Revealing the complexity of users’ intention to adopt healthcare chatbots: A mixed-method analysis of antecedent condition configurations

Healthcare chatbots provide a professional, immediate, and low-cost tool for advising people on health information. Unlike previous studies on chatbot adoption that focus on the one-way net effect of components, this study reveals the complex antecedent configurations behind online healthcare chatbot adoption through an asymmetric approach. This study attempts to explain the causal configuration of user adoption in terms of three dimensions: functional, social, and user motivation features. A sequential mixed-method approach was chosen and a two-stage study, fuzzy set comparison analysis (fsQCA), and semi-structured interviews were conducted to deepen users' understanding, perceptions, and attitudes toward online healthcare chatbots, expressing more fine-grained insights into variable relationships. Five configurations were found to explain the high willingness of users to adopt healthcare chatbots. Perceived social presence was a core condition for each configuration. However, the social features of chatbots can only lead to high levels of trust and satisfaction when combined with functionality. The findings of this paper's mixed-method design and complexity study contribute to the adoption and theoretical literature on intelligent health services and chatbots. It guides tailoring chatbot functionality to individual user needs and provides practical guidance for the development of chatbots for health services through diverse combinations of machine chatbot features and user-motivated features.     

Intelligent chatbot interaction system capable for sentimental analysis using hybrid machine learning algorithms

Chatbots actively help the human user through digital conversation through NLP based on artificial intelligence (AI). It can be pre-trained to understand the user's queries and produce an immediate response in NLP. The user input of the chatbot is any format like voice, text, sentiments, etc. Many research works have been implemented. The issues of existing works are that during the digital conversation, it does not accurately identify the user's requirement, it may go irrelevant to the user's query, and also, primarily, it is voice-based and faces hitches in the analysis of the user's intention, unable to track the context in long-conversation. Therefore, for understanding the context, sentimental calculations are essential. This paper proposed to make the immediate response of users in the chatbot by using Bi-directional Recurrent Neural Network with a Fuzzy Naïve Bayes classifier (BRNN-FNB). This paper aims to build chatbot models with AI-based sentimental analysis, which helps humans to perform accurate interactions. The working concept of chatbots is based on two forms of artificial intelligence domains: machine learning and natural language processing. It may be used in many applications like digital marketing, education, and online forums. The accuracy rate of the proposed work BRNN-FNB got 93% using the Seq-to-Seq technique. And also, the accuracy rate of the proposed work BRNN-FNB without using Seq-to-Seq got 92%.     

The appropriation of conversational AI in the workplace: A taxonomy of AI chatbot users

There is an exponential growth of the use of AI applications in organisations. Due to the machine learning capability of artificial intelligence (AI) applications, it is critical that such systems are used continuously in order to generate rich use data that allow them to learn, evolve and mature into a better fit for their user and organisational context. This research focuses on the actual use of conversational AI, in particular AI chatbot, as one type of workplace AI application to answer the research question: how do employees experience the use of an AI chatbot in their day-to-day work? Through a qualitative case study of a large international organisation and by performing an inductive analysis, the research uncovers the different ways in which users appropriate the AI chatbot and identifies two key dimensions that determine their type of use: the dominant mode of interaction and the understanding of the AI chatbot technology. Based on these dimensions, a taxonomy of users is presented, which classifies users of AI chatbots into four types: early quitters, pragmatics, progressives, and persistents. The findings contribute to the understanding of how conversational AI, particularly AI chatbots, is used in organisations and pave the way for further research in this regard. The implications for practice are also discussed.     

Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms

Text-based chatbots are being touted as a disruptive innovation with unprecedented business potential. However, frequent failures in human–chatbot conversations have led to consumer pushback. This study investigates the response of consumers to chatbots in terms of their intention to switch to human agents. Drawing upon the stimulus–organism–response (SOR) framework, focus is placed on how the anthropomorphic attributes of chatbots influence consumers’ perceived trust in chatbots and its implications for switching intention. Further, the moderating role of relationship norms in the relationships between the anthropomorphic attributes and trust in chatbots is examined. A mixed-methods approach is used; the qualitative analysis reveals three main anthropomorphic attributes of chatbots, two types of relationship norms and the specific response to chatbots. The quantitative results suggest that the anthropomorphic attributes of perceived warmth and perceived competence positively affect consumers’ perceived trust in chatbots, whereas communication delay negatively affects it. Relationship norms are found to moderate some of these effects such that exchange relationships strengthen the importance of perceived competence on trust, although communal relationships do not moderate the effects of perceived warmth on trust. Trust in chatbots negatively affects consumers’ intention to switch to a human agent. Theoretical and managerial implications are also discussed for scholars and practitioners in ways to improve the design and maximize the utility of chatbots.     

Perceived conversational ability of task-based chatbots – Which conversational elements influence the success of text-based dialogues?

The use of text-based chatbots offering individual support to customers has increased steadily in recent years. However, thus far, research has focused on comparing text-based chatbots with either each other or with humans, whilst the investigation of task-based dialogues has been scarce. This paper aims to identify the characteristics of dialogues – that is, conversational elements – that lead to a successful task-based conversation. For this purpose, the chatbot, KIM, by MAGGI Kochstudio was used. It was designed to help customers find a recipe tailored to their individual needs. In order to investigate which conversational elements contribute to successful communication between the user and the chatbot KIM, a usability study collecting 123 unstructured dialogues and a scenario-based experiment using four dialogues with 627 respondents was conducted. The quantitative analysis demonstrates that task completion is characterized by a higher perception of the chatbot’s conversational ability and user satisfaction. The chatbot should propose correct recipe suggestions following a short dialogue, without the user needing to provide too much input. Based on these findings, we recommended equipping the skillset of task-based chatbots with elements that will complement their assistive qualities – for example, improved use of standard phrases, and reactions to similar domains and non-requests. Gender-specific differences in task completion should be considered.     

考虑排污权交易的企业生产决策鲁棒优化

研究面向产品需求不确定的排污权交易企业生产决策。首先,在确定生产技术的条件下,针对产品需求不确定性,建立企业收益函数,然后,基于最小、最大后悔值准则,构建企业生产决策鲁棒模型,求解相对最优鲁棒生产决策,最后,通过数值分析说明本章模型的应用,为企业生产过程中决策者应对不确定决策提供建议,提高企业对排污权交易机制的适应性。     

顾客随机选择模式下最终产品开发与模仿的博弈分析

在一个相互竞争的最终产品市场中,顾客往往是在一定品牌忠诚度下随机采用不同竞争品牌的产品,因此企业在制定率先开发还是等待模仿战略时,要充分考虑创新产品在竞争市场中的扩散形态,这使博弈分析变得更加复杂。本文在Bass创新扩散条件下,研究了改进更新产品率先开发或拷贝式模仿的利润和利润均衡点,进行了完全信息动态情况下两者之间的博弈分析。同时指出了在不完全信息动态情况下,研究企业率先开发或进行拷贝式模仿博弈的途径。     

An ensemble model for classifying idioms and literal texts using BERT and RoBERTa

An idiom is a common phrase that means something other than its literal meaning. Detecting idioms automatically is a serious challenge in natural language processing (NLP) domain applications like information retrieval (IR), machine translation and chatbot. Automatic detection of Idioms plays an important role in all these applications. A fundamental NLP task is text classification, which categorizes text into structured categories known as text labeling or categorization. This paper deals with idiom identification as a text classification task. Pre-trained deep learning models have been used for several text classification tasks; though models like BERT and RoBERTa have not been exclusively used for idiom and literal classification. We propose a predictive ensemble model to classify idioms and literals using BERT and RoBERTa, fine-tuned with the TroFi dataset. The model is tested with a newly created in house dataset of idioms and literal expressions, numbering 1470 in all, and annotated by domain experts. Our model outperforms the baseline models in terms of the metrics considered, such as F-score and accuracy, with a 2% improvement in accuracy.     

大企业与小企业的产品创新困境与出路选择研究

人们习惯认为,大企业在资金与人才方面与小企业相比有较大的优势,因此大企业应当有更多的产品创新,但实际结果却恰恰与此认识相反.在奥尔森的集体行动的逻辑模型基础上,分析了在资金与人才等方面都不占优势的小企业何以能够创造大多数创新产品的原因,并指出了与它们相对应的产品创新路径.     

Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature

Section identification is an important task for library science, especially knowledge management. Identifying the sections of a paper would help filter noise in entity and relation extraction. In this research, we studied the paper section identification problem in the context of Chinese medical literature analysis, where the subjects, methods, and results are more valuable from a physician's perspective. Based on previous studies on English literature section identification, we experiment with the effective features to use with classic machine learning algorithms to tackle the problem. It is found that Conditional Random Fields, which consider sentence interdependency, is more effective in combining different feature sets, such as bag-of-words, part-of-speech, and headings, for Chinese literature section identification. Moreover, we find that classic machine learning algorithms are more effective than generic deep learning models for this problem. Based on these observations, we design a novel deep learning model, the Structural Bidirectional Long Short-Term Memory (SLSTM) model, which models word and sentence interdependency together with the contextual information. Experiments on a human-curated asthma literature dataset show that our approach outperforms the traditional machine learning methods and other deep learning methods and achieves close to 90% precision and recall in the task. The model shows good potential for use in other text mining tasks. The research has significant methodological and practical implications.     

A blockchain-based framework of cross-border e-commerce supply chain

Blockchain technology provides us a new tool to solve the product traceability problem in supply chain management. This research focuses on the cross-border e-commerce context, to propose a blockchain-based framework, and develop a set of corresponding techniques and methods for achieving traceable products and transactions in supply chain management. A general blockchain-based product traceability framework is introduced. This framework is based on a cross-border e-commerce supply chain context, incorporating a series of blockchain-based models, including multi-chain structure model, data management model and block structure model. Several core methods and algorithms are also developed, such as information anchoring method, key distribution method, information encryption algorithm and anti-counterfeiting method. The framework, models and methods form a complete and comprehensive solution, which are evaluated by applying to several typical problems and attack cases. The case analysis results show that the framework, models and methods can successfully deal with key recover problem, and protect against clone attack, counterfeit tag attack and counterfeit product attack. The effectiveness, extendibility, security, implementation and governance issues of applying these solutions are also discussed. This research contributes to the theoretical and practical literatures on blockchain technology, cross-border e-commerce and supply chain management research fields.     

企业服务型制造转型升级研究——以SG集团为例

对服务型制造示范企业——SG集团进行深入研究,对其成功转型的战略、策略及关键做法做以总结,针对SG和其他企业服务型制造转型升级过程中存在的共性问题进行分析并从产业和企业两个层面提出措施建议,对未来企业向服务型制造转型升级提供实践指导。     

面向企业产品创新的信息重组

基于复杂网络特征和产品创新过程模型,提出利用信息重组进行企业产品创新的方案。首先介绍了作为理论基础的网络小世界效应和传递性;其次,揭示了产品创新信息来源的三个层面;再次,从知识的显性和隐性关联关系两个角度出发,论述了信息重组的模式和方法;最后,讨论了企业产品创新的信息重组的体系结构。     

The contribution of information to business success: a LISREL model analysis of manufacturers in Shanghai

In an attempt to quantitatively measure the contribution of information to business development, two studies were carried out in Shanghai, China: one on small businesses and the other on medium-sized businesses. The LISREL (linear structural relations) model was applied in both studies to determine the magnitude of the contribution. Both studies showed that the use of information does contribute positively and in a statistically significant way to business success, although its contribution is not as important as some other factors such as business environment. A comparison of the LISREL models from the two studies also revealed similarities and differences in the use of information in these two businesses sectors.     

Information security management needs more holistic approach: A literature review

Information technology has dramatically increased online business opportunities; however these opportunities have also created serious risks in relation to information security. Previously, information security issues were studied in a technological context, but growing security needs have extended researchers' attention to explore the management role in information security management. Various studies have explored different management roles and activities, but none has given a comprehensive picture of these roles and activities to manage information security effectively. So it is necessary to accumulate knowledge about various managerial roles and activities from literature to enable managers to adopt these for a more holistic approach to information security management. In this paper, using a systematic literature review approach, we synthesised literature related to management's roles in information security to explore specific managerial activities to enhance information security management. We found that numerous activities of management, particularly development and execution of information security policy, awareness, compliance training, development of effective enterprise information architecture, IT infrastructure management, business and IT alignment and human resources management, had a significant impact on the quality of management of information security. Thus, this research makes a novel contribution by arguing that a more holistic approach to information security is needed and we suggest the ways in which managers can play an effective role in information security. This research also opens up many new avenues for further research in this area.     

Customer perceived value in the software business

This paper addresses customer perceived value in the context of high-technology industry, specifically in the software business. Customer perception of value is a complex phenomenon not only theoretically, but even more so in practice. We have chosen to examine this phenomenon in the specific context of the software business, as we believe that software as an object of exchange encourages fresh viewpoints owing to its abstract nature. Our study is exploratory in nature, with an empirical insight gained through two qualitative case studies from the software business. The conclusion presents elements of customer perceived value within both software project and product businesses. Based on the findings, we suggest a framework for examining the way business logic influences the customer's value perception, especially in terms of the complexity of the perception of both benefits and sacrifices.     

Towards model governance in predictive toxicology

Efficient management of toxicity information as an enterprise asset is increasingly important for the chemical, pharmaceutical, cosmetics and food industries. Many organisations focus on better information organisation and reuse, in an attempt to reduce the costs of testing and manufacturing in the product development phase. Toxicity information is extracted not only from toxicity data but also from predictive models. Accurate and appropriately shared models can bring a number of benefits if we are able to make effective use of existing expertise. Although usage of existing models may provide high-impact insights into the relationships between chemical attributes and specific toxicological effects, they can also be a source of risk for incorrect decisions. Thus, there is a need to provide a framework for efficient model management. To address this gap, this paper introduces a concept of model governance, that is based upon data governance principles. We extend the data governance processes by adding procedures that allow the evaluation of model use and governance for enterprise purposes. The core aspect of model governance is model representation. We propose six rules that form the basis of a model representation schema, called Minimum Information About a QSAR Model Representation (MIAQMR). As a proof-of-concept of our model governance framework we develop a web application called Model and Data Farm (MADFARM), in which models are described by the MIAQMR-ML markup language.     

虚拟顾客参与平台的运行机制:理论与实证研究

通过虚拟顾客参与平台（Virtual Customer Environments, VCE）来吸引顾客参与创新、利用顾客大数据、创建顾客中心型组织成为企业的重要策略，但VCE的运行机制并未得到理论界的足够重视。本文以VCE功能为主线，将VCE分为信息展示、需求收集和深度互动三个子平台。基于刺激-机体-反应模型，分析每个子平台的运行机制。具体而言，IT环境和市场环境的发展构成VCE的运行基础，企业和顾客之间的交互关系构成VCE的运行动因，企业吸引行为和顾客参与行为构成VCE的运行过程。进一步运用内容分析法，本文检验了中国500强企业网站中VCE子平台的运行，实证研究结果支持理论模型所提出的运行机制。但同时也发现，大多数中国500强企业尚未充分构建需求收集和深度互动这两个子平台，表现为在实际运行中未能有效吸引顾客参与。本研究不仅推动了VCE理论的发展，对顾客参与理论进行了拓展，并且有助于指导企业完善VCE平台的构建，从信息技术的角度塑造顾客导向型组织，提升创新绩效。     

基于深度学习的多学科多层次学术论文结构功能识别方法比较研究

【目的/意义】学术论文的结构功能是学术论文篇章结构和语义内容的集中体现,目前针对学术论文结构功 能的研究主要集中在对学术论文不同层次的识别以及从学科差异性视角探讨模型算法的适用性两方面,缺少模 型、学科、层次之间内在联系的比较研究。【方法/过程】选择中医学、图书情报、计算机、环境科学、植物学等学科中 文权威刊物发表的学术论文作为实验语料集,在引入CNN、LSTM、BERT等深度学习模型的基础上,分别从句子、 段落、章节内容等层次对学术论文进行结构功能识别。【结果/结论】实验结果表明,BERT模型对于不同学科学术论 文以及学术论文的不同层次的结构功能识别效果最优,各个模型对于不同学科学术论文篇章内容层次的识别效果 均最优,中医学较之其他学科的学术论文结构功能识别效果最优。此外,利用混淆矩阵给出不同学科学术论文结 构功能误识的具体情形并分析了误识原因。【创新/局限】本文研究为学术论文结构功能识别研究提供了第一手的 实证资料。