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About 10,000 papers for “Operationalization”
Stephanie Sargent Weaver · The International Encyclopedia of Communication · 2015
Operationalization is the process of translating abstract things into concrete, measurable variables (→ Measurement Theory). It is how researchers define things by what they do. In other words, the “operations” are the procedures or steps one must go through in order to observe the concept being defined (Katzer et al. 1998; Babbie 2013).
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Juan B. González (12354667), Alfonso Sánchez (535704) · Figshare · 2022
<p>Descriptive statistics, coding, and corresponding questions from the Afrobarometer of all variables.</p> <p>(DOCX)</p>
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Filomena Maggino · 2023
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Duncan Cramer, Dennis Howitt · The SAGE Dictionary of Statistics · 2004
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2014
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The SAGE Dictionary of Social Research Methods · 2006
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Irene I. van Driel · The International Encyclopedia of Communication Research Methods · 2017
Operationalization describes a crucial step in the deductive research process in which conceptual definitions are translated into ones that can be measured, or operationalized . Operationalization then, literally refers to the operations executed to measure the research concepts and the connection between them in the real world. Adequate operational definitions specify what a concept is and what it is not. Sufficiently narrow, clear definitions of research concepts are needed to ensure reliability as well as validity of research endeavors. Operational definitions are reliable when operations of a study can be replicated. Validity is ensured if the definition adequately reflects the relevant research concept. Violence can, for example, be defined conceptually as harming another person intentionally. Directing violent behavior at inanimate objects then, cannot be counted as violence in an operationalization of that concept. Most research concepts can be operationalized in numerable ways. Ultimately, the researcher has to make conscious decisions about which dimensions of the concept are of relevance to the study at hand and make sure that those dimensions are included in the conceptual definition.
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Nicolai Heinz (15187723), Ann-Kathrin Koessler (12469474), Stefanie Engel (4276927) · Figshare · 2023
<p>The figure shows how the distance treatments were embedded in the overall survey for the three conditions T<sub>Close</sub>, T<sub>Far Germany</sub> and T<sub>Far India</sub>, as well as the resulting distances. (Reprinted from USGS National Map Viewer under a CC BY 4.0 license (2022): <a href="https://apps.nationalmap.gov/viewer/" target="_blank">https://apps.nationalmap.gov/viewer/</a>. The maps are very similar but not identical to the map shown to the participants and therefore only for illustrative purposes).</p>
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Nayanjyoti Paul · Apress eBooks · 2023
This is the next logical next step after the previous chapter. We have loaded the data and democratized the content. We then enabled a production playground and enabled different personas within the organization to build experiments that can provide business value. After a lot of these steps, we have our “next best idea” identified. The next best idea can be an ETL job, reporting job, machine learning pipelines, etc., which can help bring tremendous business value to the organization. So, the logical question is, how can we move the experiment in the production playground into a managed, scalable, and monitored production job that can be owned by central IT (versus the experiment team). Similarly, once the business has vetted the business outcome, how can the code get through the security and other processes to move into production?
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Cuixia Yi (16617968), Yang Tao (773493), Fu Bo (16617971) et al. · Figshare · 2023
<div><p>Given the unprecedented and profound impact on the traditional sports industry and its economic driving force brought by the “Internet +”, the purpose of this study is to explore the driving relationship between the sports industry and local economic development and deconstruct the dynamic regulation mechanism of “Internet +” on the economic spillover of the sports industry. Empirical data collected from China’s 11 inter-provincial panel data from 2015–2019 were analyzed through the fixed-effects and threshold models. The study finds that the economic spillover of the sports industry has significantly promoted regional economic development, and the regulation effect of the Internet has complex non-linear characteristics. Only when Internet development exceeds a specific threshold can it develop a benign complementary mechanism with the sports industry. The impact of “Internet +” on the threshold regulation of the sports industry economic spillover has spatial and temporal differentiation characteristics in the three dimensions of Internet hardware level, Internet penetration rate, and Internet application value. The results of this study extend the current study, indicating that it is a practical choice for local governments to develop the economy through the sports industry. Provinces and cities should enhance the Internet penetration rate, expand Internet business applications, and upgrade Internet development to a specific point to maximize the drive of positive spillover. Regional heterogeneity requires differential regulation strategies and more concentration on the middle and western regions.</p></div>
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Herman Vantrappen, Frederic Wirtz · 2023
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Dagmar Sibyl Steuwer · 2012
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Lindsey Smith Taillie (3159258), Maxime Bercholz (12072112), Carmen E. Prestemon (11404578) et al. · Figshare · 2023
<div><p>Background</p><p>Policies to reduce red meat intake are important for mitigating climate change and improving public health. We tested the impact of taxes and warning labels on red meat purchases in the United States. The main study question was, will taxes and warning labels reduce red meat purchases?</p><p>Methods and findings</p><p>We recruited 3,518 US adults to participate in a shopping task in a naturalistic online grocery store from October 18, 2021 to October 28, 2021. Participants were randomized to one of 4 conditions: control (no tax or warning labels, <i>n =</i> 887), warning labels (health and environmental warning labels appeared next to products containing red meat, <i>n =</i> 891), tax (products containing red meat were subject to a 30% price increase, <i>n</i> = 874), or combined warning labels + tax (<i>n</i> = 866). We used fractional probit and Poisson regression models to assess the co-primary outcomes, percent, and count of red meat purchases, and linear regression to assess the secondary outcomes of nutrients purchased. Most participants identified as women, consumed red meat 2 or more times per week, and reported doing all of their household’s grocery shopping. The warning, tax, and combined conditions led to lower percent of red meat–containing items purchased, with 39% (95% confidence interval (CI) [38%, 40%]) of control participants’ purchases containing red meat, compared to 36% (95% CI [35%, 37%], <i>p</i> = 0.001) of warning participants, 34% (95% CI [33%, 35%], <i>p</i> < 0.001) of tax participants, and 31% (95% CI [30%, 32%], <i>p</i> < 0.001) of combined participants. A similar pattern was observed for count of red meat items. Compared to the control, the combined condition reduced calories purchased (−312.0 kcals, 95% CI [−590.3 kcals, −33.6 kcals], <i>p</i> = 0.027), while the tax (−10.4 g, 95% CI [−18.2 g, −2.5 g], <i>p</i> = 0.01) and combined (−12.8 g, 95% CI [−20.7 g, −4.9 g], <i>p</i> = 0.001) conditions reduced saturated fat purchases; no condition affected sodium purchases. Warning labels decreased the perceived healthfulness and environmental sustainability of red meat, while taxes increased perceived cost. The main limitations were that the study differed in sociodemographic characteristics from the US population, and only about 30% to 40% of the US population shops for groceries online.</p><p>Conclusions</p><p>Warning labels and taxes reduced red meat purchases in a naturalistic online grocery store.</p><p><b>Trial Registration:</b><a href="http://www.clinicaltrials.gov/" target="_blank">http://www.clinicaltrials.gov/</a><a href="https://clinicaltrials.gov/ct2/show/NCT04716010" target="_blank">NCT04716010</a>.</p></div>
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Chunyu Li (437902), Thanawan Phongsatha (14203746) · Figshare · 2022
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Chima, Emmanuel Alinafe Ronald · Michigan State University Libraries · 2025
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Mina Nikolić (22145815), Dragan Bjelica (22145818) · Figshare · 2025
<div> As organizations navigate an increasingly dynamic digital landscape, the challenge of achieving consistent product success has intensified. This study investigates how key management factors—customer-driven product development, open innovation networks, organizational digital agility, and AI-integrated project management—influence digital product outcomes. Special attention is given to the dual role of artificial intelligence: as a potential enabler of innovation and a possible constraint when applied in rigid or misaligned ways. A quantitative survey was conducted among 239 professionals engaged in product-related roles across diverse industries and regions. Data were analyzed using linear regression, moderation analysis, and non-parametric testing to assess both direct and interaction effects among the variables. The results reveal that customer-driven product development, open innovation networks, and organizational digital agility each have a statistically significant positive impact on product success, with customer-driven development emerging as the strongest predictor. In contrast, AI-integrated project management does not demonstrate a significant direct effect. Notably, AI negatively moderates the relationship between open innovation networks and product success, suggesting that while AI may enhance structured knowledge-sharing, it can also diminish the creative and collaborative elements essential for innovation if not carefully managed. These findings highlight the strategic complexity of integrating AI into digital product development. While AI can enhance operational efficiency and knowledge flows, its impact on innovation outcomes is context-dependent and may disrupt the balance between human creativity and automated decision-making. The study underscores the need for hybrid models in which AI complements—not replaces—human expertise. Insights from this research offer valuable guidance for organizations aiming to design resilient, customer-centric, and innovation-driven digital product strategies in an AI-enhanced environment. </div>
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Per Davidsson · International studies in entrepreneurship · 2016
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Michael Sengl · Nomos Verlagsgesellschaft mbH & Co. KG eBooks · 2025
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Oxford University Press eBooks · 2023
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Scott Morgenstern · 2017
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