Diffusion of knowledge in social media networks: effects of reputation mechanisms and distribution of knowledge roles

Agent-based model: A method worthy of promotion in Library and Information Science

Agent-based model (ABM) is a branch of artificial intelligence. Its specialty is to construct a complex macro-system model by describing the perception, decision, learning and action of micro-agents. This method is widely used in many fields from natural science to social science. We discuss ABM by collecting relevant academic papers which apply to the field of Library and Information Science (LIS). This article systematically reviews how ABM is applied to the LIS field and argues that ABM can provide an exploratory tool with quantifiability, repeatability, interpretability, contingency, adaptability and other types of advantages. Finally, it is pointed out that this method is a research tool worthy of careful exploration.

Investigating dissemination of scientific information on Twitter: A study of topic networks in opioid publications

While previous research has mostly focused on the “number of mentions” of scientific research on social media, the current study applies “topic networks” to measure public attention to scientific research on Twitter. Topic networks are the networks of co-occurring author keywords in scholarly publications and networks of co-occurring hashtags in the tweets mentioning those publications. We investigate which topics in opioid scholarly publications have received public attention on Twitter. Additionally, we investigate whether the topic networks generated from the publications tweeted by all accounts (bot and nonbot accounts) differ from those generated by nonbot accounts. Our analysis is based on a set of opioid publications from 2011 to 2019 and the tweets associated with them. Results indicated that Twitter users have mostly used generic terms to discuss opioid publications, such as “Pain,” “Addiction,” “Analgesics,” “Abuse,” “Overdose,” and “Disorders.” A considerable amount of tweets is produced by accounts that were identified as automated social media accounts, known as bots. There was a substantial overlap between the topic networks based on the tweets by all accounts (bot and nonbot accounts). This result indicates that it might not be necessary to exclude bot accounts for generating topic networks as they have a negligible impact on the results. This study provided some preliminary evidence that scholarly publications have a network agenda-setting effect on Twitter.

A Confirmation Bias View on Social Media Induced Polarisation During Covid-19

Social media has played a pivotal role in polarising views on politics, climate change, and more recently, the Covid-19 pandemic. Social media induced polarisation (SMIP) poses serious challenges to society as it could enable 'digital wildfires' that can wreak havoc worldwide. While the effects of SMIP have been extensively studied, there is limited understanding of the interplay between two key components of this phenomenon: confirmation bias (reinforcing one's attitudes and beliefs) and echo chambers (i.e., hear their own voice). This paper addresses this knowledge deficit by exploring how manifestations of confirmation bias contributed to the development of 'echo chambers' at the height of the Covid-19 pandemic. Thematic analysis of data collected from 35 participants involved in supply chain information processing forms the basis of a conceptual model of SMIP and four key cross-cutting propositions emerging from the data that have implications for research and practice.

Influence of intrinsic motivations on the continuity of scientific knowledge contribution to online knowledge-sharing platforms

Scientific knowledge contribution to online knowledge-sharing platforms has long been regarded as instrumental behavior based on utilitarian considerations. Employing cognitive evaluation theory, this study examines scientific expert users' behavioral metrics to understand the factors responsible for users continuing to contribute their scientific knowledge for an extended period or a very short span. We found that expert users' intrinsic motivations, which has received little attention in recent studies, constitute an important indicator of sustained online scientific knowledge contribution. Furthermore, although social rewards fail to predict the continuity of scientific knowledge contribution, they prolong the duration of knowledge contribution by enhancing the intrinsic motivations of expert users. In conclusion, a self-reinforcement mechanism underlies the relationship of intrinsic motivation with social rewards, which governs continuous online scientific knowledge contribution behavior.

An Innovative Management Perspective for Organizations through a Reputation Intelligence Management Model

Banking companies aiming to maintain their sustainability in financial markets need to develop an integrated management based on the most important intangibles assets of relational capital. Decision- makers need to analyze and understand a huge volume of opinions continuously generated in digital ecosystems about emotions and feelings that their stakeholders associate with the performance and communication of the brand. Current tools of management fail to consider transversal and holistic models, which study the frequency and value of existing relationships between the relational capital and intangible assets. In this research, an innovative management model based on reputation intelligence is proposed. This model incorporates methodology from business intelligence models, through OLAP and data mining techniques, to analyses the complex relationships among intangible assets experience, emotion and attitude. The proposed model was applied to companies in the banking sector and the results obtained permit a conclusion about the kinds of relationships for these intangibles in each bank.

Impact of Open Innovation Communities on Enterprise Innovation Performance: A System Dynamics Perspective

Open innovation communities (OICs) can help enterprises make full use of external knowledge resources from users, but problems such as low user participation and low conversion rate of creative ideas impact the efficiency of OICs. Most studies on this topic employ qualitative or empirical methods from a static perspective, but ignore the effect of interaction between enterprises and users as well as the cumulative effect of time. A discussion on the dynamic evolution process of open innovation is lacking. Based on a review of the literature on OICs, innovation performance, and system dynamics, this study proposes a conceptual model of innovation performance impact, which comprises the knowledge management, governance mechanism, and user behavior subsystems. Xiaomi’s OIC in China was selected as the research object, and relevant data were collected through a web spider. According to the system dynamics modeling method, a causal relationship analysis was carried out on the three aforementioned interrelated subsystems. Then, a stock flow chart with 32 variables was constructed to determine the initial values and calculation equations for each variable. Finally, the model was constructed and verified using Vensim PLE software. The simulation results were as follows. (1) The number of product releases in the Xiaomi OIC was positively correlated with the number of posts, comments, and views. Compared with user interaction behavior (i.e., commenting and viewing), the impact of user innovation behavior (i.e., posting) on enterprise innovation performance (i.e., number of patents) is clearer. Specifically, regarding interaction behavior, the impact of the users’ commenting behavior on innovation performance (i.e., number of product releases) was relatively clearer than that of their viewing behavior. (2) Governance mechanism (i.e., R&D investment and management expense), which comprises technical and organizational mechanisms, positively affected the innovation performance of enterprises. Compared with the organizational mechanism (i.e., management expense), the impact of the technical mechanism (i.e., R&D investment) on the innovation performance was clearer. (3) Governance mechanism helped to increase the number of users in the OIC, and, in turn, affected the user innovation and interaction behavior. (4) The technical mechanism positively affected knowledge application capability, which, in turn, had a positive impact on the innovation performance of enterprises. Based on these findings, management strategies are proposed for the establishment and development of OICs.

Predicting changing pattern: building model for consumer decision making in digital market

Purpose

Consumers have the multiple options to choose their products and services, which have a significant impact on the pattern of consumer decision making in digital market and further increases the challenges for the service providers to predict their buying pattern. In this sense, the purpose of this paper is to propose a structural hierarchy model for analyzing the changing pattern of consumer decision making in digital market by taking an Indian context.

Design/methodology/approach

To accomplish the objectives, the research is conducted in two phases. An extensive literature review is performed in the first phase to list the factors related to the changing pattern of consumer decision making in digital market and then fuzzy Delphi method is applied to finalize the factors. In the second phase, fuzzy analytic hierarchy process (AHP) is employed to find the priority weights of finalized factors. The fuzzy set theory allows capturing the vagueness in the data.

Findings

The findings obtained in this study shows that consumers are much conscious about innovative and trendy products as well as brand and quality; therefore, the service providers must think about these two most important factors so that they can able to retain their consumer in their online portal.

Practical implications

The analysis shows that “innovative and trendy” is the first priority factor for the consumers followed by “brand and quality” and “fulfilment and time energy.” The proposed model can help the marketers and service providers in predicting customers’ preferences and their changing pattern efficiently under vague surroundings. The outcomes of this research work not only help the service provider to update their products and services according to consumers’ needs but can also help them to increase profit and minimize their risk.

Originality/value

This work contributes to consumer research literature focusing on problem evaluation in the context of changing pattern of consumer decision making in digital era.