). Hohnisch and Pittnauer gratefully acknowledge funding from the Stiftung der Freunde und Förderer der NRW-Akademie der Wissenschaften und der Künste (2016).Supplemental Material: The supplemental material is available at https://doi.org/10.1287/orsc.2021.1507. Collapse

Topics: decision making organization learning problem solving

Methods: experiment hierarchical linear modeling descriptive statistic statistical power analysis cluster analysis

Theories: construal level theory

Are Deep Learning-Generated Social Media Profiles Indistinguishable from Real Profiles?

2023 | HICSS | Citations: 0

Authors: Rossi, S.; Kwon, Y.; Auglend, O.; Mukkamala, R.; Rossi, M.; Thatcher, J.

Abstract: In recent years, deep learning methods have become increasingly capable of gene ... Expand

Abstract: In recent years, deep learning methods have become increasingly capable of generating near photorealistic pictures and humanlike text up to the point that humans can no longer recognize what is realand what is AI-generated. Concerningly, there is evidence that some of these methods have already been adopted to produce fake social media profiles and content. We hypothesize that these advances have made detecting generated fake social media content in the feed extremely difficult, if not impossible, for the average user of social media. This paper presents the results of an experiment where 375 participants attempted to label real and generated profiles and posts in a simulated social media feed. The results support our hypothesis and suggest that even fully-generated fake profiles with posts written by an advanced text generator are difficult for humans to identify. Collapse

Topics: social media Twitter social network armed conflict website

Methods: experiment deep learning survey descriptive statistic stochastic model

Bayesian non-parametric method for decision support: Forecasting online product sales

2023 | Decision Support Systems | Citations: 0

Authors: Wu, Ziyue; Chen, Xi; Gao, Zhaoxing

Abstract: Forecasting online product sales is essential for retailers and e-commerce platf ... Expand

Abstract: Forecasting online product sales is essential for retailers and e-commerce platforms, but it can be challenging owing to the complex dynamics and mixed trends in sales data. Popular end-to-end approaches tend to capture spurious correlations in historical data, whereas two-stage approaches that decompose time series and make separate predictions often result in error accumulation. To address these issues, we propose PoissonGP, a novel Bayesian model that employs a non-homogeneous Poisson process with a Gaussian process prior for sales pre­ diction. PoissonGP can capture complex patterns in data with multiple trends and manage distribution shifts caused by changes in long-run sales. Additionally, it incorporates forecast uncertainty and provides interpret­ ability for building efficient and visual decision support systems. Experimental results on several synthetic and empirical datasets suggest that PoissonGP outperforms existing approaches, making it a promising tool for ecommerce platforms. Collapse

Topics: marketing management digital platform user behavior online advertising electronic commerce

Methods: longitudinal research time series analysis experiment Bayesian analysis simulation

Responding to Online Reviews in Competitive Markets: A Controlled Diffusion Approach

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Yang, Mingwen; Zheng, Zhiqiang (Eric); Mookerjee, Vijay; Chen, Hongyu

Abstract: We study how firms respond to online customer reviews in a competitive market w ... Expand

Abstract: We study how firms respond to online customer reviews in a competitive market where they jostle with one another for sales based on online ratings. The focus of this paper is on how firms can optimally manage their ratings through management response and how review ratings affect the sales and profits of competing firms. We develop a controlled diffusion process to model the coevolution of sales and ratings as a function of the response strategy chosen to maximize profit over time. Our model considers a variety of factors, such as profit margin and customer rating sensitivity, that influence a firm's effort to manage ratings and subsequently its sales and profits. More response effort needs to be exerted to manage ratings when either the profit margin of a tour is very high or customers are very sensitive to ratings. We estimate our model using data on Ctrip's tours that include each tour's sales, reviews, prices, and tour features. We find that consumers anchor their beliefs in the mean market rating and that their purchase decisions depend on the tour's rating relative to this anchor. Thus, relative, rather than absolute, ratings matter. Our study informs firms on how competition and other primitives impact their efforts to manage ratings and hence profit. Our methodology allowed us to conduct "what-if" analyses, for example, to study what would happen to the review ratings, sales, and profits of a tour if a firm adopted a different response strategy. We were also able to provide turnaround strategies for struggling tours, i.e., factors that a loss-making tour should change if it wishes to make a positive profit. Ultimately, we conducted a competitive analysis that allowed us to modify certain parameters that affect the intensity of competition and hence the sales and the profits of competing tours. Finally, we demonstrate the flexibility of the model by extending it to incorporate multiple state variables that might affect the response strategy. Collapse

Topics: application programming interface online learning system Twitter innovation management service quality

Methods: longitudinal research time series analysis generalized autoregressive conditional heteroskedasticity model natural language processing autoregressive moving average model

Theories: co-evolution theory

Architectural Search and Innovation

2022 | Organization Science | Citations: 0

Authors: Albert, Daniel; Siggelkow, Nicolaj

Abstract: Product innovation can result from the novel design and combination of product c ... Expand

Abstract: Product innovation can result from the novel design and combination of product components as well as from changing the underlying architecture: that is, the way components interact with each other. Even though previous studies have shown that architectural change can constitute a powerful source of innovation, little insight exists on how organizations should engage in architectural search itself. In this paper, using computer simulation, we explore underlying mechanisms of architectural search. We find that contrary to search for component combinations, architectural search provides greater performance improvements the narrower the search scope, regardless of product complexity. Moreover, our theory and findings suggest a more differentiated typology of architectural innovation. Although narrow architectural search often leads to pure architectural innovations that do not require substantial component changes, broader architectural search often leads to composite architectural innovation (i.e., architectural innovations that typically render existing component designs suboptimal but allow for new high-performing component combinations to arise). Lastly, although narrow architectural search outperforms broad architectural search in the long run, in the short run broad architectural search can have performance advantages. Collapse

Topics: innovation management new product development organizational productivity

Methods: simulation design artifact experiment synthesis stochastic model