Dataset Information

Innovation indicators based on firm websites-Which website characteristics predict firm-level innovation activity?

ABSTRACT: Web-based innovation indicators may provide new insights into firm-level innovation activities. However, little is known yet about the accuracy and relevance of web-based information for measuring innovation. In this study, we use data on 4,487 firms from the Mannheim Innovation Panel (MIP) 2019, the German contribution to the European Community Innovation Survey (CIS), to analyze which website characteristics perform as predictors of innovation activity at the firm level. Website characteristics are measured by several data mining methods and are used as features in different Random Forest classification models that are compared against each other. Our results show that the most relevant website characteristics are textual content, the use of English language, the number of subpages and the amount of characters on a website. In our main analysis, models using all website characteristics jointly yield AUC values of up to 0.75 and increase accuracy scores by up to 18 percentage points compared to a baseline prediction based on the sample mean. Moreover, predictions with website characteristics significantly differ from baseline predictions according to a McNemar test. Results also indicate a better performance for the prediction of product innovators and firms with innovation expenditures than for the prediction of process innovators.

SUBMITTER: Axenbeck J

PROVIDER: S-EPMC8021193 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The digital transformation of firms plays an increasingly important role in the economy and society. However, limited access to data on firm-level digital intensity is an impediment to advancement of multiple research projects concerned with firm digitalization. To alleviate this challenge, this paper proposes a method for estimating firm-level digital intensity based on other more readily available firm-level data and reference data on digitalization, which is available on sector-level. The proposed method utilizes firm-level revenue breakdown by sector to estimate sector revenue-weighted digital intensity scores, which lead to classification of firms into low, medium and high digital intensity groups. The output from the proposed method can be directly used in research concerned with firm digitalization and investigating this multifaceted phenomenon. Results from the application of the proposed method to an illustrative sample of large US and non-US firms (2000 observations in total) indicate that firm-level digital intensity can be efficiently estimated for large samples using data commonly available to researchers. The key differences between the proposed method and alternative methods are:•Recognition of the fact that firms might participate in more than one sector or industry, which partially explains within-sector heterogeneity in firm-level digital intensity. We found that 67.8% of large US firms and 78.6% of large non-US firms were engaged in more than one industry.•Use of reference sector-level digital intensity scores, which allows for rapid update, application across geographies and time, as well as parallel calculation of multiple digital intensity scores for each reference data. Furthermore, use of reference data enables supplementation of firm-level data on digitalization.•Replicability of the method and reproducibility of the results through inclusion of the source code and availability of data through research and commercial databases.

Project description:This study applies the theory of planned behaviors to evaluate economic outcomes resulting from planned innovation and dynamic entrepreneurship of Vietnamese firms. The analysis uses data on Vietnamese small and medium manufacturing firms from surveys conducted by United Nations University World Institute for Development Economics Research (UNU-WIDER) during 2005-2015. Employing various estimation techniques including normal standard one-side regressions (fixed effect models, panel robust model, and Pds-lasso) and two-side structural two-stage models (extended regression model, treatment effect model, and IV-Lasso), we analyze the impacts of innovation activities on firm profitability in connection to the role of dynamic entrepreneurship and planned innovation. We measure planned innovation by the interaction between intention to innovate and firm innovation activities. The study shows that planned innovation is associated with higher profitability for firms. This holds true for all three innovation activities including introduction of a new product, introduction of a new production process and improvements to existing products/processes. In light of the theory of planned behaviors, entrepreneurial intentions embedded in planned innovation can underlie a comprehensive plan and action that drives the innovation process. The findings suggest that for small and medium enterprises (SMEs) to thrive, willingness to pursue innovation by the firm owners is key to success as the intention to innovate will enable firms to gain a planning advantage. This advantage leads to a better resource allocation within the firms, shaping more effective strategies to implement a planned innovation. Overall, the study provides an important implication for the introduction of support schemes that promote innovation for SMEs in Vietnam. Any support schemes, introduced either by the public or private sector to target SMEs, should be engaged with the group of dynamic entrepreneurs who have intentions to innovate to warrant a higher chance of success.

Project description:ObjectiveTo establish the validity of sensor-based measures of work processes for predicting perceived mental and physical exertion of critical care nurses.Materials and methodsRepeated measures mixed-methods study in a surgical intensive care unit. Wearable and environmental sensors captured work process data. Nurses rated their mental (ME) and physical exertion (PE) for each four-hour block, and recorded patient and staffing-level workload factors. Shift was the grouping variable in multilevel modeling where sensor-based measures were used to predict nursing perceptions of exertion.ResultsThere were 356 work hours from 89 four-hour shift segments across 35 bedside nursing shifts. In final models, sensor-based data accounted for 73% of between-shift, and 5% of within-shift variance in ME; and 55% of between-shift, and 55% of within-shift variance in PE. Significant predictors of ME were patient room noise (ß = 0.30, p < .01), the interaction between time spent and activity levels outside main work areas (ß = 2.24, p < .01), and the interaction between the number of patients on an insulin drip and the burstiness of speaking (ß = 0.19, p < .05). Significant predictors of PE were environmental service area noise (ß = 0.18, p < .05), and interactions between: entropy and burstiness of physical transitions (ß = 0.22, p < .01), time speaking outside main work areas and time at nursing stations (ß = 0.37, p < .001), service area noise and time walking in patient rooms (ß = -0.19, p < .05), and average patient load and nursing station speaking volume (ß = 0.30, p < .05).DiscussionAnalysis yielded highly predictive models of critical care nursing workload that generated insights into workflow and work design. Future work should focus on tighter connections to psychometric test development methods and expansion to a broader variety of settings and professional roles.ConclusionsSensor-based measures are predictive of perceived exertion, and are viable complements to traditional task demand measures of workload.

Project description:Background/objectiveQuality innovation networks' (QINs') support of nursing homes (NHs) is a national strategy to systematically improve the quality of care experienced by residents. QINs have been tasked with providing NHs with information, resources, tools, and training to assist in developing best practices and to support quality improvement efforts in infection prevention (including joining the National Healthcare Safety Network [NHSN]), avoid unnecessary hospitalizations, and increase use of hospice and palliative care. Our objective was to examine QIN online resources provided to NHs to support best practices and improvement efforts.DesignAn environmental scan was conducted.Setting/measurementEach QIN website was evaluated on (1) usability, (2) accessibility and prominence, (3) website design, (4) availability of training materials, (5) recency of update, (6) identification of key personnel, and (8) quality focus areas (ie, infection prevention, NHSN, antibiotic stewardship, reducing unnecessary or avoidable hospitalizations, and palliative and hospice care).ResultsQIN websites varied dramatically in design and resources offered to NHs as well as in the content and ease of finding information. Antibiotic stewardship and NHSN resources were widely available. Information (ie, fact sheets) on reducing avoidable hospitalizations was commonly available, while resources, such as tool kits, webinars, training, and contact information for personnel on reducing avoidable hospitalizations, were available to 23 states. Infection prevention resources were varied and limited to 34 states. Both palliative care and hospice resources were available through only a few QINs (13 states and 20 states, respectively).ConclusionsGiven that much of the information, tool kits, and resources are standardized and in the public domain, centralized resources with tailored or specialized links to unique local resources, like in-person trainings and state-specific contact information, could be more beneficial for NHs. J Am Geriatr Soc 67:2376-2381, 2019.

Dataset Information

Innovation indicators based on firm websites-Which website characteristics predict firm-level innovation activity?

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets