Estimating Reputation Polarity on Microblog Posts

In reputation management, knowing what impact a tweet has on the reputation of a brand or company is crucial. The reputation polarity of a tweet is a measure of how the tweet influences the reputation of a brand or company. We consider the task of automatically determining the reputation polarity of a tweet. For this classification task, we propose a feature-based model based on three dimensions: the source of the tweet, the contents of the tweet and the reception of the tweet, i.e., how the tweet is being perceived. For evaluation purposes, we make use of the RepLab 2012 and 2013 datasets. We study and contrast three training scenarios. The first is independent of the entity whose reputation is being managed, the second depends on the entity at stake, but has over 90% fewer training samples per model, on average. The third is dependent on the domain of the entities. We find that reputation polarity is different from sentiment and that having less but entity-dependent training data is significantly more effective for predicting the reputation polarity of a tweet than an entity-independent training scenario. Features related to the reception of a tweet perform significantly better than most other features.     

AI recruitment algorithms and the dehumanization problem

According to a recent survey by the HR Research Institute, as the presence of artificial intelligence (AI) becomes increasingly common in the workplace, HR professionals are worried that the use of recruitment algorithms will lead to a “dehumanization” of the hiring process. Our main goals in this paper are threefold: (i) to bring attention to this neglected issue, (ii) to clarify what exactly this concern about dehumanization might amount to, and (iii) to sketch an argument for why dehumanizing the hiring process is ethically suspect. After distinguishing the use of the term “dehumanization” in this context (i.e. removing the human presence) from its more common meaning in the interdisciplinary field of dehumanization studies (i.e. conceiving of other humans as subhuman), we argue that the use of hiring algorithms may negatively impact the employee-employer relationship. We argue that there are good independent reasons to accept a substantive employee-employer relationship, as well as an applicant-employer relationship, both of which are consistent with a stakeholder theory of corporate obligations. We further argue that dehumanizing the hiring process may negatively impact these relationships because of the difference between the values of human recruiters and the values embedded in recruitment algorithms. Drawing on Nguyen’s (in: Lackey, Applied Epistemology, Oxford University Press, 2021) critique of how Twitter “gamifies communication”, we argue that replacing human recruiters with algorithms imports artificial values into the hiring process. We close by briefly considering some ways to potentially mitigate the problems posed by recruitment algorithms, along with the possibility that some difficult trade-offs will need to be made.

     

Assessing the effects of consumers’ product evaluations and trust on repurchase intention in e-commerce environments

This study enhances the existing literature on online trust by integrating the consumers’ product evaluations model and technology adoption model in e-commerce environments. In this study, we investigate how perceived value influences the perceptions of online trust among online buyers and their willingness to repurchase from the same website. This study proposes a research model that compares the relative importance of perceived value and online trust to perceived usefulness in influencing consumers’ repurchase intention. The proposed model is tested using data collected from online consumers of e-commerce. The findings show that although trust and e-commerce adoption components are critical in influencing repurchase intention, product evaluation factors are also important in determining repurchase intention. Perceived quality is influenced by the perceptions of competitive price and website reputation, which in turn influences perceived value; and perceived value, website reputation, and perceived risk influence online trust, which in turn influence repurchase intention. The findings also indicate that the effect of perceived usefulness on repurchase intention is not significant whereas perceived value and online trust are the major determinants of repurchase intention. Major theoretical contributions and practical implications are discussed.     

Workers’ replacements and firms’ innovation dynamics: New evidence from Italian matched longitudinal data

In this paper, we explore the impact of a firm's workers’ replacements on innovation performance by using rich matched employer–employee panel data for the Veneto region of Italy. We take the well-known resource-based theory of the firm as our departure point, and develop a set of hypotheses which we test empirically with negative binomial regressions. We find that workers’ replacements significantly dampen innovation performance, coherently with the idea that they generate losses in the tacit knowledge base of the firm. We also find that workers’ replacements are especially detrimental to large and young firms, possibly because large companies benefit comparatively less from ‘diaspora’ effects and because innovative capabilities in young firms are mostly dependent on specific human capital. Finally, our results show that firms’ location in industrial districts significantly mitigates the negative impact of workers’ replacements, and that a similar picture emerges when firms are more exposed to knowledge spillovers, particularly of related knowledge.     

Human capital and innovation: the importance of the optimal organizational task structure

Management literature has identified high-skilled human capital as a crucial dimension of innovation processes at the firm level. In this study, we introduce an alternative view of human capital based on the tasks that firms’ workers perform. We propose a measure of cognitive analytical and interpersonal tasks: the degree of abstractism. We argue that the level of abstractism of a firm has an effect on a firm's propensity to innovate and on its product innovation performance. We hypothesize that while the degree of abstractism has a linear positive relationship with the propensity to innovate, the relationship between abstractism and product innovation performance follows an inverted u-shaped relationship. We find partial support to our hypotheses using data from more than six thousand Portuguese firms. We discuss how these results change our understanding of the relationship between human capital and innovation at the firm level.     

Public reaction to information release for crisis discourse by organization: Integration of online comments

This study explores how online comments influence public reactions to organizational crisis discourses based on five scenarios depicting typical online comment patterns. Through an empirical study involving 621 participants, several key findings are obtained. First, online comments have a significant effect on post-crisis reactions involving emotions, attitudes and behavioral intentions. However, except in the case of perceived comment manipulation, public reaction patterns associated with overwhelmingly positive and non-overwhelmingly positive comments are not significantly different and are friendlier to the crisis organization than the other three patterns. Moreover, reactions associated with the pattern in which there are no online comments are quite similar to those associated with the pattern of non-overwhelmingly negative comments. Second, positive emotions enhance perceived organizational integrity, whereas negative emotions reduce this perception. Third, perceived comment manipulation has a negative moderating effect on the relationship between positive emotion and perceived organizational integrity and a positive moderating effect on the relationship between negative emotion and perceived organizational integrity. Finally, emotions and perceived organizational integrity are found to be predictors of loyalty and boycott intentions. The findings of the study extend the present knowledge on crisis communication by highlighting the impact of online comments on the post-crisis reactions of the public and also provide crisis managers with guidelines on how to deploy appropriate crisis communication strategies.     

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.     

Differential influences of perceived organizational factors on younger employees’ participation in offline and online intergenerational knowledge transfer

Intergenerational supportive climate, top management support, organizational institution are seen as three types of important organizational factors for intergenerational knowledge transfer (IGKT), however, current studies are qualitative with little empirical evidence, further, the interrelations of them are little known. This paper investigates and verifies their relationships by an empirical study, especially focuses on their differential effects on younger employees’ participation in IGKT using offline versus online communication methods drawing upon social influence theory. A survey with younger doctors was conducted in the medical industry to test the research model proposed in this study. Results show that younger employees’ perceived intergenerational supportive climate (PISC) has a significant positive influence on offline IGKT, but not on online IGKT; perceived top management support (PTMS) has a significant positive influence on offline IGKT, but has a negative impact on online IGKT; perceived organizational institution support (POIS) has a significant positive influence on online IGKT, but not on offline IGKT. These findings contribute to a more comprehensive understanding about IGKT, as well as help managers be more effective to enhance younger employees’ participation in IGKT through offline/online methods, which contribute to offline and online intergenerational knowledge transfer to knowledge management.     

Inducing knowledge sharing in teams through cost-efficient compensation schemes

A natural challenge for firms seeking to implement knowledge management systems is accounting for the interplay of incentives among workers and supervisors. This paper studies this interaction, focusing on the dynamics arising from joint projects, task evaluation, and knowledge sharing, in the framework of a game-theoretic model. We identify conditions under which a firm can minimize the costs of implementing an incentives-based system for knowledge sharing, taking into account the firm’s ability to monitor individual performance. We show that an employer in our framework has a stronger incentive to monitor tasks when relative-performance pay is small and when higher-paid agents are more likely to generate new knowledge.     

Relationships among community interaction characteristics,perceived benefits,community commitment,and oppositional brand loyalty in online brand communities

To stand up for the brands they support, members of brand communities develop “oppositional brand loyalty” towards other rival brands. This study identifies how the interaction characteristics of brand community affect the perceived benefits of community members, and whether the perceived benefits cause members to develop community commitment, as well as the relationship between community commitment and oppositional brand loyalty. This study examined members of online automobile communities in Taiwan, and obtained a total of 283 valid samples. The analytical results reveal that interaction characteristics of brand community make members perceive many benefits, with “brand community engagement” being the most noticeable. Furthermore, hedonic, social, and learning benefits are the main factors to form community commitments. When members have community commitments, they will form oppositional brand loyalty to other rival brands. Based on the analytical results, this study provides suggestions to enterprises regarding online brand community operations.     

Investigating the role of in-situ user expectations in Web search

Pre-adoption expectations often serve as an implicit reference point in users’ evaluation of information systems and are closely associated with their goals of interactions, behaviors, and overall satisfaction. Despite the empirically confirmed impacts, users’ search expectations and their connections to tasks, users, search experiences, and behaviors have been scarcely studied in the context of online information search. To address the gap, we collected 116 sessions from 60 participants in a controlled-lab Web search study and gathered direct feedback on their in-situ expected information gains (e.g., number of useful pages) and expected search efforts (e.g., clicks and dwell time) under each query during search sessions. Our study aims to examine (1) how users’ pre-search experience, task characteristics, and in-session experience affect their current expectations and (2) how user expectations are correlated with search behaviors and satisfaction. Our results with both quantitative and qualitative evidence demonstrate that: (1) user expectation is significantly affected by task characteristics, previous and in-situ search experience; (2) user expectation is closely associated with users’ browsing behaviors and search satisfaction. The knowledge learned about user expectation advances our understanding of users’ search behavioral patterns and their evaluations of interaction experience and will also facilitate the design, implementation, and evaluation of expectation-aware user models, metrics, and information retrieval (IR) systems.     

Exploring the impact of online social participation on loneliness in older adults: Evidence from The Canadian Longitudinal Study on Aging

As individuals age, mental and physical impairment can emerge and impact their well-being. Previous research on social participation in older adults has typically focused on specific activities or failed to distinguish between different forms of participation. In contrast, we propose that social participation is a resource optimization process for older adults, where various forms of participation must be optimized in order to improve well-being outcomes. Drawing on Selective Optimization with Compensation theory, we develop hypotheses on how older adults with cognitive decline select and optimize their social participation profile and how multiple modes of participation synergistically affect their perceived loneliness. Using data from the Canadian Longitudinal Study on Aging (CLSA), we test a polynomial model and find that online social participation has a U-shaped effect on loneliness, while personal-oriented offline social participation mitigates this effect. We also find that the impact of social participation modes varies considerably between younger and older groups. Our findings suggest implications for the design of integrative aging support programs and the use of ICT to promote mental well-being in different stages of advanced age.     

基于课题组的农业科研绩效评价体系的构建与实证分析

本文以课题组为统计单元,就农业科研的投入与产出确定量化指标和权重,采用计量评价方法对浙江省农科院主要课题组科研产投情况进行实证分析,并探讨了课题组不同研究人数、不同研究类型、不同项目经费对科研产出影响以及科研产出对人年收入影响,初步构建并明确了适合农业科研院所的课题组绩效评价体系,提出了农业科研投入产出的主要特点与启示。     

‘I’m busy (and competitive)!’ Antecedents of knowledge sharing under pressure

This study considers the dilemma faced by employees every time a colleague requests knowledge: should they share their knowledge? We use adaptive cost theory and self-efficacy theory to examine how individual characteristics (i.e., self-efficacy and trait competitiveness) and situational perceptions (i.e., ‘busyness’ and perceived competition) affect knowledge sharing behaviours. A study was conducted with 403 students who completed a problem-solving exercise and who were permitted (but not required) to respond to requests for knowledge from people who were doing the same activity. Our results suggest that people who perceive significant time pressure are less likely to share knowledge. Trait competitiveness predicted perceived competition. This and low task self-efficacy created a sense of time pressure, which in turn led to people feeling ‘too busy’ to share their knowledge when it was requested. Perceived competition was not directly related to knowledge sharing. Implications for research and practitioners are discussed.     

On the class separability of contextual embeddings representations – or “The classifier does not matter when the (text) representation is so good!”

The literature has not fully and adequately explained why contextual (e.g., BERT-based) representations are so successful to improve the effectiveness of some Natural Language Processing tasks, especially Automatic Text Classifications (ATC). In this article, we evince that such representations, when properly tuned to a target domain, produce an extremely separable space that makes the classification task very effective, independently of the classifier employed for solving the ATC task. To demonstrate our hypothesis, we perform a thorough class separability analysis in order to visualize and measure how well BERT-based embeddings separate documents of different classes in comparison with other widely used representation approaches, e.g., TFIDF BoW, static embeddings (e.g., fastText) and zero-shot (non-tuned) contextual embeddings. We also analyze separability in the context of transfer learning and compare BERT-based representations with those obtained from other transformers (e.g., RoBERTa, XLNET). Our experiments covering sixteen datasets in topic and sentiment classification, eight classification methods and three class separability metrics show that the fine-tuned BERT embeddings are highly separable in the corresponding space (e.g., they are 67% more separable than the static embeddings). As a consequence, they allow the simplest classifiers to achieve similar effectiveness as the most complex methods. We also find moderate to high correlations between separability and effectiveness in all experimented scenarios. Overall, our main finding is that more discriminative (i.e., separable) textual representations constitute a critical part of the ATC solutions that, given the current state-of-the-art in classification algorithms, are more prominent than the algorithmic (classifier) method for solving the task.     

Controlling contractual exchange risks in R&D interfirm cooperation: an empirical study

Suppliers and buyers of R&D results perceive two exchange risks: first, the risk to achieve a lower profitability on the innovation return than the exchange partner, second, the risk of the partner becoming a competitor by unplanned, one-sided knowledge flows. Both risks motivate opportunistic behaviour. The paper deals with the reduction of the perceived supplier risks. We analyse how an option on later negotiation of an additional continuous innovation return sharing which is based on contractual hostages can lower the perceived exchange risks. An empirical study examines how effectively these hostages lower the perceived risks.     

A case analysis of knowledge sharing implementation and job searching in Malaysia

The aim of this research is to consider an analysis through case studies on how knowledge sharing may be successfully implemented. The study will clarify a conceptualisation that provides a new understanding of the relationship between unemployed graduates and the human capital concept. The cases adopted web documentation for the research technique, and an interpretive approach was used as the research paradigm. Two online recruitment agency sites were analysed through qualitative analysis. The connections show how knowledge sharing can be used as a medium to help unemployed graduates to get jobs through online recruitment agencies. High quality candidates must have good soft skills, problem-solving skills and employable value added skills to get the best jobs. The findings will be expressed as qualitative results from the two online recruitment agencies researched as the case studies for the paper. From these case studies, the findings will contribute to the ongoing study on how to implement knowledge sharing among undergraduates after they finished their studies.     

Is this time different? How digitalization influences job creation and destruction

With the process of digitalization now in full swing, many are wondering how the adoption of new technologies influences job creation and destruction. Much hinges upon the specific tasks that machines take on and how many new tasks are created through the adoption of new digital technologies. Some argue that most tasks that are at risk of automation are those performed by rather low- to medium-skilled employees, while most new tasks that emerge from the adoption of digital technologies complement high-skilled labor. We present evidence derived from representative survey data from Switzerland that is consistent with this view. Specifically, we find that increased investment in digitalization is associated with increased employment of high-skilled workers and reduced employment of low-skilled workers, with a slightly positive net effect. The main effects are almost entirely driven by firms that employ machine-based digital technologies, e.g. robots, 3D printing or the Internet of Things. We do not find any significant employment effects when non-machine-based digital technologies are considered, e.g. ERP, e-commerce or cooperation support systems.     

How much has my co-worker contributed? The impact of anonymity and feedback on social loafing in asynchronous virtual collaboration

This paper reports an experiment that investigates how feedback about co-worker's performance and anonymity affect a team member's performance in an idea generation task conducted asynchronously. The results indicated that there was no main effect of anonymity: anonymity by itself did not affect group performance. Receiving information about co-worker's performance increased group performance; however, the perceived level of co-worker's performance did not affect group performance. Participants who perceived that their co-workers had high performance experienced significantly more social comparison with identified input than with anonymous input. However, this motivation gain did not translate into a performance gain. Implications of these results and future research are discussed.     

Sector heterogeneity and dynamic effects of innovation subsidies: Evidence from Horizon 2020

We evaluate the effect of a pan-European innovation funding program on firm growth and innovative output. Using a difference-in-differences estimation on a sample of matched firms, we find that subsidized firms are able to invest more in tangible and intangible assets, achieve higher growth of turnover and employment, and file more patent applications. We then analyze the dynamic treatment effect and find that the effects of subsidization tend to get stronger over time. Moreover, our findings indicate that the effect of subsidization is highly heterogeneous across sectors with different R&D or knowledge intensity and level of competition. Finally, we explore some economic channels to explain how subsidies generate strong effects on firm performance. We show that subsidized firms are able to generate more internal financing and attract more long-term borrowing after receiving the subsidy, yet we find no evidence that subsidized firms are able to attract more external equity financing than similar unsubsidized firms.