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Company business models are vulnerable to various contingencies in the business environment that may unexpectedly render their business logic ineffective. In particular, technological advancements, such as the Internet of things, big data, sharing economy and crowdsourcing, have enabled new forms of business models that can effectively and abruptly make traditional business models obsolete. By disrupting or even diminishing companies’ revenue streams, environmental contingencies may present a significant threat to business continuity (BC). Evaluating the resilience of business models against these contingencies should therefore be a core area of BC. However, existing BC approaches tend to focus on the continuity of the resources and processes through which a particular business model is accomplished in practice but omit the business model itself. We argue that in order for BC approaches to become holistic and strategic, business models need to become a part of the BC considerations, entailing an expansion of the scope of BC from value preservation to value creation. We propose an approach of Strategic Business Continuity Management, which consists of two parts: (1) sustaining the continuity of the company business model (value preservation) and (2) evaluating and modifying the business model (value creation). We illustrate conceptually the value creation part with an example drawn from the sharing economy. 相似文献
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《International Journal of Information Management》2016,36(3):258-270
In Canada, the healthcare system remains paper-laden, and EHR adoption by physicians lags behind many other industrial countries. Recent reviews identified individual and organizational factors as having the most important influence on EHR adoption and proposed taking a multidimensional perspective to study these adoption determinants. However, most studies have focused on physician EHR adoption measured at the individual level.ObjectivesFirst, we used a multilevel regression model to assess whether organizations' characteristics influenced physician behavioral intention to use EHR. Second, we sought to identify individual and organizational factors that explain physician intention.MethodsWe conducted a prospective cross-sectional study among physicians in 49 primary healthcare organizations in four regions of the province of Quebec (Canada). We first analyzed relationships between individual and organizational variables and intention. Second, we performed multilevel modeling to explore organizational characteristics' impact on physician intention to use EHR.Results278 completed questionnaires were returned from the 31 organizations that had at least 5 participants (response rate: 39.8%). Questionnaires showed satisfactory psychometric properties. The multilevel modeling found no significant overall influence of organizational level on physician intention to use EHR. Second, six of the individual level constructs had a positive and strongly significant impact on physician intention.ConclusionIn the Quebec context, organization-level seems to have no significant impact on EHR adoption by physicians. Hence, particular strategies are more likely to succeed if they target individual physicians rather than organizations. 相似文献
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《Journal of Informetrics》2014,8(3):776-790
This study proposes a temporal analysis method to utilize heterogeneous resources such as papers, patents, and web news articles in an integrated manner. We analyzed the time gap phenomena between three resources and two academic areas by conducting text mining-based content analysis. To this end, a topic modeling technique, Latent Dirichlet Allocation (LDA) was used to estimate the optimal time gaps among three resources (papers, patents, and web news articles) in two research domains. The contributions of this study are summarized as follows: firstly, we propose a new temporal analysis method to understand the content characteristics and trends of heterogeneous multiple resources in an integrated manner. We applied it to measure the exact time intervals between academic areas by understanding the time gap phenomena. The results of temporal analysis showed that the resources of the medical field had more up-to-date property than those of the computer field, and thus prompter disclosure to the public. Secondly, we adopted a power-law exponent measurement and content analysis to evaluate the proposed method. With the proposed method, we demonstrate how to analyze heterogeneous resources more precisely and comprehensively. 相似文献
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The increasing need to compete in innovation and the prevalence of IT in social and economic interactions have led to greater globalization in innovation sourcing, particularly through online crowdsourcing platforms. Crowdsourcing platform participation, a phenomenon inadequately covered, is an instance of providing an innovative solution or idea intertwined with personal and social factors that interact to result in a behavior. A better understanding of the impact of social factors and participants’ hedonic, utilitarian, and social motivations can guide the design and management of these crowdsourcing platforms to foster sustained engagement. This study considered the competitive and social nature of these platforms and analyzed participation intentions from a novel standpoint—a combination of motivational and socio-cognitive perspectives and their relationships within two different types of crowdsourcing platforms: Atizo’s third-party-hosted community and Nokia’s brand-hosted IdeasProject community. A comparison of these two types of crowdsourcing platforms for the same activity of ideation at an individual level revealed differences in behavior determinants based on the platform host type, domain specificity, and mechanisms supporting different motives and social factors. 相似文献
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程序设计类课程是工科专业重要的基础课程,介绍了本课程小组依托我院本科实验室平台,提升程序设计系列课程质量的一些探索改革的方法及经验。 相似文献
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《Journal of Informetrics》2019,13(2):485-499
With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics, and bibliometrics establish quantified analysis methods and measurements for evaluating scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citation count prediction model, we employed artificial neural network which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method outperforms state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations. 相似文献
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《Journal of Informetrics》2020,14(2):101018
Research and development activities are regarded as one of the most influencing factors of the future of a country. Large investments in research can yield a tremendous outcome in terms of a country’s overall wealth and strength. However, public financial resources of countries are often limited which calls for a wise and targeted investment. Scientific publications are considered as one of the main outputs of research investment. Although the general trend of scientific publications is increasing, a detailed analysis is required to monitor the research trends and assess whether they are in line with the top research priorities of the country. Such focused monitoring can shed light on scientific activities evolution as well as the formation of new research areas, thus helping governments to adjust priorities, if required. But monitoring the output of the funded research manually is not only very expensive and difficult, it is also subjective. Using structural topic models, in this paper we evaluated the trends in academic research performed by federally funded Canadian researchers during the time-frame of 2000–2018, covering more than 140,000 research publications. The proposed approach makes it possible to objectively and systematically monitor research projects, or any other set of documents related to research activities such as funding proposals, at large-scale. Our results confirm the accordance between the performed federally funded research projects and the top research priorities of Canada. 相似文献
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《Journal of Informetrics》2020,14(2):101009
Network science has been extensively explored in solving various bibliometrics tasks such as Co-authorship prediction, Author classification, Author clustering, Author ranking, Paper ranking, etc. While majority of the past studies exploit homogeneous bibliographic network (consists of singular type of nodes and edges), in recent past there is a surge in using heterogeneous bibliographic entities and their inter-dependencies using heterogeneous information networks (HIN). Unlike homogeneous bibliographic networks, a bibliographic HIN consists of multi-typed nodes such as Author, Paper, Venue, etc. and corresponding relations. Thus bibliographic HIN is more complex and captures rich semantics of underlying bibliographic data as well as poses more challenges. Since a real-world HIN may have different number of instances for different node types, class imbalance is ubiquitous. Recent studies discuss class imbalance in brief and exploit meta-path-based strategies to address the issue. However, there is no work which quantitatively study the effect of class imbalance in regards to solving real-world bibliometrics tasks. Therefore, this paper first proposes a metric to estimate class imbalance in HIN and study the effects of class imbalance over two bibliometrics tasks, namely (i) Co-authorship prediction and (ii) Author's research area classification, using node features generated by network embedding-based frameworks for DBLP dataset. From various experimental analysis, it is evident that class imbalance in bibliographic HIN is an inherent characteristic and for better performance of the above-mentioned bibliometrics tasks, the bibliographic HINs must consider Author, Paper, and Venue as node types. 相似文献
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《Journal of Informetrics》2020,14(4):101072
This work analyzes the variation over time of the effect of geographic distance on knowledge flows. The flows are measured through the citations exchanged between scientific publications, including and excluding self-citations. To calculate geographic distances between citing and cited publication, each publication is associated with a “prevailing” territory, according to the authors’ affiliations. We then apply a gravity model to account for the research size of the territories, in terms of cognitive proximity of citing-cited publications. The field of observation is the 2010–2017 world publications citing the 2010–2012 Italian publications, as indexed in the Web of Science. The results show that in domestic knowledge flows, geographic proximity remains an influential factor through time, although with differences among disciplines and trends of attenuating effects. Finally, we replicate the analyses of knowledge flows but with the exclusion of self-citations: in this manner the effect of geographic proximity seems reduced, particularly at the national scale, but the differences (with vs without self-citations) lessen through time. As shown in previous works, the effect of distance on continental flows is modest (imperceptible for intercontinental flows), yet here too time has some influence, including concerning exclusion of self-citations. 相似文献