Florida Region Research Articles

Strategic Agility in the SME: Use It Before You Lose It

This empirical study investigates strategic agility and its relationships with firm age, firm size, and firm performance in SMEs. The Doz and Kosonen three-factor model of strategic agility is operationalized and tested in 30 firms from multiple industries located in the Space Coast region of Florida. It is found that strategic agility decreases as firms grow older but not as firms grow larger. Strategic agility and firm performance are also found to be related as moderated by environmental turbulence. Specifically, performance increases with strategic agility in high turbulence but decreases with strategic agility in low turbulence. This finding is consistent with the view that dynamic capabilities like strategic agility bear a cost which may be unnecessary in stable environments. Overall, the study suggests that SMEs may benefit from strategic agility if it used while they still have it, that is, when they are young.

Street-Race in Reproductive Health: A Qualitative Study of the Pregnancy and Birthing Experiences among Black and Afro-Latina Women in South Florida.

The lived-experience of race and racism among historically vulnerable populations needs critical examination in the United States' (US)maternal and reproductive healthcare system. This study aims to measure how street-race influences the experiences of pregnancy and birthing among Black and Afro-Latina women in the South Florida region. Street-race, in this study, focuses on the lived experiences of phenotype and colorism (as real consequences due to structural racism) imposed on women during clinical encounters. Twenty-five women who self-reported their racial and street-identity as Black and / or Afro-Latina were interviewed, utilizing the qualitative approaches of narrative medicine and phenomenology. Study participants also had to have at least one recent pregnancy or birth of a living child (< 24months). Adverse maternal mortality outcomes also do not tell the full story of what it means to be Black while giving birth in North America. The dominance of White-presenting Latinx individuals in South Florida (a seemingly "diverse" urban region) is also historical and persistent. These oppositional consequences at the clinical encounter, regarding street-race, further illustrate how White-dominance (via phenotype and skin color) are ever-present across US healthcare institutions serving pregnant women. The lived experience of hegemonic racial hierarchies (e.g., Black and White), ongoingly contributes to the racialization of maternal and reproductive healthcare in the US, as more attention is needed to achieve health equity.

Displacement of Aedes albopictus by Aedes aegypti in Gainesville, Florida.

Aedes aegypti and Ae. albopictus are invasive mosquitoes, capable of vectoring arboviruses such as dengue, chikungunya, yellow fever, and Zika. Recent shifts in spatial distribution indicate there is a resurgence of Ae. aegypti in certain regions of Florida. After a 26-year absence, Ae. aegypti larvae were collected in a downtown neighborhood in Gainesville, Florida, in November 2019. Subsequent surveys confirmed that Ae. albopictus was completely displaced by Ae. aegypti in this neighborhood, whereas Ae. albopictus and Ae. aegypti coexisted around this community focus, and Ae. albopictus alone has been found elsewhere in the city and county since the 1990s. Field surveys revealed that Ae. aegypti is resurging in the downtown area of Gainesville and is actively dispersing to adjacent neighborhoods. Thus, Ae. aegypti could potentially replace Ae. albopictus across more of urban Gainesville in north-central Florida, as reported recently in coastal cities of northeastern Florida.

Coastal Ecosystem Vulnerability and Sea Level Rise (SLR) in South Florida: A Mangrove Transition Projection

We used static, elevation and land cover data to estimate sea level rise impacts (SLR) to urban, developed lands and coastal wetland systems in Everglades National Park and the East and West coastal regions in South Florida. Maps and data tables estimating potential state change to open water were compiled through overlay analysis of elevation, land cover, and SLR masks with future land cover projected using a land cover transition threshold model. Analysis was based on a 2–5-km-wide longitudinal band along the SW and SE coasts of Florida where sea-level rise has no surface impediments to inundation and will likely cause coastline transgression and wetland migration. Analysis used three different projections; 0.27 m (0.9 ft), 0.76 m (2.5 ft) and 1.13 m (3.7 ft) greater than current sea level by 2070 estimated by NOAA and IPCC. Under a 0.27 m SLR projection 51% of the coastal land cover may be impacted. Under 0.76 and 1.13 m projected SLR, coastal land cover areas were impacted by 56.5 and 59.1%, respectively. Migration of coastal wetlands from their current location into more inland areas in response to increased water depths and as a function of empirically derived marsh and mangrove accretion rates were also evaluated. With a SLR of 0.76 m by 2070, without accretion, 1,160 sq km of wetland became open estuarine water. However, with accretion values of 0.211 m (4.1 mm yr–1) and 0.55 m (11 mm yr–1) by 2070, there was a transition of wetland cover to open estuarine water of only 349 and 41 sq km, respectively. Under a low SLR of 0.27 m by 2070 scenario with accretion, the coastal mangroves were able to migrate inland while maintaining the current coastline. It was only under the more extreme scenario of 1.13 m SLR by 2070 that accretion was not able to compensate for inundation and there was a loss of wetland coastline everywhere.

Natural language processing (NLP) and machine learning (ML) model for predicting CMS OP-35 categories among patients receiving chemotherapy.

e13591 Background: The Hospital Outpatient Quality Reporting Program is a pay-for-quality data reporting program implemented by the Centers for Medicare &amp; Medicaid Services (CMS). Hospitals collect data on various measures of the quality of care provided in outpatient settings for the CMS. One such measure is OP-35, where data about patients who received chemotherapy in outpatient settings are collected. Such quality measures help hospitals assess their performance and allow patients to compare the quality of care among different hospitals in that region. Currently, the process to label data for OP-35 categories is manual. This study aims to develop a model using NLP and ML to predict the ten OP-35 complication categories and automate the process. Methods: Data from 1000 adult cancer patients who received chemotherapy at a comprehensive cancer center in the South Florida region between Sept and Oct 2019 were extracted to train the ML models. Text from the Chief Complaint field was manually labeled into ten binary categories: anemia, nausea, dehydration, neutropenia, diarrhea, emesis, pneumonia, fever, sepsis, and pain. The data were divided into a training set (80%) and a test set (20%). After initial pre-processing of the text, term frequency–inverse document frequency (TF-IDF) feature extraction method with a vocabulary size of 10,000 was applied. Various models (stochastic gradient descent, support vector classification [SVC], and binary relevance, etc.) were trained to predict multiple labels. These models were evaluated using Jaccard score, accuracy, F1 score, and Hamming loss. Additionally, two deep learning approaches: a single dense output layer and multiple dense output layer models, were also used for comparison. Python version 3.8 was utilized for the analysis. Results: The best performing model was SVC, with a Jaccard score of 85.13 and 90% accuracy. In the first deep learning approach, a single dense output layer was used with multiple neurons where each neuron represented only one label. In the second approach, a separate dense layer for each label was created with one neuron. The model with a single output layer produced an accuracy score of 32%, and the model with multiple output layer had an accuracy score of 31%. Both deep learning models with single and multiple output layers did not perform well compared to SVC. Conclusions: Our study shows an early indication regarding the feasibility of modern ML techniques in predicting multiple label categories or outcomes. As a potential clinical decision support system, this model could replace manual data entry, minimize human error, and decrease resources for data collection. In the next stage, healthcare providers will validate this model by manually checking the predicted labels. In the final stage, model will be deployed in real-time to predict OP-35 categories automatically.

Exploring analytical, simulation-based, and hybrid model structures for multivariate crash frequency modeling

In safety literature, there are two ways to incorporate the potential correlation between multiple crash frequency variables: (1) simulation-based approach and (2) analytical closed-form approach. The current research effort undertakes a comparison between simulation-based multivariate model and copula based closed-form approach to analyze zonal level crash counts for different crash types. Further, the research builds on earlier copula based models by incorporating random parameters thus proposing a hybrid (combination of analytical and simulation based system) approach to incorporating unobserved heterogeneity. Within the proposed hybrid copula model, the empirical analysis involves estimation of count models using four different copula structures which cover a wide range of dependency structures, including radial symmetry and asymmetry, and asymptotic tail independence and dependence. Further, to the best of authors’ knowledge, this study is the first of its kind to incorporate attribute variability (random parameters) effect within the copula framework. The empirical analysis is based on traffic analysis zone (TAZ) level crash count data for both motorized and non-motorized crashes from Central Florida for the year 2016. A comprehensive set of exogenous variables including roadway, built environment, land-use, traffic, socio-demographic and spatial spillover characteristics are considered for the analysis. The resulting data fit and prediction performance offered by the proposed approach clearly highlights the hybrid model – Random Parameter Copula based approach’s superiority over the purely simulation-based multivariate model in our study context. The comparison exercise is further augmented by undertaking an in-depth comparison for different count events across different crash types and a correct classification analysis. The estimated results further reinforce the improved performance of the Random Parameter Copula-based multivariate approach. The applicability of the model for hot spot identification is illustrated by generating plots identifying high-crash and low- crash zones by crash type in the Central Florida region.

Enhancing non-motorist safety by simulating trip exposure using a transportation planning approach

Traditionally, in developing non-motorized crash prediction models, safety researchers have employed land use and urban form variables as surrogate for exposure information (such as pedestrian, bicyclist volumes and vehicular traffic). The quality of these crash prediction models is affected by the lack of “true” non-motorized exposure data. High-resolution modeling frameworks such as activity-based or trip-based approach could be pursued for evaluating planning level non-motorist demand. However, running a travel demand model system to generate demand inputs for non-motorized safety is cumbersome and resource intensive. The current study is focused on addressing this drawback by developing an integrated non-motorized demand and crash prediction framework for mobility and safety analysis. Towards this end, we propose a three-step framework to evaluate non-motorists safety: (1) develop aggregate level models for non-motorist generation and attraction at a zonal level, (2) develop non-motorists trip exposure matrices for safety evaluation and (3) develop aggregate level non-motorists crash frequency and severity proportion models. The framework is developed for the Central Florida region using non-motorist demand data from National Household Travel Survey (2009) Florida Add-on and non-motorist crash frequency and severity data from Florida. The applicability of the framework is illustrated through extensive policy scenario analysis.

Earliest detection to date of SARS-CoV-2 in Florida: Identification together with influenza virus on the main entry door of a university building, February 2020.

In February and March, 2020, environmental surface swab samples were collected from the handle of the main entry door of a major university building in Florida, as part of a pilot surveillance project screening for influenza. Samples were taken at the end of regular classroom hours, between the dates of February 1–5 and February 19-March 4, 2020. Influenza A(H1N1)pdm09 virus was isolated from the door handle on four of the 19 days sampled. Both SARS-CoV-2 and A(H1N1)pdm09 virus were detected in a sample collected on February 21, 2020. Based on sequence analysis, the Florida SARS-CoV-2 strain (designated UF-11) was identical to strains being identified in Washington state during the same time period, while the earliest similar sequences were sampled in China/Hubei between Dec 30th 2019 and Jan 5th 2020. The first human case of COVID-19 was not officially reported in Florida until March 1st. In an analysis of sequences from COVID-19 patients in this region of Florida, there was only limited evidence of subsequent dissemination of the UF-11 strain. Identical or highly similar strains, possibly related through a common transmission chain, were detected with increasing frequency in Washington state between end of February and beginning of March. Our data provide further documentation of the rapid early spread of SARS-CoV-2 and underscore the likelihood that closely related strains were cryptically circulating in multiple U.S. communities before the first “official” cases were recognized.

The Epidemiology and Predictors of Outcomes Among Confirmed COVID-19 Cases in a Large Community Healthcare System in South Florida

The novel coronavirus disease 2019 (COVID-19) continues to be a major public health concern. The aim of this study was to describe the presenting characteristics, epidemiology and predictors of outcomes among confirmed COVID-19 cases seen at a large community healthcare system which serves the epicenter and diverse region of Florida. We conducted a retrospective analysis of individuals with lab-confirmed SARS-CoV-2 infection who were seen, from March 2, 2020 to May 31, 2020, at Memorial Healthcare System in South Florida. Data was extracted from a COVID-19 registry of patients with lab-confirmed SARS-CoV-2 infection. Univariate and backward stepwise multivariate logistic regression models were used to determine predictors of key study outcomes. There were a total of 1692 confirmed COVID-19 patients included in this study. Increasing age was found to be a significant predictor of hospitalization, 30-day readmission and death. Having a temperature of 38 °C or more and increasing comorbidity score were also associated with an increased risk of hospitalization. Significant predictors of ICU admission included having a saturated oxygen level less than 90%, hypertension, dementia, rheumatologic disease, having a respiratory rate greater than 24 breaths per minute. Being of Hispanic ethnicity and immunosuppressant utilization greatly increased the risk of 30-day readmission. Having an oxygen saturation less than 90% and an underlying neurological disorder were associated with an increased likelihood of death. Results show that a patient’s demographic, underlying condition and vitals at triage may increase or reduce their risk of hospitalization, ICU admission, 30-day readmission or death.

Modeling and Analysis of Excess Commuting with Trip Chains

Commuting, like other types of human travel, is complex in nature, such as trip-chaining behavior involving making stops of multiple purposes between two anchors. According to the 2001 National Household Travel Survey, about half of weekday U.S. workers made a stop during their commute. In excess commuting studies that examine a region’s overall commuting efficiency, commuting is, however, simplified as nonstop travel from homes to jobs. This research fills this gap by proposing a trip-chaining-based model to integrate trip-chaining behavior into excess commuting. Based on a case study of the Tampa Bay region of Florida, this research finds that traditional excess commuting studies underestimate both actual and optimal commute and overestimate excess commuting. For chained commuting trips alone, for example, the mean minimum commute time is increased by 70 percent from 5.48 minutes to 9.32 minutes after trip-chaining is accounted for. The gaps are found to vary across trip-chaining types by a disaggregate analysis by types of chain activities. Hence, policymakers and planners are cautioned with regards to omitting trip-chaining behavior in making urban transportation and land use policies. In addition, the proposed model can be adopted to study the efficiency of nonwork travel.

Manatee Trichechus manatus latirostris calf mortality in Florida: a retrospective review of pathology data from 2009-2017.

High maternal investment and extended inter-calving intervals in Florida manatees Trichechus manatus latirostris make calf survivorship critical to overall population growth. However, detailed patterns of causes of mortality in calves have not been reported and state agency statistics report portions of perinatal mortality based on body length rather than actual cause of death (COD). The objectives of this study were to categorize COD based on necropsy data and geographical location in Florida for 1209 manatee calf carcasses (<236 cm total length) examined between January 2009 and December 2017 and to describe factors contributing to calf mortality. Results indicated COD was attributed to natural causes (47%, n = 573), cold stress syndrome (38%, n = 457), watercraft injury (13%, n = 155), or other human-related causes (2%, n = 24). Natural causes were the leading COD for small calves <151 cm, with death due to stillbirth or dystocia most frequent (48%, n = 273/573). Enteric trematodiasis contributed to a large proportion of deaths from natural causes in large calves within the southwest region of Florida, with an increasing annual trend. Brevetoxicosis contributed substantially to natural causes within the southwest region exclusively and was commonly comorbid with enteric trematodiasis. Cold stress syndrome was the leading cause of death for large calves (151-235 cm), with the Atlantic region having the highest proportion of cases. Watercraft injury was a sustained threat to large calves, especially within the southwest region. This report provides details on specific health threats and patterns of mortality among manatee calves.

Comparing the Invertebrate Communities and the Decomposition Dynamics Between Dead Native and Non-Native Trees in a Seasonal Everglades Wetland.

A 6-year time-series study in the Western Everglades region of Florida, United States examined the influence of woody debris from two tree species on invertebrate richness, abundance, and diversity, as well as tree debris mass loss, fragmentation, and residence time. Samples of decomposing fine woody debris and coarse woody debris (CWD) from non-native Melaleuca quinquenervia (Cav.) Blake and native Pinus elliottii Englem trees were removed from a field site every six months and processed to capture data on biotic and abiotic variables. Invertebrates found within debris were identified to family. A total of 61,985 individual invertebrates from three classes, 17 orders, and 95 families were identified from all debris. Although both tree species supported similar richness and diversity of invertebrates, abundance was greater in P. elliottii CWD compared with M. quinquenervia. Mass loss and fragmentation of debris were more rapid in M. quinquenervia fine woody debris with no differences between species for CWD. Although M. quinquenervia CWD supported fewer invertebrates than P. elliottii, overall the exotic tree provided a similar resource during the decomposition phase as the native P. elliottii suggesting that, unlike when it is alive, its decomposing presence had a minimal impact on invertebrate food webs. Land managers should consider specific intervals between herbicide applications and controlled burns to decrease the magnitude of fires in areas where a significant portion of the fuel load consists of dead M. quinquenervia, knowing that the decomposing trees are providing significant resources for invertebrate communities in the meantime.

Addiction of Alcohol and Its Effects

Liquor is the substance most commonly mishandled in combination with cocaine. The display considers looked for to explore the conceivable impact of liquor abuse in memory change amid restraint among persistent cocaine clients. Forty eight subjects were chosen and gathered as either cocaine as it were abusers (COC) or cocaine and liquor abusers (CA). All subjects were experiencing treatment at a south Florida region private sedate recovery program at the time of testing. Participants were tried at the moment week of forbearance (early abstinence) and retested at 2 months (late forbearance).

The Serial Tech Startup Founder (STSF) Base Theory and the Entrepreneurial Paradox

In order to develop a viable tech startup, generally, resilient startup venturers need to become serial startup entrepreneurs as they have to climb the learning curve through building several startups by the time a lasting business is crafted and nurtured. In this research, I intended to explore the set of qualities that helps serial tech venture founders push forward initiating startup after startup. Because of the lack of a clear understanding of this phenomenon in the current literature, I wished to inquire into this set of qualities that generates the prerequisite driving forces ensuing serial tech venturing. This research employed semi-structured video-recorded interviews to study 22 serial tech startup founders (STSF) from the Southwest Florida region. All interviews were transcribed and coded. After generating about 700 codes, I arrived at four categories (called constituent attributes) 1) Elastically Nonconforming (EN), 2) Abstracted Serial Monetizing (ASM), 3) Tech Passioning (TP), and 4) Heroed Enterprising (HE) as well as one core category called Te(ch)nacious Heroing (TcH).

Temperature based indicators to develop adaptive responses for crop production in Florida, USA

Crop failure temperatures (CFTs) are critical upper threshold temperatures above which plant growth and development stop. Climate variability with CFTs has an essential impact on agriculture, which leads to a decrease in plant yield to nearly zero. This study innovatively combines data analysis and analysis of published literature to develop causal chains/loops using Driver-Pressure-State-Impact-Responses (DPSIR) framework. In data analysis, CFTs trends were estimated from 21 models participating in the Coupled Model Inter-comparison Project Phase 5 (CMIP5) for the historical (1950–2005) and future scenarios (RCP 8.5, 2006–2100) at a spatial resolution of 0.125°x0.125° over Florida region. From the scenario funnel plots, it is evident that the frequency of number days above CFTs was found to be increasing at the rate of 2 days/year, and maximum mean temperature intensity was found in the range of 0.02 to 0.04 °C/year till 21st century. The causal chain and loop help to understand the complex structure and feedback mechanism for CFTs. This also helps in bridging the gap between climate and crop to address the adaptation strategies if the impacts are known. Adaptation strategies from the effects of the crops found to be promising to mitigate the effects of climate on crop and which can be used by the stakeholders and managers for their own use.

Morphological correlates of invasion in Florida cane toad (Rhinella marina) populations: Shortening of legs and reduction in leg asymmetry as populations become established

Exotic organisms have traits that allow them to effectively disperse and propagate into novel environments. The ability to adapt to new environments has enabled the cane toad (Rhinella marina) to successfully invade novel ecosystems. Their leg length correlates with dispersal success and is selected for in invading populations. Given their successful invasion of multiple continents, R. marina represent a unique opportunity to understand the generality of invasion strategies within a species. Here we test for spatial and temporal shifts in leg length (length of tibio-fibula relative to snout-vent length) of invasive R. marina in Florida populations to assess their adaptation to novel environments. Individuals experiencing new conditions are susceptible to physiological stress, which may negatively affect their development and inhibit their dispersal potential. To this end, we tested for spatial and temporal trends in fluctuating asymmetry, an indicator of physiological stress during development. We compared patterns between two regions of Florida (central vs southern Florida) with different invasion histories. We found that relative leg length of R. marina decreased over time in both regions, and experienced shifts in fluctuating asymmetry over time from initial colonization to population establishment. Our results suggest that successful invasive species rapidly adjust to novel environments. In addition, our results support predictions that morphological change plays a role in aiding dispersal for rapidly dispersing species.

Chemical and physical attributes of fruit juice and peel of pomegranate genotypes grown in Florida, USA

The global demand for pomegranate has led to increasing research and improvement of cultivars that produce higher antioxidant compounds. The current study was carried out to evaluate the bioactive constituents and physical properties of fourteen pomegranate genotypes grown in the subtropical region of Florida. There were differences in aril color among genotypes. The highest total anthocyanin content was found in ‘Ariana’, ‘Molla Nepes’, and ‘Parfianka’ genotypes. Furthermore, total anthocyanin content in peel ranged from 2.14 to 10.86 mg/100 g dry weight. Total phenolic content in the pomegranate fruit juice differed significantly among genotypes, varying from 365.71 to 1167.40 mg/L. Moreover, total phenolic content in the fruit peel ranged from 1313.08 to 1700.07 mg/L. Total phenolic compounds and reducing power activity in peel tissues were greater than in pomegranate juice. Reducing power activity and titratable acidity were positively and significantly correlated with total anthocyanin content.

Modeling and Mapping High Water Table for a Coastal Region in Florida using Lidar DEM Data.

Predicting and mapping high water table elevation in coastal landscapes is critical for both science application projects like inundation risk analysis and engineering projects like pond design and maintenance. Previous studies of water table mapping focused on the application of geostatistical methods, which cannot predict values beyond an observation spatial domain or generate an ideal pattern for regions with sparse measurements. In this study, we evaluated the multiple linear regression (MLR) and support vector machine (SVM) techniques for high water table prediction and mapping using fine spatial resolution lidar-derived Digital Elevation Model (DEM) data, and designed an application protocol of these two techniques for high water table mapping in a coastal landscape where groundwater, tide, and surface water are related. Testing results showed that SVM largely improved the high water table prediction with a mean absolute error (MAE) of 1.22 feet and root mean square error (RMSE) of 2.22 feet compared to the application of the ordinary Kriging method which could not generate a reasonable water table. MLR was also promising with a MAE of around 2 feet and RMSE of around 3 feet. The study suggests that both MLR and SVM are valuable alternatives to estimate high water table elevation in Florida. Fine resolution lidar DEMs are beneficial for high water table prediction and mapping.

Loss of Coastal Islands Along Florida’s Big Bend Region: Implications for Breeding American Oystercatchers

Many coastal-dependent species have undergone large-scale population declines due to impacts from habitat loss, including American oystercatchers (Haematopus palliatus). Islands along the Big Bend region of Florida’s Gulf Coast provide important nesting habitat for oystercatchers, but reproductive success here is low and habitat degradation and loss are a major concern. To determine rates and characteristics of habitat loss, we quantified changes in island sizes within two major breeding areas of the Big Bend: the Barge Canal spoil islands and natural islands at Cedar Key. We digitized aerial photographs from the past ~ 40 years, measured area and shoreline retreat of nesting islands, and identified trends over time by fitting linear mixed effects models. The total area of the ten Barge Canal spoil islands decreased by 55% between 1979 and 2016. At Cedar Key, the total area of the six islands measured decreased by 39% between 1974 and 2016, 85% of which occurred after 1995, indicating an increase in erosion rates correlated with oyster reef declines. Changes in available nesting habitat varied between the Barge Canal and Cedar Key islands due to differences in physical attributes; however, all islands significantly decreased in size over time. Given the long life and high site fidelity of American oystercatchers, these islands may currently be acting as an ecological trap for this species. Climate change, sea-level rise, and loss of oyster reefs are likely to continue to drive oystercatcher habitat loss throughout their range; thus, creation and restoration of oyster reefs and nesting islands will become increasingly important.

The application of enduring involvement theory in the development of a success model for a craft beer and food festival

PurposeThe purpose of this research paper is to examine whether enduring involvement theory plays a role in predicting craft beer and food festival visitors' experience of the festivalscape. Though craft beer and brewing is a growing area of research, there has been limited studies and theory application in this area. Around the world, craft breweries are increasing in number and producing more unique styles of beer as the demand for craft beer increases. Craft beer consumers visit many of these breweries and are attracted to craft beer festivals in which they can sample multiple local, regional, national and international craft beers.Design/methodology/approachA quantitative methodology was used based on data collected at the site of the festival. Researchers collected 204 useable surveys from visitors attending the North Miami Brewfest in North Miami, Florida, USA. Structural equation modeling was employed to examine the relationships among enduring involvement, festivalscape, satisfaction, revisit intention and word-of-mouth.FindingsThe results revealed that enduring involvement is significant in predicting all four factors of festivalscape (food/beverage quality, convenience, facility and festival staff). The festivalscape factors facility, food quality and festival staff predicted festival attendee satisfaction which in turn predicted both revisit intention and word-of-mouth. However, the festivalscape factor convenience did not influence satisfaction.Research limitations/implicationsThe authors surveyed only one festival in one region in South Florida. Further studies can survey multiple festivals in multiple regions to increase the generalizability of the research model. Enduring involvement theory could be applied to other niche areas in hospitality and tourism in the future (in addition to craft beer tourism).Practical implicationsCraft beer festival organizers should appeal to craft beer clubs, breweries and publications to attract those with a commitment to the craft beer industry to their event. People with an enduring, lasting commitment to craft beer are more likely to have a positive experience of the festivalscape at the event. Lastly, festival organizers should focus on the festivalscape factors facility, festival staff and food and beverage quality to influence satisfaction at the event.Originality/valueThis project applies enduring involvement theory in a festival setting. The research is further unique by adding enduring involvement as a predictor of festivalscape experience.