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Background: Geostatistics focuses on spatial or spatiotemporal datasets. Geostatistics was initially developed to generate probability distribution predictions of ore grade in the mining industry; however, it has been successfully applied in diverse scientific disciplines. This technique includes univariate, multivariate, and simulations. Kriging geostatistical methods, simple, ordinary, and universal Kriging, are not multivariate models in the usual statistical function. Notwithstanding, simple, ordinary, and universal kriging techniques utilize random function models that include unlimited random variables while modeling one attribute. The coKriging technique is a multivariate estimation method that simultaneously models two or more attributes defined with the same domains as coregionalization.

Objective: This study investigates the impact of populations on traffic volumes as a variable. The additional variable determines the strength or accuracy obtained when data integration is adopted. In addition, this is to help improve the estimation of annual average daily traffic (AADT).

Methods procedures, process: The investigation adopts the coKriging technique with AADT data from 2009 to 2016 from Montana, Minnesota, and Washington as primary attributes and population as a controlling factor (second variable). CK is implemented for this study after reviewing the literature and work completed by comparing it with other geostatistical methods.

Results, observations, and conclusions: The Investigation employed two variables. The data integration methods employed in CK yield more reliable models because their strength is drawn from multiple variables. The cross-validation results of the model types explored with the CK technique successfully evaluate the interpolation technique's performance and help select optimal models for each state. The results from Montana and Minnesota models accurately represent the states' traffic and population density. The Washington model had a few exceptions. However, the secondary attribute helped yield an accurate interpretation. Consequently, the impact of tourism, shopping, recreation centers, and possible transiting patterns throughout the state is worth exploring.

As public health professionals strive to promote vaccines for inoculation efforts, fervent anti-vaccination movements are marshaling against it. This study is motived by a need to better understand the online discussion around vaccination. The authors identified the sentiments, emotions and topics of pro- and anti-vaxxers’ tweets, investigated their change since the pandemic started and further examined the associations between these content features and audiences’ engagement.

Utilizing a snowball sampling method, data were collected from the Twitter accounts of 100 pro-vaxxers (266,680 tweets) and 100 anti-vaxxers (248,425 tweets). The authors are adopting a zero-shot machine learning algorithm with a pre-trained transformer-based model for sentiment analysis and structural topic modeling to extract the topics. And the authors use the hurdle negative binomial model to test the relationships among sentiment/emotion, topics and engagement.

In general, pro-vaxxers used more positive tones and more emotions of joy in their tweets, while anti-vaxxers utilized more negative terms. The cues of sadness predominantly encourage retweets across the pro- and anti-vaccine corpus, while tweets amplifying the emotion of surprise are more attention-grabbing and getting more likes. Topic modeling of tweets yields the top 15 topics for pro- and anti-vaxxers separately. Among the pro-vaxxers’ tweets, the topics of “Child protection” and “COVID-19 situation” are positively predicting audiences’ engagement. For anti-vaxxers, the topics of “Supporting Trump,” “Injured children,” “COVID-19 situation,” “Media propaganda” and “Community building” are more appealing to audiences.

This study utilizes social media data and a state-of-art machine learning algorithm to generate insights into the development of emotionally appealing content and effective vaccine promotion strategies while combating coronavirus disease 2019 and moving toward a global recovery.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-03-2022-0186

The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

Climate change is expected to cause larger and more frequent precipitation events in key agricultural regions of the United States, damaging crops and soils. Subsurface tile drainage is an important technology for mitigating the risks of a wetter climate in crop production. In this study, the authors examine how quickly farmers adapt to increased precipitation by investing in drainage technology.

Using farm-level data from the 2018 Agricultural Resource Management Survey (ARMS) of soybean producers, the authors construct a drainage adoption timeline based on when the operator began farming their land and when tile drainage was installed, if at all. The authors examine both the initial investment decision and the speed with which drainage is installed by adopters. A Heckman-style Poisson regression is used to model the count nature of adoption speed (measured in years taken to install tile drainage) and to correct for potential sample-selection bias.

The authors find that local precipitation is not a significant determinant of the drainage investment decision but may be highly influential in the timing of adoption among drainage users. Farms exposed to crop-damaging levels of precipitation install tile drainage faster than those with low to moderate levels of rainfall. Estimates of farm adaptation speeds are heterogeneous across farm and operator characteristics, most notably land tenure status.

Understanding how US farmers adapt to extreme weather through technology adoption is key to predicting the long-term impacts of climate change on America's food system. This study extends the existing climate adaptation literature by focusing on the speed of adoption of an important and increasingly common climate-mitigating technology – subsurface tile drainage.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

This paper aims to investigate the primary motivations for China’s outward foreign direct investment (ODI) decisions.

Using a panel data sample covering the period 2003–2012 and a comprehensive set of 176 host countries.

This study finds that market size, trade variables and natural resource variables are strongly related to the Chinese ODI stocks. This indicates that Chinese ODI decisions are driven by both market- and resource-seeking motives. The subperiod sample test results lend even stronger support to the market-seeking motive for ODI.

These results seem to emerge from the policy changes that were undertaken during the sample period. Consistent with subgroup tests, this study finds that the main purposes of China’s ODI in the top 100 countries are natural resource explorations and production line replacements.

The purpose of this paper is to investigate the role of firms’ features on innovation performance in Industry 4.0, focusing on the concepts of breadth and depth of openness in the innovation process.

Using data gathered from 96 firms active in Industry 4.0 (I4.0) in Italy, a Poisson regression analysis is conducted to investigate the relationship between the openness of firms’ innovation processes at the level of knowledge sources and their innovation performance in I4.0.

The results highlight the relationship between the level of openness and innovative performance in I4.0. In particular, the breadth of the openness of the innovation process of enterprises is curvilinearly related to innovation in I4.0, taking an inverted U-shape.

Managers of firms operating in I4.0 should consider openness as a strategic response to the knowledge requirements and risks associated with the innovation process in complex technologies.

Through the questionnaires administered mainly to highly qualified individuals, an original and unique database has been created with information on the openness of the innovative process and the innovation performances in I4.0.

When there is freedom of press, newspapers provide prying eyes that investigate and report the malfeasance by public officials. More prying eyes together with more newspaper readership make monitoring of public officials by the public easier and cheaper. This paper aims to investigate the role of newspapers in helping the public observe the conduct of local officials fearful of discovery of malfeasance by the newspaper readers in the USA during 1978 – 2008 when the internet was still a fledgling source of news.

A model that recognize that corruption is an agency problem that thrives in the absence of monitoring of public officials is used. The estimation technique used address problems issuing from the subjective nature of measures of press freedom and perception of corruption, and the persistence of corruption over time.

More newspapers and newspaper readers help to alleviate the agency problem that underlies public corruption in the USA and elsewhere. More newspapers (i.e. more journalists) act to deter corruption at the margin, and, ceteris paribus, higher readership works on exposing corrupt acts and helps to convict the errant officials in larger numbers.

The paper provides a timely context to consider the implication of sharp fall in local newspapers as well as newspaper readership all across the USA.

This paper extends the literature by considering press freedom, the number of newspapers and size of newspaper readership as joint determinants of public corruption.

The purpose of this study is to investigate whether and how inter-firm cooperation and firm age moderate the relationship between family ownership and productivity.

We first estimate the total factor productivity (TFP) of a large sample of Italian firms observed over the period 2010–2018 and then apply a Poisson random effects model.

TFP is, on average, higher for non-family firms (non-FFs) than for FF. Furthermore, inter-organizational cooperation and firm age mitigate the negative effect of family ownership. In detail, it is found that belonging to a network acts as a moderator in different ways according to firm age. Indeed, young FFs underperform non-FF peers, although the TFP gap decreases with age. In contrast, the benefits of a formal network are high for older FFs, suggesting that an age-related learning process is at work.

The study provides evidence that FFs can outperform non-FFs when they move away from Socio-Emotional Wealth-centered reference points and exploit knowledge flows arising from high levels of social capital. In the case of mature FFs, networking is a driver of TFP, allowing them to acquire external resources. Since FFs often do not have sufficient in-house knowledge and resources, they must be aware of the value of business cooperation. While preserving the familiar identity of small companies, networks grant FFs the competitive and scale advantages of being large.

Despite the wide but ambiguous body of research on the performance gap between FFs and non-FFs, little is known about the role of FFs’ heterogeneity. This study has proven successful in detecting age as a factor in heterogeneity, specifically to explain the network effect on the link between ownership and TFP. Based on a representative sample, the study provides a solid framework for FFs, policymakers and academic research on family-owned companies.

Exploration and exploitation have long been documented as prominent approaches to business management and organizational adaptation to external environment. Maintaining balance between these activities is a key to survival and prosperity. However, there is little direct evidence of the effect of such combined usage of both approaches on firm performance in times of crisis, especially within small- and medium-sized enterprises (SMEs). The purpose of this paper is to reveal the role of balanced ambidexterity in shaping firm performance during COVID-19 recession.

Based on a survey of 333 Russian SMEs, the authors test the proposed theoretical framework linking innovative ambidexterity to firm performance level and variability taking into account technological uncertainty.

The results show that innovative ambidexterity tends to increase level and decrease variability of performance outcomes, whereas technological uncertainty acts as a positive contingency for this impact.

The results provide an improved understanding of ambidexterity and organizational literatures by clarifying the contingent nature of the ambidexterity–firm performance relationship during COVID-19 recession.