Explore the vast range of titles available.

Purpose The purpose of this paper is to explain the technological phenomenon artificial intelligence (AI) and how it can contribute to knowledge-based marketing in B2B. Specifically, this paper describes the foundational building blocks of any artificial intelligence system and their interrelationships. This paper also discusses the implications of the different building blocks with respect to market knowledge in B2B marketing and outlines avenues for future research. Design/methodology/approach The paper is conceptual and proposes a framework to explicate the phenomenon AI and its building blocks. It further provides a structured discussion of how AI can contribute to different types of market knowledge critical for B2B marketing: customer knowledge, user knowledge and external market knowledge. Findings The paper explains AI from an input–processes–output lens and explicates the six foundational building blocks of any AI system. It also discussed how the combination of the building blocks transforms data into information and knowledge. Practical implications Aimed at general marketing executives, rather than AI specialists, this paper explains the phenomenon artificial intelligence, how it works and its relevance for the knowledge-based marketing in B2B firms. The paper highlights illustrative use cases to show how AI can impact B2B marketing functions. Originality/value The study conceptualizes the technological phenomenon artificial intelligence from a knowledge management perspective and contributes to the literature on knowledge management in the era of big data. It addresses calls for more scholarly research on AI and B2B marketing.

The analysis of user behavior provides a large amount of useful information. After being extracted, this information is called user knowledge. User knowledge plays a guiding role in implementing user-centric updates for software platforms. A good representation and application of user knowledge can accelerate the development of a software platform and improve its quality. This paper aims to further the utilization of user knowledge by mining the user knowledge that is implicit in user behavior and then constructing a knowledge graph of this behavior. First, the association between a software bug and a software component is mined from the user knowledge. Then, the knowledge entity extraction and relationship extraction are performed from the development code and the user behavior. Finally, the knowledge is stored in the graph database, from which it can be visually retrieved. Relevant experiments on CIFLog, an integrated logging processing software platform, have proved the effectiveness of this research. Constructing a user behavior knowledge graph can improve the utilization of user knowledge as well as the quality of software platform development.

Scoping reviews are a popular and influential form of evidence synthesis. Guidance has previously highlighted the importance of conducting knowledge user (KU) consultations within scoping reviews; however, their use has been limited to date, and the guidance is not clear regarding their methodology and indications for use. This has led to methodological ambiguity and uncertainty regarding KU consultation. We aimed to explore the views and experiences of scoping review authors on using KU consultations within scoping reviews. A descriptive qualitative study design was used. We recruited scoping review authors who had previously conducted a KU consultation to participate in individual semi-structured interviews focusing on the conduct, value, utility, and impact of KU consultations within scoping reviews and the barriers and enablers to conducting them. We used reflexive thematic analysis to analyze interviews. We conducted 15 interviews with 16 participants (one dyad). We identified three main themes; ‘Motivations to Do KU Consultations in Scoping Reviews’, ‘The Who, What, and How of Doing Consultations in Scoping Reviews’, and ‘Fostering Growth: Lessons Learned and Future Steps’. Authors view KU consultations as a valuable methodological component of scoping reviews; however, sufficient resources and capacity are needed to conduct them. There is also a lack of clarity and consensus regarding what defines a consultation and how best to conduct them, particularly alongside other forms of KU involvement. Further guidance is needed to clarify the role of KU consultations within scoping reviews. Scoping reviews are a way of summarizing knowledge on a particular topic. In this study, we looked at how researchers use \"knowledge user consultations\" in scoping reviews. Knowledge users (KU) are people such as patients, policymakers, and health-care providers who can use the results of the research findings. Although using KU consultations in scoping reviews is recommended, it is not often done, and there is confusion about how and when to do it. We interviewed 16 researchers who have done KU consultations as part of a scoping review. From these interviews, we found three key themes: ‘Motivations to Do KU Consultations in Scoping Reviews’, ‘The Who, What, and How of Doing Consultations in Scoping Reviews’, and ‘Fostering Growth: Lessons Learned and Future Steps’. Overall, researchers felt that KU consultations are useful and add value, but they also said more resources and clearer guidance are needed to do them properly. There is still a lot of uncertainty about what counts as a consultation and how it should be done. More detailed and practical guidance would help make this process clearer for future research. •Approaches to knowledge user (KU) consultation in scoping reviews vary greatly.•Clarity is lacking regarding the difference between consultation and cocreation; KU involvement of all types is viewed as beneficial for scoping reviews.•KU consultations in scoping reviews are limited by a lack of guidance and resources.•A variety of approaches to KU involvement should be considered.

Background Integrated knowledge translation (IKT) is a model of research co-production, whereby researchers partner with knowledge users throughout the research process and who can use the research recommendations in practice or policy. IKT approaches are used to improve the relevance and impact of research. As an emerging field, however, the evidence underpinning IKT is in active development. The Integrated Knowledge Translation Research Network represents a collaborative interdisciplinary team that aims to advance the state of IKT science. Methods In 2017, the Integrated Knowledge Translation Research Network issued a call to its members for concept papers to further define IKT, outline an IKT research agenda, and inform the Integrated Knowledge Translation Research Network’s special meeting entitled, Integrated Knowledge Translation State of the Science Colloquium, in Ottawa, Canada (2018). At the colloquium, authors presented concept papers and discussed knowledge-gaps for a research agenda and implications for advancing the IKT field. We took detailed field notes, audio-recorded the meeting and analysed the data using qualitative content analysis. Results Twenty-four participants attended the meeting, including researchers ( n  = 11), trainees ( n  = 6) and knowledge users ( n  = 7). Seven overarching categories emerged from these proceedings – IKT theory, IKT methods, IKT process, promoting partnership, definitions and distinctions of key IKT terms, capacity-building, and role of funders. Within these categories, priorities identified for future IKT research included: (1) improving clarity about research co-production/IKT theories and frameworks; (2) describing the process for engaging knowledge users; and (3) identifying research co-production/IKT outcomes and methods for evaluation. Conclusion The Integrated Knowledge Translation State of the Science Colloquium initiated a research agenda to advance IKT science and practice. Next steps will focus on building a theoretical and evidence base for IKT.

Background Collaborations involving partnerships between academic researchers and knowledge users can improve the relevance and potential adoption of evidence in health care practices and decision-making. However, descriptions of partnering practice characteristics are often limited to self-report from the lead academic researcher, with no comparison among team members. The primary objective of this study was to determine the extent to which nominated principal investigator (NPI) respondents of a questionnaire about funded Canadian partnered health research projects agreed with other team researchers and knowledge users (KU) on partnership practices. Methods We conducted secondary analysis of a subset of data from 106 respondents from 53 partnered Canadian health research projects funded between 2011 and 2019. We organized projects into NPI-researcher and NPI-KU dyads, and analyzed 23 binary variables about types of knowledge users involved and approaches for involving knowledge users in the project. We calculated Kappa scores and examined if agreement varied by dyad type and time across three blocks of years of project funding using a two-way ANOVA. We also explored how agreement varied by question type (independent t-test) and by variable (Pearson Chi-Square). Results Overall agreement on partnership practices was minimal (mean Kappa = 0.38, SD 0.27). NPI- researcher dyads had higher Kappa scores than NPI-KU dyads ( p  = 0.03). There were no significant differences across funding year blocks ( p  > 0.05). Agreement on the types of knowledge users engaged in the project was weak (mean Kappa = 0.43, SD 0.32), and there was no difference by dyad type. Agreement was minimal on the approaches for involving knowledge users the project (mean Kappa = 0.28, SD 0.31), and NPI-researcher dyads had significantly higher Kappa scores than NPI-KU dyads ( p  = 0.03). Variable-level agreement ranged between 47 and 98%. Conclusions The overall low level of agreement among team members responding about the same project has implications for the continued study and practice of partnered health research. These findings highlight the caution that must be used in interpreting retrospectively assessed self-report practices. Moving forward, prospective documentation of partnered research practices offers the greatest potential to overcome the limitations of recall-based retrospective analyses.

PurposeThe purpose of this study was to assess the transposability of study results published in the Journal of Organizational Change Management (JOCM) and the Journal of Change Management (JCM) between 2000 and 2019 for change-management practitioners and researchers.Design/methodology/approachA systematic scoping review of a large sample of articles published in both journals was undertaken: 122 studies were considered for analysis and coded by two independent coders using an inductive grid.FindingsFindings show that few studies (1) describe the nature of changes undertaken by organizations; (2) explain the contextual elements that characterize the environment at the moment when these same transformations are deployed; or (3) nuance their observations according to the change operation.Research limitations/implicationsInformation on the type of change undertaken by the organization and about how change has been implemented is useful when communicating new scientific knowledge to practitioners. Nevertheless, the way in which studies are sometimes described masks some important nuances to be considered when interpreting or replicating certain results.Practical implicationsThe relevance of these issues is enhanced by the fact that researchers or practitioners (as knowledge users) are likely to reproduce some of the actions carried out in previous studies in order to deepen research avenues or to facilitate the implementation of change initiatives in workplaces.Originality/valueThis research is among the first to assess the transferability of change-management study results published in both journals over such a long period. Its relevance also speaks to the importance of contextualizing results to ease their transposability by researchers and practitioners.

Knowledge recommendation plays a crucial role in online learning platforms. It aims to optimize the service quality so as to improve users’ learning efficiency and outcomes. Existing approaches generally leverage RNN-based methods in combination with attention mechanisms to learn user preference. There is a lack of in-depth understanding of users’ knowledge-level changes over time and the impact of knowledge item categories on recommendation performance. To this end, we propose the knowledge-level-evolution and category-aware personalized knowledge recommendation (KLECA) model. The model firstly leverages bidirectional GRU and the time adjustment function to understand users’ learning evolution by analyzing their learning trajectory data. Secondly, it considers the effect of item categories and descriptive information and enhances the accuracy of knowledge recommendation by introducing a cross-head decorrelation module to capture the information of knowledge items based on a multi-head attention mechanism. In addition, a personalized attention mechanism and gated function are introduced to grab the relationship between items, item categories and user learning trajectory to strengthen the representation of information. Through extensive experiments on real-world data collected from an online learning platform, the proposed approach has been shown to significantly outperform other approaches.

The objective of this article is to discuss the knowledge dissemination and exchange components of the knowledge translation process that includes synthesis, dissemination, exchange, and ethically sound application of knowledge. This article presents and discusses approaches to knowledge dissemination and exchange and provides a summary of factors that appear to influence the effectiveness of these processes. It aims to provide practical information for researchers and knowledge users as they consider what to include in dissemination and exchange plans developed as part of grant applications. Not relevant. Dissemination is targeting research findings to specific audiences. Dissemination activities should be carefully and appropriately considered and outlined in a dissemination plan focused on the needs of the audience who will use the knowledge. Researchers should engage knowledge users to craft messages and help disseminate research findings. Knowledge brokers, networks, and communities of practice hold promise as innovative ways to disseminate and facilitate the application of knowledge. Knowledge exchange or integrated knowledge translation involves active collaboration and exchange between researchers and knowledge users throughout the research process.

Purpose This study develops and validates an innovative approach for extracting knowledge from online user reviews by integrating textual content and emojis. Recognizing the pivotal role emojis play in enhancing the expressiveness and emotional depth of digital communication, this study aims to address the significant gap in existing sentiment analysis models, which have largely overlooked the contribution of emojis in interpreting user preferences and sentiments. By constructing a comprehensive model that synergizes emotional and semantic information conveyed through emojis and text, this study seeks to provide a more nuanced understanding of user preferences, thereby enhancing the accuracy and depth of knowledge extraction from online reviews. The goal is to offer a robust framework that enables more effective and empathetic engagement with user-generated content on digital platforms, paving the way for improved service delivery, product development and customer satisfaction through informed insights into consumer behavior and sentiments. Design/methodology/approach This study uses a structured methodology to integrate and analyze text and emojis from online reviews for effective knowledge extraction, focusing on user preferences and sentiments. This methodology consists of four key stages. First, this study leverages high-frequency noun analysis to identify and extract product attributes mentioned in online user reviews. By focusing on nouns that appear frequently, the authors can systematically discern the primary features or aspects of products that users discuss, thereby providing a foundation for a more detailed sentiment and preference analysis. Second, a foundational sentiment dictionary is established that incorporates sentiment-bearing words, intensifiers and negation terms to analyze the textual part of the reviews. This dictionary is used to assign sentiment scores to phrases and sentences within reviews, allowing the quantification of textual sentiments based on the presence and combination of these predefined lexical items. Third, an emoticon sentiment dictionary is developed to address the emotional content conveyed through emojis. This dictionary categorizes emojis based on their associated sentiments, thus enabling the quantification of emotional expressions in reviews. The sentiment scores derived from the emojis are then integrated with those from the textual analysis. This integration considers the weights of text- and emoji-based emotions to compute a comprehensive attribute sentiment score that reflects a nuanced understanding of user sentiments and preferences. Finally, the authors conduct an empirical study to validate the effectiveness of the proposed methodology in mining user preferences from online reviews by applying the approach to a data set of online reviews and evaluating its ability to accurately identify product attributes and user sentiments. The validation process assessed the reliability and accuracy of the methodology in extracting meaningful insights from the complex interplay between text and emojis. This study offers a holistic and nuanced framework for knowledge extraction from online reviews, capturing both explicit and implicit sentiments expressed by users through text and emojis. By integrating these elements, this study seeks to provide a comprehensive understanding of user preferences, contributing to improved consumer insight and strategic decision-making for businesses and researchers. Findings The application of the proposed methodology for integrating emojis with text in online reviews yields significant findings that underscore the feasibility and value of extracting realistic user knowledge to gain insights from user-generated content. The analysis successfully captured consumer preferences, which are instrumental in informing service decisions and driving innovation. This achievement is largely attributed to the development and utilization of a comprehensive emotion-sentiment dictionary tailored to interpret the complex interplay between textual and emoji-based expressions in online reviews. By implementing a sentiment calculation model that intricately combines textual sentiment analysis with emoji sentiment analysis, this study was able to accurately determine the final attribute emotion for various product features discussed in the reviews. This model effectively characterized the emotional knowledge of online users and provided a nuanced understanding of their sentiments and preferences. The emotional knowledge extracted is not only quantifiable but also rich in context, offering deeper insights into consumer behavior and attitudes. Furthermore, a case analysis is conducted to rigorously test the validity of the proposed model in a real-world scenario. This practical examination revealed that the model is not only capable of accurately extracting and analyzing user preferences but is also adaptable to different contexts and product categories. The case analysis highlights the robustness and flexibility of the model, demonstrating its potential to enhance the precision of knowledge extraction processes significantly. Overall, the results confirm the effectiveness of the proposed approach in integrating text and emojis for comprehensive knowledge extraction from online reviews. The findings validate the model’s capability to offer actionable insights into consumer preferences, thereby supporting more informed and strategic decision-making by businesses. This study contributes to the broader field of sentiment analysis by showcasing the untapped potential of emojis as valuable indicators of user sentiments, opening new avenues for research and applications in digital marketing and consumer behavior analysis. Originality/value This study introduces a pioneering approach to extract knowledge from Web user interactions, notably through the integration of online reviews that incorporate both textual content and emoticons. This innovative methodology stands out because it holistically considers the dual channels of communication, text and emojis, to comprehensively mine Web user preferences. The key contribution of this study lies in its novel insights into the extraction of consumer preferences, advancing beyond traditional text-based analysis to embrace nuanced expressions conveyed through emoticons. The originality of this study is underpinned by its acknowledgment of emoticons as a significant and untapped source of sentiment and preference indicators in online reviews. By effectively merging emoticon analysis and emoji emotion scoring with textual sentiment analysis, this study enriches the understanding of Web user preferences and enhances the accuracy and depth of consumer preference insights. This dual-analysis approach represents a significant leap forward in sentiment analysis, setting a new standard for how digital communication can be leveraged to derive meaningful insights into consumer behavior. Furthermore, the results have practical implications to businesses and marketers. The insights gained from this integrated analytical approach offer a more granular and emotionally nuanced view of customer feedback, which can inform more effective marketing strategies, product development and customer service practices. By pioneering this comprehensive method of knowledge extraction, this study paves the way for future research and practice to interpret and respond more accurately to the complex landscape of online consumer expressions. This study’s originality and value lie in its innovative method of capturing and analyzing the rich tapestry of Web user communication, offering a ground-breaking perspective on consumer preference extraction that promises to enhance both academic research and practical applications in the digital era.