Physicians’ willingness to share: a TPB-based analysis

Physicians’ willingness to share knowledge is a central antecedent of effective knowledge sharing within hospitals. This paper aims at supporting the micro-foundation of knowledge sharing with the identification of the factors that affect willingness to share and their classification according to a behavioural model grounded in the Theory of Planned Behaviour (TPB). The identification of antecedents and their classification is based on the review of the major contributions published on top tier and specialist journals. This study identifies 22 factors that affect physicians’ willingness to share. These factors are classified according to the TPB framework, which identifies three behavioural antecedents: attitude, subjective norm and perceived behaviour control. The organization of the factors allows appreciating the diffusion of knowledge-sharing behaviours as the result of three concurring perspectives: raising attention to the benefits of knowledge sharing, building up social pressures from relevant actors and designing appropriate organizational contingencies.     

Marketing analysis of wineries using social collective behavior from users’ temporal activity on Twitter

Marketing professionals face challenges of increasing complexity to adapt classic marketing strategies to the phenomenon of social networks. Companies are currently trying to take advantage of the useful collective knowledge available on social networks to support different types of marketing decisions. The appropriate analysis of this information can offer marketing professionals with important competitive advantages. This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users’ temporal activity. Time series of mentions made by individual users to each company’s Twitter account are aggregated to obtain collective activity data for the companies, which is a consequence of both the company’s and other users’ actions. These data are processed using classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, to extract collective temporal behavior patterns and models of the dynamics of customers over time for a single brand and groups of brands. The derived knowledge can be used for different tasks, such as identifying the impact of a marketing campaign on Twitter and comparatively assessing the social behaviors of different brands and groups of brands to assist in making marketing decisions. Our methodology is validated in a case study from the wine market. Twitter data were gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley), and comparative behavior analysis was carried out from the perspective of the use of Twitter as a communication channel for marketing campaigns.     

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.     

Sequential analysis and clustering to investigate users’ online shopping behaviors based on need-states

With the fast growth of e-commerce and the emerging new retail trend—online and offline integration—it is important to recognize the target market and satisfy customers with different needs by analyzing their online search behaviors. Accordingly, we propose sequential search pattern analysis and clustering to analyze consumers’ search behavior throughout the entire shopping process from the perspective of consumer need-states. We seek to understand how recommendation functions (RFs) or popular non-RF web features help consumers to shop online from a need-state perspective. We adopt maximal repeat patterns (MRPs) and lag sequential analysis (LSA) to analyze the sequence of search paths and identify significant repeated search patterns. Furthermore, to investigate the behaviors of customers with different types of need-states, we analyze webpages related to RFs and non-RF features using clustering to connect the evaluation results of search patterns with page traversal behaviors. This yields four groups of consumers who browse for information, adopt recommendations, consult reviews, and conduct searches with different levels of goal-oriented or exploratory-based need-states. The results show that consumers with strong goal-oriented need-states have the simplest search paths compared to other groups, whereas exploratory-based consumers have the most complicated search paths. Furthermore, consumers with higher need-states tend to search directly, consult reviews carefully, and have stored sequential search patterns, whereas consumers with exploratory-based need-states tend to explore the categories of products and adopt product classification hierarchy as a pivot to explore web features and then adopt specific types of RFs. Interestingly, consumers in the review-consulting group all belong to the goal-oriented need-states type with strong knowledge-building behaviors compared to others. The results reveal that each group employs its own particular web features to facilitate the shopping process and we can identify consumer types based on shopping behavior in the early stage of shopping. This suggests that e-store sellers can refine web features and deploy marketing strategies tailored to the search patterns for different levels of need-states.     

Internet users’ perceptions of information sensitivity – insights from Germany

With the ever-increasing collection of user data, online privacy becomes an urgent matter for users and research across borders. The perception of information sensitivity is central to privacy attitudes and behaviors in different usage contexts. In an online questionnaire, n = 592 German internet users evaluated how sensitive they perceive 40 different data types. The German sensitivity evaluations revealed in this study are compared to results from the US and Brazil (Markos et al., 2017), in order to understand the cultural impact on evaluations. Additionally, we analyze how attitudes and demographic characteristics of the German sample influence the perception of sensitivity on an individual level. Some distinct differences in sensitivity perception between Germany, Brazil, and the US can be observed, but the rank orders of sensitivity of data types is very similar between the countries, indicating that there is a consensus on what constitutes sensitivity across nations. On an individual level, disposition to value privacy, risk propensity, and education level influence the perception of sensitivity. The findings contribute to an understanding of how to design information and communication strategies to inform internet users how to manage their data carefully.     

Novel delay-partitioning approaches to stability analysis for uncertain Lur’e systems with time-varying delays

This work deals with the problem of absolute stability analysis for a class of uncertain Lur’e systems with time-varying delays. Novel delay-partitioning approaches are presented, which are dividing the variation interval of the delay into three subintervals. Some new augment Lyapunov–Krasovskii functionals (LKFs) are defined on each of the obtained subintervals which can efficiently make use of the information of the delay and relate to the reciprocally convex combination technique and the Wirtinger-based integral inequality method. Several improved delay-dependent criteria are derived in terms of the linear matrix inequalities (LMIs). The merit of the proposed criteria lies in their less conservativeness and lower numerical complexity than relative literature. Two numerical examples are included to illustrate the effectiveness and the improvement of the proposed method.     

Review Article: The Personalisation of Computing—from Behemoth to Desktop

Social media (SM) are fast becoming a locus of disaster-related activities that range from volunteers helping locate disaster victims to actions that are malicious and offensive, from sincere expressions of empathy towards affected communities to consuming disaster imagery for mere entertainment, from recovery support funds being collected to online marketers preying on the attention afforded to a disaster event. Because of the diversity and sheer volume of both relevant and irrelevant information circulating throughout SM, prioritising an affected population’s needs and relevant data is an increasingly complex task. In addition, SM data need to be interpreted as manifestations of social processes related to community resilience, diversity and conflict of interests, and attitudes to particular response strategies. The use of SM in disasters generates a growing need for domain-specific technological solutions that can enhance public interests as well as address the needs of both disaster managers and the affected population. This task requires integrating social sciences into the development of tools that enable disaster SM data detection, filtering, analysis and representation. The aim of this paper is to contribute to a critical-constructive dialogue between social scientists and developers of SM analytic capabilities. In the context of historical, anthropological and sociological research on disaster, this paper outlines concepts of the disaster paradigm, data as a product of social and representational practices, and disaster context, and discusses their heuristic significance for the analysis of disaster SM as a manifestation of social and cultural practices.     

Influence of technology transfer from universities on manufacturing firms’ innovative performance

Technology transfer (TT) from universities to manufacturing firms is important for enhancing innovation performance (IP) and ultimately improving competitiveness. However, TT is hampered by bureaucracy, inertia, inefficiency, cognitive dissonance, and low research and development activity. The purpose of this study is to examine the relationship between TT from universities to manufacturing firms and their IP. This study was conducted based on Megnigbeto's (2018) triple helix game theory. A mixed-method approach was used. The independent variable was TT, measured in terms of technology spillover, networking, and the presence of accelerators and incubators within a firm's locality. The dependent variable was IP, measured through innovation output and innovation efficiency. This study used an explanatory sequential research design. The target population was manufacturing firms in Kenya, identified using a multi-stage sampling strategy. Primary data were collected using semi-structured questionnaires, interview schedules, and checklists. Linear regressions, hierarchical multiple moderated regressions, structural equation modeling, and partial least squares were used to test the hypotheses. The results indicate that TT from universities significantly influences IP in manufacturing firms. Universities are important intermediaries of TT in manufacturing firms in Kenya because of their improved IP and competitiveness. Universities should create dynamic linkages with industries and adopt an engaged learning approach in their programs to create greater and more unique values for enhanced competitiveness and sustainable development.     

Uncovering the value stream of digital content business from users’ viewpoint

Although digital content (DC) business has been increasingly recognized as one of the core industries in a knowledge-based economy, little is known about its value stream from DC users’ perspective. This study suggests a DC value chain framework involving the stream of DC value from DC creation to use from users’ perspective, which has three key components of DC-value stream: (1) DC-value creation, (2) DC-value interaction, and (3) DC-value use. Drawing upon the DC value chain framework, we propose a research model exploring the direct relationships between DC-value creation and DC-value interaction and between DC-value interaction and DC-value use. It also explains the moderating role of DC types between DC-value and DC-value interactions. The empirical results of the study indicate that DC-value interactions play an intermediary role between DC creation value and DC use value and that users’ perceptions of DC value vary in different types of DC. We discuss theoretical contributions and practical implications along with some avenues for future research.     

Exploring the economic value of personal information from firms’ financial statements

Currently personal data gathering in online markets is done on a far larger scale and much cheaper and faster than ever before. Within this scenario, a number of highly relevant companies for whom personal data is the key factor of production have emerged. However, up to now, the corresponding economic analysis has been restricted primarily to a qualitative perspective linked to privacy issues. Precisely, this paper seeks to shed light on the quantitative perspective, approximating the value of personal information for those companies that base their business model on this new type of asset. In the absence of any systematic research or methodology on the subject, an ad hoc procedure is developed in this paper. It starts with the examination of the accounts of a number of key players in online markets. This inspection first aims to determine whether the value of personal information databases is somehow reflected in the firms’ books, and second to define performance measures able to capture this value. After discussing the strengths and weaknesses of possible approaches, the method that performs best under several criteria (revenue per data record) is selected. From here, an estimation of the net present value of personal data is derived, as well as a slight digression into regional differences in the economic value of personal information.     

Two modes of participation: A conceptual analysis of 102 cases of Internet and social media participation from 2005–2015

In this article we analyze 102 case studies of Internet or social media-enabled participatory projects, technologies, platforms and companies in operation between roughly 2005–2015. We assign each case a “signature” representing the degree of presence/absence of seven dimensions of participation and then cluster these signatures to look for patterns of the most common ways of “doing participation” today. Two main clusters become apparent: 1) a “radical-direct” mode that emphasizes direct individual autonomy and influence, commitment to having a voice and setting goals, and individual or collective control over resources thereby produced; and 2) an “experiential-affective” mode that emphasizes the experience of being or becoming part of a collective, and the affective, communicational, and educational features of that experience.     

Mapping firms’ locations in technological space: A topological analysis of patent statistics

Where do firms innovate? Mapping their locations and directions in technological space is challenging due to its high dimensionality. We propose a new method to characterize firms’ inventive activities via topological data analysis (TDA) that represents high-dimensional data in a shape graph. Applying this method to 333 major firms’ patents in 1976–2005 reveals hitherto undocumented industry dynamics: some firms remain undifferentiated; others develop unique portfolios. Firms with unique trajectories, which we define and measure graph-theoretically as “flares” in the Mapper graph, tend to perform better. This association is statistically and economically significant, and continues to hold after we control for portfolio size, firm survivorship, and industry classification.     

How does openness influence the impact of a scholar’s research? An analysis of business scholars’ citations over their careers

How do the effects of cognitive openness and structural openness on the research impact of business scholars vary over their careers? By analysing a longitudinal sample of 35,296 scholars who published in business and management journals, we show that the cognitive openness and the structural openness of business scholars have non-linear relationships with their research impact. In particular, we found that, whereas moderate levels of cognitive openness and structural openness are desirable for increasing young scholars’ citations, a high level of cognitive openness and a low level of structural openness contribute to senior scholars’ citations. This study contributes to our understanding of different search behaviour across business scholars’ career paths and its implications for scholars’ research impact.