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In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.

With so many placement opportunities around the students in their final or prefinal year, they start to feel the strain of the season. The students feel the need to be aware of their position and how to increase their chances of being hired. Hence, a system to guide their career is one of the needs of the day.

The job role prediction system utilizes machine learning techniques such as Naïve Bayes, K-Nearest Neighbor, Support Vector machines (SVM) and Artificial Neural Networks (ANN) to suggest a student’s job role based on their academic performance and course outcomes (CO), out of which ANN performs better. The system uses the Mepco Schlenk Engineering College curriculum, placement and students’ Assessment data sets, in which the CO and syllabus are used to determine the skills that the student has gained from their courses. The necessary skills for a job position are then extracted from the job advertisements. The system compares the student’s skills with the required skills for the job role based on the placement prediction result.

The system predicts placement possibilities with an accuracy of 93.33 and 98% precision. Also, the skill analysis for students gives the students information about their skill-set strengths and weaknesses.

For skill-set analysis, only the direct assessment of the students is considered. Indirect assessment shall also be considered for future scope.

The model is adaptable and flexible (customizable) to any type of academic institute or universities.

The research will be very much useful for the students community to bridge the gap between the academic and industrial needs.

Several works are done for career guidance for the students. However, these career guidance methodologies are designed only using the curriculum and students’ basic personal information. The proposed system will consider the students’ academic performance through direct assessment, along with their curriculum and basic personal information.

The purpose of this research paper is to explore and offer insightful information on the useful use of Google Tag Manager (GTM) in the context of library websites and to bridge the gap between GTM’s technical features and the practical requirements of libraries. It gives libraries the ability to use GTM’s capabilities to increase user engagement, data-driven decision-making and improve online services.

This study reviews existing literature on GTM in the context of websites and libraries. The methodology involves identifying keywords and searching terms related to GTM, digital marketing, user engagement, Web analytics and library websites. Sources and databases were consulted, including library science journals, marketing journals, academic databases, publications on digital marketing and search platforms such as Google Books, Google Scholar, Google Search Engine, JSTOR and library associations like the American Library Association. Initial screening was done based on titles and abstracts, followed by a thorough-text review, categorization and synthesizing of the findings.

GTM provides libraries with a potent tool to improve their online presence, customize user experiences and collect insightful real-time data. Libraries may harness GTM’s potential to better engage people and provide services by properly implementing it and maintaining it over time. It can be a flexible instrument that supports contemporary library services in the digital era. The findings of this study indicate that GTM technology may be used in library services; nevertheless, there are several barriers, such as librarians’ attitudes and technical abilities, that prevent GTM acceptance in library services.

This study covers the implementation of a free GTM tool in library websites that will help the library and information professionals to leverage the GTM in the library’s online presence. Furthermore, this study recommends that libraries and librarians should develop guidelines and policies for the critical adoption of a free GTM tool in the library environment, which will support improving the library’s user engagement and tracking of library website traffic.

The purpose of this study to improve the efficiency of customer acquisition and retention through the design of salary information disclosure mechanism.

This study develops a stylized game-theoretic model of delegating customer acquisition and retention, focusing on how firms choose delegation and wage information disclosure strategy.

The results confirm the necessity for enterprises to disclose salary information. When sales agents are risk neutral, firms should choose multi-agent (MA) delegation and disclose their wages. However, when agents are risk averse, firms may disclose the wages of acquisition agents or both agents in MA delegation, depending on the uncertainty of the retention market.

This paper contributes to the literature on delegation of customer acquisition and retention and demonstrates that salary disclosure can be used as a supplement to the incentive mechanism.

The purpose of this study is to explore the impact of online reviews on answer adoption in virtual Q&A communities, with an eye toward extending knowledge exchange and community management.

Online reviews contain rich cognitive and emotional information about community members regarding the provided answers. As feedback information on answers, it is crucial to explore how online reviews affect answer adoption. Based on signaling theory, a research model reflecting the influence of online reviews on answer adoption is established and empirically examined by using secondary data with 69,597 Q&A data and user data collected from Zhihu. Meanwhile, the moderating effects of the informational and emotional consistency of reviews and answers are examined.

The negative binomial regression results show that both answer-related signals (informational support and emotional support) and answerers-related signals (answerers’ reputations and expertise) positively impact answer adoption. The informational consistency of reviews and answers negatively moderates the relationships among information support, emotional support and answer adoption but positively moderates the effect of answerers’ expertise on answer adoption. Furthermore, the emotional consistency of reviews and answers positively moderates the effect of information support and answerers’ reputations on answer adoption.

Although previous studies have investigated the impacts of answer content, answer source credibility and personal characteristics of knowledge seekers on answer adoption in virtual Q&A communities, few have examined the impact of online reviews on answer adoption. This study explores the impacts of informational and emotional feedback in online reviews on answer adoption from a signaling theory perspective. The results not only provide unique ideas for community managers to optimize community design and operation but also inspire community users to provide or utilize knowledge, thereby reducing knowledge search costs and improving knowledge exchange efficiency.

The purpose of this study is to examine the state of the literature on knowledge sharing in open source software (OSS) development communities by examining the existing research and identifying the knowledge gaps and opportunities that can inform areas for future research.

A systematic literature review was conducted of literature published between January 2011 and February 2023. A total of 24 papers were identified and reviewed.

The findings reveal that the literature on knowledge sharing in OSS development communities from developing countries are limited. Additionally, there exists a limited focus on the development of frameworks to support knowledge sharing in OSS communities. The transient nature of OSS development contributors’ results in knowledge loss; thus, knowledge retention needs further investigation.

This study only included papers whose titles, keywords or abstracts included the search keywords “knowledge sharing” and “Open Source Software”. While the keywords were carefully applied, when applying the search, it cannot be ruled that some relevant studies might have been missed. The study was also limited to conferences and journal papers published in English. Despite the limitations, the study provides a systematic review of knowledge sharing in OSS communities and presents findings that can be useful to researchers and practitioners interested in this area.

The study provides a systematic literature review of published papers and identifies themes and future research areas on knowledge sharing in OSS communities. Additionally, this review offers insights into future research avenues for theory, content and context on knowledge sharing in OSS development communities.

In prior literature, online endorsement system allowing the users to “like” or “dislike” shared information is found very useful in information filtering and trust elicitation in most social networks. This paper shows that such systems could fail in the context of investment communities due to several psychological biases.

This study develops a series of regression analyses to model the “like”/“dislike” voting process and whether or not such endorsement distinguishes between valuable information and noise. Trading simulations are also used to validate the practical implications of the findings.

The main findings of this research are twofold: (1) in the context of investment communities, online endorsement system fails to signify value-relevant information and (2) bullish information and “wisdom over the past event” information receive more “likes” and fewer “dislikes” on average, but they underperform in stock market price discovery.

This study demonstrates that biased endorsement may lead to the failure of the online endorsement system as information gatekeeper in investment communities. Two underlying mechanisms are proposed and tested. This study opens up new research opportunities to investigate the causes of biased endorsement in online environment and motivates the development of alternative information filtering systems.

This research seeks to better understand the potential uses of maker technologies, such as single-board computers and microcontrollers, more broadly within libraries and not simply confined to the makerspace. Through interviews with librarians creating such projects, this study illustrates their successes, challenges, means of acquiring the necessary skills and knowledge, as well as their perceptions of the broader benefits and challenges to other library and information science practitioners.

This research study employed semistructured interviews with 12 librarians who have created projects with maker technologies for broader library use. Inductive qualitative analysis of the interview transcripts was conducted to identify themes of interest to the stated research questions.

Librarians' projects included: displaying digital signage, hosting online public access catalog stations, tallying reference desk interactions, counting patrons at the gate and monitoring 3-D printing statistics, among others. Participants appreciated the low-cost, flexible and creative nature of such technologies, and though they also encountered technical and organizational challenges in their use, relayed a potential series of benefits to librarians and library staff were these technologies to be more widely used.

Although significant research efforts have focused on aspects of makerspaces across all types of libraries, little work has formally collected and assessed library practitioners' work with maker technologies outside of the makerspace. Participants help detail the potential benefits of having a deeper understanding of this work, and the successes it could bring to librarians' work.

The research aims to explore the influence mechanism of peer feedback and users' knowledge contribution behavior. This study draws on the social identity theory and considers social identity as a mediating factor into the research framework.

This paper collected users' activity data of 142,191 ideas submitted by 76,647 users from the MIUI community between October 2010 and May 2018 via Python software, and data were processed using Stata 16.0.

The results indicate that knowledge feedback and social feedback positively influence users' knowledge contribution (quantity and quality), respectively. User's cognitive identity positively mediates the relationship between peer feedback and knowledge contribution behavior, affective identity positively mediates the relationship between peer feedback and knowledge contribution behavior, while evaluative identity positively mediates the relationship between peer feedback and knowledge contribution quality, but there is no mediating effect between peer feedback and knowledge contribution quantity.

This study advances knowledge management by highlighting peer feedback on online innovation communities. By demonstrating the significant mediating effect of social identity, this study empirically clarifies the relationships of peer feedback (knowledge feedback and social feedback) to specific dimensions of knowledge contribution, thereby providing managerial guidance to the online innovation community on incentivizing and managing user interaction to foster the innovation development of firms.