The Interaction Between the Recent Evolution of Working from Home and Online Shopping

The Interaction Between the Recent Evolution of Working from Home and Online Shopping | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Interaction Between the Recent Evolution of Working from Home and Online Shopping Motahare Mohammadi, Amir Davatgari, Sina Asgharpour, Ramin Shabanpour, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3974111/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jun, 2024 Read the published version in Transportation → Version 1 posted You are reading this latest preprint version Abstract The growing behaviors of work-from-home (WFH) and online shopping hold significant potential for reducing traffic congestion and emissions. Understanding the frequency and the interplay between these two behaviors is important for successful implementation. This study investigates the recent trends of WFH and online shopping and the underlying factors influencing individuals’ decisions on these two behaviors. Focusing on non-grocery online shopping, this study uses comprehensive survey data collected across the United States during October and November 2021. We develop a Generalized Structural Equation Model (GSEM) to jointly examine WFH and online shopping frequency and their interaction. Moreover, the study investigates the psychological aspects of WFH and online shopping, introducing four stochastic latent constructs—WFH comfort, WFH unproductiveness, online shopping enjoyment, and online shopping inconvenience using the attitudinal variables. Results indicate a positive causal relationship, suggesting that increased WFH promotes online shopping engagement. Perceived comfort and productivity at home affect WFH frequency shaped by factors like home workspace, commuting time, childcare responsibilities, and telecommunications with co-workers. Likewise, perceived convenience and enjoyment significantly affect online shopping, influenced by aspects such as timesaving, and the delivery and return process. Technological tools at home also play a role in WFH frequency. Demographic factors like age, race, income, physical disability, and mode choice habits correlate with WFH and online shopping incidence, while job category and employer flexibility influence WFH frequency. These insights can help policymakers to regulate remote work and online shopping activities as they continue to grow. Work from home (WFH) online shopping information and communication technology (ICT) SEM COVID-19 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION Work-from-home (WFH) and online shopping are rapidly growing. Since 2009, the number of employees working from home and the share of e-commerce in retail sales in the United States have increased by 159% and 72%, respectively (B2B Insights, 2022; Census, 2022, 2009). The rapid advancement of information and communication technologies (ICT) over the last few decades is one reason for these changes (OECD, 2021, 2019). Another reason is the impact of the COVID-19 pandemic on our lives. The COVID-19 pandemic forced people to perform essential daily activities such as working and shopping at home (Bhatti et al., 2020; Javadinasr et al., 2022; Mohammadi et al., 2022; Salon et al., 2021). These changes in individuals’ daily activities have an impact on many travel behaviors, such as the number of trips per day, destination, mode, and route choice decisions, and thus have an impact on the entire transportation system. Despite the overall increasing trend of WFH adoption over the past decade, the dynamics of WFH experienced notable fluctuations because of the COVID-19 pandemic’s ever-changing nature. Following global lockdowns and the implementation of social distancing measures, WFH witnessed a remarkable upsurge (Jain et al., 2022); however, emerging evidence in the post-pandemic era indicates a decline as individuals are gradually returning to traditional workplaces (Huang et al., 2023; Rahman Fatmi et al., 2022). For instance, a U.K. survey reveals that teleworking constituted an average of 15% of work time before the pandemic, rose to 89% at wave 1, and subsequently decreased to 72.5% at wave 3 of the pandemic (Baumann et al., 2023). Online shopping, however, experienced a sustained increase suggested by researchers during the lockdown period as well as the post-pandemic era (Brůhová Foltýnová and Brůha, 2024; Diaz-Gutierrez et al., 2023). Observing huge shifts to WFH and online shopping, these ICT-based activities as part of travel demand management, provide transportation planners and policymakers with an opportunity to address transportation system issues such as urban congestion and GHG emissions (Hensher et al., 2022; Shabanpour et al., 2018). Hence, it is critical to understand what influences individuals' decisions on WFH and online shopping, as well as the interrelationship between these two behaviors. WFH and online shopping might have a mixed relationship. These two behaviors, for example, can have a positive impact on each other, because people who WFH have more access to ICT devices, are already familiar with how to use such technology, and are interested in using them. According to the latest PYMNTS and Strip report, observing the digital behaviors of over 30,000 consumers in 11 countries, 44% of consumers stated that they were working from home. Furthermore, within this group of individuals who are highly connected through online work, 73% participated in online retail shopping (PYMNTS, 2023). Additionally, WFH may contribute to online shopping incidence in another way. Most people stop for shopping while commuting (Golob and Regan, 2001; PYMNTS, 2019), and those who WFH might either avoid shopping or do online shopping rather than in-person shopping. There are some contradictory findings in the literature, for example, WFH may reduce online shopping because it is stated that workers at home spend more time on non-commuting trips (Hensher et al., 2022), potentially increasing in-person shopping. The complex connectivity of these two behaviors needs to be studied as it determines changes in activity patterns and their implications on transportation systems. The discussion above motivated us to comprehensively analyze the concurrent trends of WFH and online shopping frequency, shedding light on their recent evolution as well as exploring how these two behaviors contribute to changes in each other. Online shopping can be categorized into two classes: grocery and non-grocery. Since online grocery shopping emerged recently following the COVID-19 pandemic and its benefits are still unknown or underestimated (New York Times, 2022; Tyrväinen and Karjaluoto, 2022), in this study, we capture the non-grocery online shopping behavior, and we use the terms “online shopping” and “online non-grocery shopping” interchangeably. We focus on non-grocery-only online shopping that entails purchasing items beyond everyday essentials and food products, including categories such as apparel and footwear, consumer electronics and appliances, health and beauty products, home, and garden items, as well as leisure and personal goods (Bourlier, 2020). We use the data drawn from a 2021 nationwide “COVID FUTURE” survey administered by the research group to track US households’ activity-travel behavior evolution since the COVID-19 pandemic. The collected information covers various aspects including individual and household characteristics, job-related factors (e.g., employer WFH policies), travel habits, different ways people go about their daily activities, and their lifestyle attitudes. This comprehensive dataset allows us a detailed look at many factors that play a role in WFH and online shopping decisions. A Generalized Structural Equation Model (GSEM) is developed, which provides a robust framework for concurrently examining complex relationships among variables, yielding comprehensive insights in research studies. To capture the complexity of individuals’ decision-making, we incorporated both observed and unobserved (i.e., latent) variables that significantly influence these two ICT-based behaviors. Estimated latent variables, representing a combination of positive and negative attitudes toward WFH and online shopping, along with home-tech factors account for unobserved patterns, recognize the influence of different technology attributes, and comprehensively explore the underlying factors. For example, individuals who are more productive when WFH due to no commuting time, more comfortable workspace at home, etc., might work remotely more often, which is captured by the “WFH Comfort” Latent variable. Similarly, one might be encouraged to shop online as they perceive this behavior as convenient and beneficial due to a combination of reasons such as timesaving and, a variety of available options online, all of which form “Online Shopping Enjoyment” latent. On the other hand, different technological attributes such as high-speed internet, and computers may facilitate the choice of WFH and online shopping, and that is integrated by the “Home Tech Availability” latent variable into the model. 2. BACKGROUND Working from home and online shopping have been popular as early as the 1980s, attracting many researchers and urban planners’ interest due to their potential impacts on individuals’ activity-travel behavior. There is a vast body of literature on WFH and online shopping (Asgari et al., 2014 ; Balbontin et al., 2022 , 2021 ; Forsythe and Shi, 2003 ; Friesz et al., 1989 ; Hensher et al., 2022 , 2021 ; Mafé and Blas, 2007 ; Mohammadi et al., 2022 ; Paleti, 2016 ; Pouri and Bhat, 2003 ; Sener and Bhat, 2011 ; Sener and Reeder, 2012 ; Shabanpour et al., 2018 ; Siegel, 2003 ; Singh et al., 2013 ; Walls et al., 2007 ). All these studies use statistical analysis to explore factors affecting individuals’ decisions for WFH and online shopping. In the following, we summarize the studies conducted on WFH and online shopping, then we present the existing research gaps. 2.1. WFH Literature WFH literature can be categorized into two groups: studies before the COVID-19 pandemic (Asgari et al., 2014 ; Peters et al., 2004 ; Sener and Bhat, 2011 ; Shabanpour et al., 2018 ; Singh et al., 2013 ; Varma et al., 1998 ; Walls et al., 2007 ) and after the pandemic (Balbontin et al., 2022 , 2021 ; Mohammadi et al., 2022 ; Nguyen, 2021 ; Nguyen and Armoogum, 2021 ). These two groups are differentiated due to a better understanding of the literature after the pandemic about WFH behavior. Studies conducted before the pandemic mostly focused on non-attitudinal factors influencing WFH behavior. For example, many emphasize the importance of demographic characteristics in employee preferences for WFH. Particularly, individuals with higher levels of education, those belonging to higher income groups, and individuals with children were found to be more inclined to adopt WFH (He and Hu, 2015 ; Loo and Wang, 2018 ). Few studies investigated the effects of perceptions and attitudes on WFH frequency. For example, Mokhtarian et al. (Mokhtarian and Salomon, 1997 ) found that job suitability, perceived benefits, and productivity influence WFH decisions. The COVID-19 pandemic pushed many non-essential workers to work at home providing the opportunity to explore the revealed preferences of WFH (Beck and Hensher, 2020 ; Javadinasr et al., 2022 ; Mohammadi et al., 2022 ; Salon et al., 2021 ). As many who were new to WFH started practicing it, the role of attitudes and perceptions have been highlighted after the pandemic literature. For example, Mohammadi et al. (Mohammadi et al., 2022 ) investigated the long-term impacts of the COVID-19 pandemic on WFH frequency in the US using data from waves 1 and 2 of the COVID Future survey. They developed a generalized structural equation model (GSEM) in which impacts of WFH productivity and changes in perceived risk of exposure to COVID-19 along with other observed factors on preferences toward WFH are captured. They found that both perceived WFH productivity and COVID-19 risk positively and significantly influence the frequency of this behavior. Balbontin et al. ( 2022 ) modeled the decision to WFH or commute during pandemic restrictions in Australia by looking into the impacts of two latent variables: WFH-loving attitude and risk perception in using public transit. They found that WFH loving attitude which is mostly defined by the company’s flexibility toward the workplace significantly increases WFH probability. Risk perception in using public transit was also found to be an important factor influencing WFH or commuting with private vehicles. Nguyen ( 2021 ) explored adoption, perceptions, and attitudes toward WFH during and after the pandemic in Vietnam. In line with the previous study, He found the importance of the company’s policy for the workplace to WFH choice. Moreover, he indicated the critical role of difficulties with being focused and accessing the data at home in perceiving WFH as a good solution during the pandemic. A line of literature studying the post-pandemic WFH employs the stated preference approach to explore the impact of the COVID-19 pandemic on individuals’ future WFH behaviors. For instance, Foltýnová and Brůha (Brůhová Foltýnová and Brůha, 2024 ) investigated the expectations of Czech Republic residents regarding WFH after the pandemic, using a multi-wave survey conducted in 2020 and 2021. Their findings highlighted job type, age, and education as key predictors of WFH adoption in the post-pandemic era. Similarly, Rahman Fatmi et al. (Rahman Fatmi et al., 2022 ) studied the lasting impact of COVID-19 on individuals’ preferences for WFH in post-pandemic times. Analyzing data from a survey in British Columbia, the study developed a random parameter logit and found factors such as age, gender, commute time, dwelling size, and location characteristics to be significant in WFH preferences. Similarly, Jian et al. (Jain et al., 2022 ) investigated the enduring impacts of COVID-19 on employees’ post-COVID WFH intentions, using information from 1,364 workers in Greater Melbourne. The authors employed SEM to examine the role of psycho-social latent variables in employees’ intention to increase WFH post-COVID. Their findings revealed that perceived behavioral control (e.g., job type, technology, access to materials) and Subjective Norms (e.g., employer and family support) as key determinants for future WFH intentions. Asgari et al. (Asgari et al., 2022 ) characterized the impact of the pandemic-induced remote work on future teleworking decisions, leveraging a survey conducted in Florida in May 2020. Their analysis revealed that the post-pandemic WFH adoption is influenced by several attitudinal factors including pro-WFH, pro-technology, interaction enjoyment, and productivity attitude. Moreover, Kong et al. (Kong et al., 2023 ) employed a survey collected from Washington State in 2020. Using an SEM, they demonstrated that positive or negative perceptions towards WFH before and during the pandemic significantly affect individuals’ future decisions to continue WFH after the pandemic. 2.2. Online Shopping Literature Similar to WFH, online shopping literature can be divided into before (Currás-Pérez et al., 2011 ; Forsythe and Shi, 2003 ; Mafé and Blas, 2007 ) and after the pandemic studies (Andruetto et al., 2023 ; Asgari et al., 2023 ; Dias et al., 2020 ; Diaz-Gutierrez et al., 2023 ; Meister et al., 2023 ; Melović et al., 2021 ; Qalati et al., 2021 ; Shabanpour et al., 2022 ). The establishment of protective measures induced people not only to WFH but also to shop from home via online platforms (Bhatti et al., 2020 ; Rossi et al., 2022 ). Hence, studies conducted after the pandemic could better capture preferences for shopping online. Since the literature on preferences for online shopping is limited, we also review studies on online grocery shopping. Shabanpour et al. ( 2022 ) investigated how online grocery shopping behavior has changed because of the pandemic using data from a survey conducted in the Chicago region. They found that individuals who had not previously engaged in online grocery shopping were inclined to alter their habits and switch to online shopping during the pandemic. Furthermore, they indicated the significant role of income and COVID-19 restrictions in online shopping frequency during the pandemic. Another study conducted by Asgari et al. (Asgari et al., 2023 ) in South Florida employs SEM to explore both observed and latent attitude variables. Their findings underscore the significance of experience with online grocery platforms as a crucial factor influencing continued use. Additionally, the study reveals that positive attitudes toward technology and perceptions regarding various aspects of online grocery shopping, such as convenience, efficiency, usefulness, and easiness, play important roles in driving the likelihood of future engagement in online grocery shopping. Andruetto et al. (Andruetto et al., 2023 ) examined the variations in online shopping patterns for both grocery and non-grocery items during the first wave of the pandemic in Italy and Sweden. Their findings revealed a significant transition from traditional in-person shopping to online shopping in both countries. However, the extent of this shift is more pronounced in Italy compared to Sweden, attributable to the more stringent restriction policies enforced in Italy. Melovic et al. (Melović et al., 2021 ) investigated factors influencing Millennials’ online shopping behavior in Montenegro using SEM. They illustrated that perceived risks and barriers of online shopping such as delivery and quality of the product purchased online significantly impact millennials’ preferences. Moreover, they found that while the frequency of online shopping does not differ based on gender, the behavior does. Using a similar modeling approach, Qalati et al. ( 2021 ) studied the relationship between perceived service quality, perceived website quality, and perceived reputation and online shopping adoption, illustrating the significant impacts of these factors on online shopping choice. Dias et al. (Dias et al., 2020 ) studied the interrelationships between online and in-person activity engagement, specifically focusing on shopping and eating meals. The authors found that in-person and online activities are both complement and substitute for each other. Moreover, their findings revealed that income, built-environment variables such as the presence of shopping centers in the neighborhood, and household structure-related factors including the presence of children or the number of household members, affect shopping behavior. Diaz-Gutierrez et al. ( 2023 ) studied factors affecting post-pandemic online shopping behavior using data collected from Washington State residents. They estimated SEM showing that the perceived health risk, perception toward online shopping, and online shopping frequency before the pandemic are the most significant factors influencing post-pandemic online shopping frequency. Moreover, Meister et al. ( 2023 ) employed a stated choice experiment to design a survey implemented in Switzerland in the first wave of the pandemic. According to their study, shopping time and cost, wait time, and perception of infection risk are the most significant factors affecting individuals’ decision to conduct online grocery shopping after the pandemic. 2.3. Research Gap and Contributions The studies mentioned above provide interesting results on either WFH or online shopping behavior. However, all these studies are focused on a statistical analysis of either WFH or online shopping behavior. The study most closely aligned with our current effort is the quantitative research by Sener and Reeder (Sener and Reeder, 2012 ), who explored the behavioral linkages across ICT choice dimensions, specifically WFH and online shopping. Their use of copula modeling represents a sophisticated statistical technique that allows for testing different forms of dependence between these two behaviors. The study utilized data from the 2009 National Household Travel Survey (NHTS), which includes individual/household demographics, commute characteristics, attitudinal factors, and residential neighborhood variables. They found a positive and asymmetric fit of the best model emphasizing the presence of unobserved factors influencing the underlying processes of WFH and online shopping behaviors. However, like any other study, there are certain aspects to consider. First, the temporal context of the study, originating in 2012, raises questions about the current relevance of their findings, given the dynamic nature of technology and evolving societal trends (OECD, 2021 , 2019 ). Second, Sener and Reeder’s pioneering work in examining behavioral linkages between telecommuting and teleshopping sets the stage for understanding these ICT choices. However, the current effort advances this understanding by incorporating a more flexible modeling approach. The GSEM used in this study proves advantageous as it can estimate complex nonlinear relationships simultaneously. This offers unparalleled flexibility in capturing the dependence between decisions and estimating latent variables simultaneously. The incorporation of latent variables, reflecting attitudes and technology availability, enhances the analysis. It offers a more nuanced understanding of the factors influencing work-from-home and online shopping behaviors, capturing the previously unseen aspects of these dynamics. In summary, this study brings a triple-layered contribution to the existing literature. Firstly, it looks into the unexplored area of understanding the intricate interactions between two crucial ICT behaviors: WFH and online shopping. Secondly, taking advantage of a dataset recently gathered in the United States during October and November 2021, our research provides a current snapshot of the evolving landscape, encapsulating the complexity of WFH and online shopping patterns. Finally, we navigate through the often-overlooked aspects, characterizing the impact of travel habits, job-related characteristics, at-home technologies, and attitudinal/perceptual factors on both WFH and online shopping behavior. These nuanced investigations contribute to a richer understanding of the dynamic factors shaping work-from-home and online retail activities. 3. METHOD 3.1. Survey The data source for this study is a three-wave nationwide survey administered by our research group in the United States throughout 2020 and 2021 using the Qualtrics online platform. In the first wave conducted from April 2020 to June 2021, a total of 9,265 responses were gathered. 2,877 and 2,728 participants rejoined for the second and third wave, respectively. Individuals were invited to participate through a quota-sample survey panel, direct random email invitations, and media coverage of the study project. This study focuses on wave 3 of the survey, which was administered in October and November 2021. COVID Future survey puts together: (1) socio-demographics such as age, gender, race, education, employment, household income, and household structure, (2) job characteristics such as job category and employer flexibility for WFH, (3) health issues such as disability status, covid infection, (4) home environment characteristics such as availability of a computer, high-speed internet connection, and the sufficiency of technological devices, (5) WFH choice and frequency and attitudes toward WFH, (6) employee’s productivity when WFH during the pandemic, how it has changed compared to before the pandemic and the perceived reasons of the change, (7) commute and non-commute trip features, (8) Online shopping frequency including online ordering grocery, non-grocery, and food as well as attitudes toward online shopping, (9) Online school, (10) telemedicine (11) long-distance travel behavior, (12) relocation, and (13) lifestyle attitudes. In this study, we elaborate on the sections relevant to our scope, specifically focusing on work-from-home, employee productivity, technology accessibility, job characteristics, Travel habits, and online shopping frequency attitudes. Interested readers can download this survey data and perform additional analysis. Other works based on this survey data can be found in (Bhagat-Conway et al., 2021; Chauhan et al., 2021b , 2021a ; Conway et al., 2020 ; Javadinasr et al., 2022 ; Mirtich et al., 2021 ; Mohammadi et al., 2022 ; Salon et al., 2021 ). In addressing the primary objectives of this study, we look into work-from-home (WFH) and online shopping activities. In the WFH section, our approach involves a systematic inquiry into employer flexibility, with questions presented in the following order: 1- Do you have the option to WFH?, 2- Are you required to WFH all the time?, 3- Are you required to WFH some of the time?, 4- Can you WFH whenever you want?, and 5- Can you WFH only some of the times?. Following this, those who had the option to WFH were asked how many days in the last 7 days they worked from home In optimizing model performance, a statistical significance test prompted the categorization of WFH frequency into the ordinal scale as follows: “0 WFH days per week” as 0, “1–2 WFH days per week” as 1, “3–4 WFH days per week” as 2, and “5 or more WFH days per week” as 3. Moving on to non-grocery online shopping behavior, respondents were provided with specific categories to express the frequency of ordering non-grocery items for delivery in the last 7 days. The response options included: “0”, “1 time,” “2–3 times,” and “4 times or more.” The same categorical framework was employed in the modeling process when estimating online shopping frequency. In the following, we discuss further questions related to the observed and latent variables incorporated in the model. Moreover, we provide an overall scheme of the developed framework. 3.2. Analytical Framework and Sample Description 3.2.1. Analytical Framework The analytic framework focuses on understanding the inter-relationship between WFH and online shopping frequency decisions while incorporating individuals’ demographics, household characteristics, job-related factors, travel habits, home technology-related factors, as well as lifestyle attitudes. The impacts of home technology-related factors, and lifestyle attitudes are not directly observed, and so are viewed as latent constructs. In this study, five latent constructs are used: (1) different attributes of the technology that is available at home referred to as Home Tech Availability, (2) individuals’ perceived reasons for their increased productivity at home referred to as WFH Comfort, (3) individuals’ perceived reasons for their decreased productivity at home referred as WFH Unproductiveness, (4) individuals’ perceived reasons for shopping online frequently referred as Online Shopping Enjoyment, and (5) individuals’ perceived reasons for not shopping online frequently referred as Inconvenient Online Shopping. These latent constructs are likely to impact WFH and online shopping decisions through comfort levels, interest, and trust in the use of ICT devices as well as general personality temperament. While being intuitive, these constructs were also based on previous studies on technology adoption and usage as well as the psychology related to WFH and online shopping (Asgari et al., 2023 , 2022 ; Berry et al., 2002 ; Diaz-Gutierrez et al., 2023 ; Duarte et al., 2018 ; Jain et al., 2022 ; Jiang et al., 2013 ; Kong et al., 2023 ; Meister et al., 2023 ; Melović et al., 2021 ; Mohammadi et al., 2022 ). Furthermore, the decision to settle on these specific latent variables was based on analyzing the correlation between attitudinal factors and home-tech attributes with the main outcomes. Moreover, we carefully examined the goodness-of-fit measures and the significance level of the factors during the modeling process to ensure the model’s statistical adequacy. Therefore, these latent constructs and their set of indicators presented in this paper are the most appropriate ones. We discuss the latent constructs and the choice of indicators in detail later in the following. Figure 1 represents the overall framework of this study. 3.2.2. Sample Description The analysis uses the portion of the survey that was employed in wave 3 of the survey and has the choice to decide about their WFH frequency. Hence, those employees who did not have the option to WFH, and those who were required to WFH all the time were excluded. The sample of this study includes 488 respondents. To ease understanding, Fig. 2 represents survey flowchart, and how the final sample is maintained. Figure 3 shows the spatial distribution of respondents across the United States. As seen, respondents are fairly distributed over California, Arizona, Texas, Illinois, New York, Ohio, Michigan, and many other densely populated states. Only a few states have a low number of observations. Table 1 presents the summary statistics of respondents’ key socio-demographic characteristics in the study sample. As mentioned earlier, this study focuses on those who are employed and have the option to WFH, hence, our sample exhibits a potential bias towards white, high-income, and educated households. This deliberate choice enables an in-depth exploration of WFH and online shopping dynamics within this specific context. While findings may not generalize broadly, they provide valuable insights into the behaviors and attitudes of individuals with WFH opportunities, contributing to a comprehensive understanding of this targeted demographic. Table 1 Summary statistics of respondents’ key characteristics Variable Category Frequency (%) Employment Full time 82.4 Part-time 17.6 Income Under $ 50K 15.4 $ 50K - $ 100K 34.6 $ 100K - $ 150K 25.6 More than $ 150K 24.0 Age 18–24 1.4 25–34 11.9 35–44 22.7 45–54 24.4 55–64 23.2 65–74 14.8 75 and above 1.6 Gender Female 56.8 Male 43.2 Race Hispanic 12.1 White/Caucasian 80.3 Black/African 9.0 Asian 9.4 Native American 2.3 Other 1.8 Education Completed graduate degree(s) 34.8 Bachelor’s degree(s) or some graduate school 43.0 Some college or technical school 18.0 Completed high school or GED 3.9 Some grade/high school 0.2 Household Size 1 17.4 2 37.7 3 18.6 4 15.2 5 or more 11.1 Figure 4 ‎(a) and Fig. 4 ‎(b) show the distribution of the dependent variables, online shopping, and WFH frequency, in the study sample. According to Fig. 4 ‎(a), the majority of respondents shop online 0 times, once, twice, or three times per week. As shown in Fig. 4 ‎(b), respondents are almost evenly dispersed across the various WFH frequency categories, with a higher density existing in 0 and 5 or more WFH days per week. Figure 4 ‎(c) illustrates the joint distribution of WFH frequency and online shopping behavior, providing an insight into how WFH and online shopping behaviors’ impacts on each other can be mixed. As shown, most of the observations are distributed over the diagonal and the lower triangle of the joint distribution matrix. More distribution over the diagonal or close to it, indicates positive correlation between WFH and online shopping. In particular, we observe this joint distribution with high correlation in the tails (zero WFH/zero online shopping and 5 + WFH/2–3 online shopping areas), implying that they are positively associated with one another. However, in certain dense areas, such as 5 + WFH/0 online shopping, 3 to 4 days WFH/0 online shopping, and 0 WFH/1-time online shopping, the positive correlation is less evident. This illustrates the nuanced and potentially complex nature of the relationship between WFH frequency and online shopping behavior. Furthermore, this aligns with our estimated results, where we found a causal relationship more significant than a correlation. Moving on to latent constructs, in creating our first latent constructs, the correlation matrix analysis navigated us to explore how people view working from home, especially in terms of their productivity at home. To understand changes in productivity, we asked respondents to rate their work output at home during the pandemic compared to before, using a 6-Likert scale from “significantly lower” to “significantly higher”. For those who noticed changes, we asked them about the factors contributing to decreased productivity and those fostering increased productivity. Respondents selected the factors increasing/decreasing their WFH productivity among the set of reasons that they were given. These reasons are reported through a binary scale. We acknowledge that capturing these reasons by a binary scale limits the variability, and information available compared to a 5-Likert scale. The latent WFH comfort is a measure of how convenient employees perceive working from home. This latent is derived from factors that contribute to an increase in productivity. Figure 5 presents the indicators for WFH comfort and other psychological latent constructs. More than 60% of individuals who reported higher productivity attributed it to the absence of commuting time, while around 25% credited the ability to get more sleep. Another frequently reported factor influencing productivity improvement is the flexibility of working hours, selected by 50% of respondents. Conversely, WFH unproductiveness aims to capture how employees perceive their lack of productivity when working from home. Approximately 40% of respondents identified more multitasking as a significant reason for decreased productivity (see Fig. 5 ). Other notable factors contributing to WFH unproductiveness are challenging communication with coworkers and an uncomfortable workspace, both selected by a considerable percentage of respondents. In the context of online shopping, we sought insights into respondents’ expectations regarding the frequency of ordering items for delivery post-COVID-19. Those expecting increased online shopping were asked about the driving factors, while those expecting a decrease were questioned about the reasons behind this shift. Respondents were provided with various reasons, enabling a thorough exploration of the diverse motivations and inhibitors influencing their expectations and behaviors in the evolving online shopping trend after the COVID-19 pandemic. The online shopping enjoyment latent construct represents the overall positive and pleasurable experiences individuals derive from engaging in online shopping activities. It may include the satisfaction, joy, and fulfillment associated with the online shopping process. As shown in Fig. 5 , Notably, time-saving, with about 90% of respondents acknowledging it as a motivating factor, and the convenience of 24/7 shopping, noted by around 80% of participants, contribute significantly to the enjoyment of online shopping. However, the option to read reviews before making a purchase was the least selected motivation, with only 45% finding it influential among those expecting more online shopping in the future. The inconvenient online shopping latent construct reflects the challenges and hindrances individuals face that make the online shopping experience less convenient and, consequently, less enjoyable. For those anticipating less online shopping, more than 90% expressed that the desire for immediate access to purchases discourages them from shopping online frequently (see Fig. 5 ). Additionally, 65% mentioned shipping fees as a significant inhibitor, while difficulty with online shopping platforms was the least chosen reason, selected by approximately 10% of respondents. In addition to psychological latent variables, we introduced a latent variable named home-technology availability, aiming to measure the overall accessibility and sufficiency of technological resources within individuals’ home environments. To construct this latent variable, we used participants’ responses to the following questions: 1) Do you currently have high-speed internet service at your home? 2) Which of the following devices do you have access to at your home? Please select all that apply: Computer (desktop or laptop), Tablet, Smartphone; and 3) Do you have enough internet-connected devices in your home for all the people who want to use them? Fig. 6 illustrates the distribution of significant indicators used in estimating the “Home-Technology Availability” latent variable. About 98% of individuals have access to high-speed internet and computers (i.e., laptops or desktops) at home. Moreover, 97% of individuals have adequate internet-connected devices at home for all intended users. Table 2 provides definitions and summary statistics for key exogenous explanatory variables used in estimating the main outcomes of the study. Table 2 Definition and summary statistics of explanatory variables used in the final model Variable Definition Mean Std. Dev. Individual Characteristics: Age age 48.09 13.34 Age: 60 or above 1: if the individual is 60 years old and above; 0 o/w 0.21 0.41 Race: Hispanic 1: if the individual is Hispanic; 0 o/w 0.12 0.33 Race: Black/African American 1: if the individual is Black/African American; 0 o/w 0.09 0.29 Having physical difficulty 1: if the individual has physical difficulty; 0 o/w 0.02 0.14 Household Characteristics : High income 1: if the household income is higher than $ 100,000; 0 o/w 0.50 0.50 Children: 13–17 years old Number of 13–17-year-old children in the household 0.22 0.51 Travel Habits : Commute mode: Private vehicle 1: if the individual commutes with a private vehicle; 0 o/w 0.65 0.48 Active mode usage Frequency of walk, bike, and shared bike usage in the last 7 days 3.36 3.33 Job-related Factors : Job Category: Education 1: if the individual works in the Education industry; 0 o/w 0.14 0.35 Employer flexibility 1: if the individual always has the option to WFH; 0 o/w 0.42 0.49 3.3. Model Specification This study explores the interplay between WFH and online shopping behaviors using the Generalized Structural Equation Model (GSEM), a statistical tool for analyzing complex relationships among variables. GSEM extends Structural Equation Modeling (SEM) to handle a wider range of data types and distributions, proving valuable for diverse outcomes like categorical or ordinal variables. The GSEM serves as a comprehensive framework for modeling relationships between latent constructs, observed factors, and observed outcomes within a unified structure. We introduce ordered logit links to GSEM due to the ordinal nature of our primary outcomes: WFH frequency, \({Y}_{1}\) , and online shopping frequency, \({Y}_{2}\) . The following formulation represents the cumulative probabilities associated with different response categories. \(P\left({Y}_{1}\le j\right)= P \left({Y}_{1}\le j∣{L}_{1}, {L}_{2},{L}_{3}, X\right)\) \(\forall j\in \{1, 2, \dots ,{J}_{1}\}\) (1) \(P\left({Y}_{2}\le k\right)= P\left({Y}_{2}\le k∣{L}_{4}, {L}_{5}, X, {Y}_{1}\right)\) \(\forall k\in \{1, 2, \dots ,{J}_{2}\}\) (2) Where \({Y}_{1}\) and \({Y}_{2}\) denote WFH and online shopping frequency variables, respectively. \(j\) and \(k\) are the number of discrete values that WFH and online shopping frequency variables can take, respectively. \(\varvec{L}\) represents latent constructs, and \(\varvec{X}\) is the vector observed factors. In equation ‎(1), \(P({Y}_{1}\le j)\) represents the probability that the WFH frequency, \({Y}_{1}\) , falls within or below a specific category j. This probability is conditioned on WFH, and technology-related latent constructs, \({L}_{1}, { L}_{2}, { L}_{3}\) and observed factors, \(\varvec{X}\) . Similarly, in equation ‎(2), \(P({Y}_{2}\le k)\) shows the probability that the online shopping frequency, \({Y}_{2}\) , is within or below category k, conditioned on online shopping related latent constructs, \({L}_{4}\) and \({L}_{5}\) , observed factors, \(\varvec{X}\) , and the estimated WFH frequency, \({Y}_{1}\) . Within the GSEM framework, the measurement model establishes connections between latent constructs and the observed variables. In this study, latent constructs, \(\varvec{L}\) , serve as critical components, representing underlying factors that shape WFH and online shopping behaviors. While these constructs are not directly observable, they are inferred from a set of observed variables (i.e., indicators), creating a link between theoretical concepts and empirical data. This is expressed as: Where X is a vector of the observed variables, Λ is the matrix of factor loadings, indicating how each observed variable is associated with the latent constructs, and Φ is the vector of latent constructs. These are underlying factors that are not directly observable but inferred from the observed variables. Δ is the vector of error terms or measurement errors associated with observed variables. It captures variability not explained by the latent constructs. The structural model explores the relationships among latent constructs, however, since we do not consider any relationship between latent constructs, the structural model simplifies to: Where \(\zeta\) is the vector of error terms or disturbances associated with the latent constructs. These account for any unobserved factors or variability in the latent constructs. The estimation process employs Maximum Likelihood Estimation (MLE), a statistical method seeking parameter values that maximize the likelihood of observing the given sample data. The likelihood function, \(\varvec{\Gamma }\), quantifies how well the model explains the observed data and is calculated as: \(f\left({x}_{i}|{\Theta }\right)\) represents the probability density function of the observed data \({x}_{i}\) given the model parameters \({\Theta }\) . The product is taken over all observed data points. The MLE process is performed iteratively to adjust parameter values, optimizing the likelihood. We used a specialized software, R with Lavaan package to perform the MLE process for estimating the parameters. 4. RESULTS To cover the complexity of the underlying factors before looking into the broader implications reflected in the main outcomes, we will first discuss the GSEM outcomes related to the latent constructs. Then, we will proceed to the results for the main outcomes of our study. 4.1. Latent Constructs Table 3 displays the results for the latent constructs. Cells marked with “_” indicate that the corresponding row variable is set to 1 during the estimation process. Fixing one indicator of a latent construct to 1 in GSEM enhances interpretability by providing a standardized scale for the latent variable. This fixed loading serves as a reference point, making it easier to understand the relative importance of each indicator in contributing to the overall construct. Additionally, it helps compare the contributions of different indicators within the same construct, facilitating clearer insights into the factors influencing the latent variable. All variables are significant at 99%, 95%, and 90% level. The latent variable WFH comfort captures the perceived reasons for increased productivity when working from home. It draws on a set of indicators, each offering insight into the factors contributing to a more comfortable and efficient remote work experience. The findings highlight the significant role of several factors: the perceived availability of extra time for sleep, indicating the potential positive impact on overall well-being (Forbes, 2020 ); the belief in a more comfortable home workspace, emphasizing the importance of physical surroundings for productivity (Apollo Technical, 2023 ); the reduction of distractions, underlining the value of a focused work environment (Gordon, 2023 ); the flexibility in working hours, allowing for personalized schedules (McKinsey & Company, 2022 ); and the absence of commuting time, emphasizing the time-saving benefits of remote work (Barrero et al., 2021 ). As seen in Table 3 , in terms of the impact significance on the WFH comfort latent variable, the results highlight that no commuting time and flexible working hours hold the most significance, while getting more sleep has the least impact. WFH unproductiveness latent variable captures perceived reasons for decreased productivity while working from home. Among the prominent contributors are responsibilities linked to dependents or childcare, highlighting the intricate balance individuals must maintain while working from home. Employees who take care of someone else at home might face numerous interruptions or even reduced working hours (Igielnik, 2021 ; Modestino et al., 2021 ). Additionally, discomfort in the home workspace emerges as a significant factor, acknowledging the impact of the physical environment on productivity (Toniolo-Barrios and Pitt, 2021 ). The perception of reduced job demands for remote workers sheds light on the potential challenges individuals face in maintaining a sense of engagement and motivation in a remote setting (Nur et al., 2019 ). Moreover, being more multi-tasking can lead to divided attention, increased stress, and reduced efficiency, contributing to the perception of decreased productivity while working from home (Park et al., 2011 ). While difficulties in communication with co-workers underscore the importance of effective virtual collaboration (Jämsen et al., 2022 ). Regarding the significance of the impacts of each of these indicators, the results show that care responsibilities and multi-tasking have the most impact on WFH unproductiveness, while job demanding less has the least impact. Table 3 Indicators of latent constructs Latent constructs Indicator Coefficient Z_value Work-from-home comfort More comfortable workspace at home Fixed to 1 Fewer distractions at home 1.002*** 5.191 No commute time 1.208*** 5.503 Flexible working hours 1.022*** 4.447 Get more sleep 0.662*** 4.077 Work-from-home unproductiveness Lack of comfortable workspace at home Fixed to 1 More multitasking 1.206*** 4.343 Job demanding less 0.47*** 3.154 Child, dependent, or elderly care 1.834*** 3.338 Difficult communication with co-workers 0.588** 2.486 Online shopping enjoyment Saves time Fixed to 1 Can read reviews before purchase 0.8* 1.881 Have a wider variety of choices 0.957** 2.491 Avoid going to stores 0.663** 2.436 Can shop 24/7 0.699** 2.953 Online shopping inconvenience Return process frustrating Fixed to 1 Shipping fees 1.205*** 3.233 Difficulty with online shopping platform 0.958*** 4.22 Inaccurate online information 0.348*** 2.305 Want immediate access to purchases 1.346*** 3.171 Home-technology availability Sufficient internet-connected devices for all household members Fixed to 1 High-speed internet service 1.287*** 6.055 Computer (i.e., laptop or desktop) 2.088*** 8.164 Note: ***, **, * ==> Significance at 99%, 95%, 90% level The online shopping enjoyment latent variable is developed based on motivations that drive more frequent online shopping since the pandemic compared to what was before. Motivations that turned out to be significant are the ability to shop 24 hours a day, providing flexibility in shopping times (Smart Insights, 2020 ); the ability to save travel time, allowing for efficient shopping without commuting (Kumar and Kashyap, 2018 ); the motivation to avoid going to stores, emphasizing the convenience of online transactions (Emrich et al., 2015 ); the ability to read reviews before shopping, enabling informed decision-making; and the numerous options available online, providing a wide range of choices (Jiang et al., 2013 ). These motivations collectively contribute to the perception of online shopping as a pleasant and convenient experience. Among these motivations, the results show that saving time and having a wider variety of choices online hold the most significant impacts. Conversely, the inconvenient online shopping latent variable is constructed based on factors that hinder frequent online shopping. Significant inhibitors include frustrating return process, indicates dissatisfaction with the return procedure (Duarte et al., 2018 ); concerns about shipping fees, which may discourage online purchases (Lewis, 2006 ); the need for immediate access to the product, suggests a preference for immediate access (Dublino, 2023 ); the perception that information online is not accurate, highlights concerns about the reliability of information available online (Dublino, 2023 ), and difficulty with the online shopping platform, reflects challenges in navigating online platforms (Smart Insights, 2020 ). These inhibitors collectively contribute to the perception of online shopping as inconvenient. Furthermore, the findings indicate that the strongest inhibitors are the need for immediate access to the product and a frustrating return process, respectively. In contrast, inaccurate online information emerges as the weakest factor. Understanding the motivations and inhibitors of online shopping provides valuable insights into the factors influencing online shopping behavior, facilitating targeted strategies to enhance the overall online shopping experience. Lastly, the technology available at home latent variable is positively and significantly associated with three factors: high-speed internet, sufficient devices at home, and computers at home. This construct reflects the acknowledgment that the presence of these technological elements in one’s home environment contributes positively to the overall level of technological accessibility. Specifically, high-speed internet indicates the availability of a robust internet connection, facilitating seamless connectivity (Bournea, 2022 ). Sufficient devices at home emphasize the importance of having an adequate number of devices, ensuring accessibility and convenience for various tasks (HAYES, 2022 ). Finally, the computer-at-home variable highlights the significance of possessing a personal computer, a fundamental tool for a wide range of professional and personal activities (Pew Research Center, 2007 ). The results show that computers at home, high-speed internet, and sufficient devices at home have the most significant impact on the technology available at home latent variable, respectively. 4.2. Main Outcomes Table 4 presents the estimated results for two main outcomes, WFH and online shopping frequency. A complete set of variables and variables interactions were examined, and a variety of socio-demographics, commute and non-commute trip features, job-related factors, and attitudes/perceptions were found to be significant to WFH and online shopping choices. All the variables are statistically significant at 95%, 90%, and 85% levels. Cells marked with “_” indicate that the specific row variable does not directly impact the outcome variable in the corresponding column. A comprehensive discussion of the model results for each outcome is provided, as well as additional insights into the similarities and differences between WFH and online shopping behaviors. The similarities and differences are discussed in terms of the variables that influence WFH and online shopping frequency at the same time and those that are significant only to one of these choices. Table 4 Jointly modeled WFH and online shopping frequency Variable WFH Freq. Online Shopping Freq. Coefficient z-value Coefficient z-value Individual Demographics Age: 60 or above \(\times\) Age -0.003* -1.846 Race: Hispanic -0.558*** -3.454 Race: Black African 0.383** 2.127 Have Physical Difficulty 0.607* 1.815 0.917** 2.274 HH Characteristics High-income 0.329*** 3.027 0.375*** 3.532 Number of Children Aged 13–17 -0.231** -2.212 Job-related Factors Job Category: Education -0.302* -1.784 Employer Flexibility 1.078*** 8.864 Travel Habits Privet Vehicle Commuter -0.951*** -7.57 Active Mode Usage 0.025* 1.704 Latent Constructs WFH Comfort 0.83*** 2.857 WFH Unproductiveness -1.052* -1.802 Online Shopping Enjoyment 0.795** 2.289 Online Shopping Inconvenience -4.172*** -3.851 Home Technology Availability 3.539*** 4.44 Interaction WFH Freq. 0.101** 2.35 Threshold parameters Mu (01) -1.012*** -6.292 -0.213 -1.385 Mu (02) -0.288 -1.83 0.59*** 3.791 Mu (03) 0.51*** 3.203 1.754*** 9.71 p_value 0.000 Comparative Fit Index (CFI) 0.861 Tucker-Lewis Index (TLI) 0.916 Root Mean Square Error of Approximation (RMSEA) 0.025 Standardized Root Mean Square Residual (SRMR) 0.064 ***, **, * ==> Significance at 95%, 90%, 85% level. 4.2.1. Individual Characteristics The estimated results show a negative correlation between age and online shopping frequency with people who are more than 60 years old are less likely to shop online (Statista, 2022 ). In fact, this generation may not be into using ICT devices which results in their unfamiliarity and discomfort with ICT and may decrease their overall propensity toward online shopping (Vaportzis et al., 2017 ). Despite the frequent discussion of previous studies, the results did not reveal any statistically significant differences between gender groups in WFH and online shopping frequency (Mafé and Blas, 2007 ). Among races, Hispanic employees were found to be less likely to WFH. This could be because such individuals likely work in construction, maintenance, and manufacturing occupations which require their presence in the field (Census, 2015 ). The online shopping results, however, highlighted the tendency of Black/African American people to shop online. In fact, the potential racial profiling and false accusation of shoplifting in stores led minorities to avoid going to stores (Elan, 2021 ). Individuals with physical difficulties were found to be more likely to WFH and shop online which could be because of the challenges they face when traveling, performing the job at the workplace, and shopping in stores (Bohra and Willingham, 2021 ; DIVERSEability Magazine, 2018; Peters et al., 2004 ). 4.2.2. Household Characteristics Among household characteristics, High-income households were found to be positively associated with both WFH and online shopping frequency. This association can be explained by a couple of potential facts. First, higher-income households may have better access to WFH and online shopping technologies, resulting in a higher frequency of using such technology to both work and shop at home (Swenson and Ghertner, 2020 ). Second, as the household income rises, the number of workers in the household may increase. Hence, due to the lack of time available to travel to work or stores, such households have a higher propensity for WFH and online shopping (Sener and Reeder, 2012 ). Third, high-income workers may hold higher positions in business, giving them the bargaining power to negotiate for workplace flexibility (Peters et al., 2004 ; Shabanpour et al., 2018 ). The results showed that the presence of 13- to 17-year-old children in the household has a negative impact on online shopping frequency. Insights from Pew Research Center report emphasize that teenagers tend to prefer in-store shopping experiences over online alternatives. This observed preference among teenagers contributes to the lower prevalence of online shopping in their households (Desilver, 2013 ). 4.2.1. Job-related Factors The job category is very important in WFH decision-making. Those working in education were found to be less likely to WFH frequently. The data source used for model estimation was collected in the fall of 2021 when the Centers for Disease Control and Prevention (CDC) recommended that US schools reopen for in-person learning regardless of their ability to implement all COVID-19 protective measures (The Guardian, 2021 ; U.S. Department of Education, 2021 ). Since then, education employees, the majority of whom are teachers, have been required to work in person at least some of the time. Aside from employee preferences for WFH, the frequency of this ICT choice is also determined by the employer’s decision. The findings revealed that employers’ flexibility toward employees’ workplaces is significant to WFH incidence. The constant availability of the option to WFH was demonstrated to increase the frequency of this behavior, which is consistent with the findings of Balbontin et al. (Balbontin et al., 2021 ). 4.2.1. Travel Habits Regarding travel habits, mode choice habits were found to be significantly correlated with WFH and online shopping decisions. The findings revealed that those who commute by private vehicle are less likely to WFH. One possible explanation is that having a private vehicle causes daily commute convenient, resulting in a lower frequency of WFH (Balbontin et al., 2021 ; Mohammadi et al., 2022 ). On the other hand, it was found that those who frequently use active modes including walking, biking, and shared bikes as their primary means of transportation are more likely to shop online. This could be due to a combination of factors. The first consideration is the potential difficulty of carrying shopping bags while walking or biking (Ramadan et al., 2018 ). Second, The challenges associated with active mode transportation may contribute to a higher likelihood of individuals opting for online shopping instead of traveling. Third, active mode users may be environmentally friendly and do online shopping to eliminate auto travel (Sener and Bhat, 2011 ). 4.2.2. Latent Constructs The findings indicate that home technology availability plays a significant role in influencing the frequency of WFH. This suggests a positive and indirect association between the presence of high-speed internet services, possession of a laptop or desktop, and having sufficient internet-connected devices for all household members at home with the incidence of WFH. Essentially, these technological factors contribute to making information and communication technology (ICT) more accessible, resulting in a more convenient WFH experience. This could be because a well-equipped technological environment fosters the accessibility and ease of remote work, making it a more feasible and attractive option for individuals (Blitchok, 2022 ). The positive and significant association between WFH frequency and the latent variable WFH comfort indicates that individuals who experience a greater level of comfort in their remote work environment are more inclined to engage in WFH more frequently. This comfort is influenced by various factors, including the extra time for more sleep, the convenience of avoiding commuting, the presence of a comfortable and well-equipped home workspace, the flexibility offered by personalized working hours, and the reduced distractions experienced at home. When individuals perceive WFH as a comfortable and productive arrangement, it naturally becomes a preferred choice. This association highlights the intricate interplay between perceived benefits and the decision to embrace remote work regularly, emphasizing the need for environments that foster both comfort and productivity for widespread WFH adoption. (Barrero et al., 2021 ; Jain et al., 2022 ; Mohammadi et al., 2022 ). Conversely, the negative association between WFH frequency and the latent variable WFH unproductiveness suggests that as the perceived unproductiveness of remote work increases, individuals are less likely to choose WFH more frequently. In other words, when individuals face challenges and perceive their remote work as unproductive, they are inclined to avoid engaging in WFH more often. This negative relationship indicates that factors contributing to WFH unproductiveness, such as caregiving responsibilities, an uncomfortable home workspace, multi-tasking, communication difficulties, and the perception of reduced job demands, act as deterrents to the frequency of remote work. The higher the perceived obstacles to productivity, the lower the likelihood of individuals choosing WFH with greater frequency (Jain et al., 2022 ). In the context of online shopping, the latent variable of online shopping enjoyment was shown to be significantly and positively correlated with the incidence of this ICT choice. This indicates that the enjoyment derived from online shopping experiences, characterized by constant shopping availability, the ability to read reviews before making a purchase, the convenience of skipping travel to stores, time savings, and the availability of a variety of choices online, all play pivotal roles in influencing individuals’ decisions to shop online more frequent. These factors contribute to a positive online shopping experience, fostering convenience and accessibility, ultimately shaping preferences for frequent online shopping (Duarte et al., 2018 ; Emrich et al., 2015 ; Jiang et al., 2013 ). In contrast, the results show that inconvenient online shopping significantly decreases the probability of frequent online shopping. This reveals that the frustrating return process, shipping fees, the need for immediate access to the product, difficulty with the online shopping platform, and inaccurate online information are all barriers to frequent online shopping. The primary goal of using ICT to shop and work is to provide convenience; thus, when it is perceived as inconvenient due to the aforementioned factors, the propensity to use it decreases (Amato-McCoy, 2016 ; Jack et al., 2019 ; Tyrrell, 2022 ). 4.2.2. Interaction of WFH and Online Shopping The results show a positive and significant causal relationship between WFH and online shopping. In other words, as individuals engage in more WFH activities, there is a corresponding and significant rise in their online shopping behavior. This interaction can be explained by several factors. Firstly, as discussed in section ‎1, individuals may be inclined to engage in online shopping when working from home, especially when spending extended periods in front of the computer. The seamless integration of online shopping into the WFH environment offers individuals the convenience to browse and make purchases during breaks or downtime. Secondly, the elimination of natural opportunities to trip chain shopping episodes, typically associated with commuting, when working from home potentially contributes to an increase in online shopping frequency. The absence of commute-related constraints allows individuals to engage in online shopping more readily, shaping a positive causal relationship between the frequency of working from home and the frequency of online shopping (Golob and Regan, 2001 ; PYMNTS, 2019 ). 4.2.3. Goodness of Fit In this study, we explored how different latent variables interact dynamically, without starting with specific expectations. The Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) show noteworthy values of 0.861 and 0.916, respectively, highlighting the model’s proficiency in navigating intricate stochastic relationships. The Root Mean Square Error of Approximation (RMSEA) impressively maintains a low threshold at 0.025, indicating a meticulous alignment between model predictions and observed data. Furthermore, the Standardized Root Mean Square Residual (SRMR) at 0.064 signifies a reasonable fit. In the absence of pre-established hypotheses, these results collectively shed light on the interplay between latent variables and outcomes. This prompts careful consideration of the model's ability to unravel intricate relationships, inviting further scholarly exploration and discourse. 5. DISCUSSION Discussing the results from a policymaker’s perspective, it is important to acknowledge both opportunities and challenges of WFH and E-commerce growth. With more people working from home, there is a potential decrease in the demand for daily commuting, leading to reduced traffic congestion, lower fuel consumption, and fewer carbon emissions (Hensher et al., 2022 ; Shabanpour et al., 2018 ). However, the shift to remote work could influence the demand for commercial office space, impacting transportation infrastructure designed to support commuting to and from central business districts (Mischke et al., 2023 ). Moreover, the reduced demand for public transportation services may pose financial challenges for transit agencies, potentially resulting in service cuts that affect those who depend on public transportation (Javadinasr et al., 2022 ). Online shopping is set to exceed $ 1.8 trillion in sales by 2023 (Bonde and Burno, 2019 ). With a larger market share and fewer barriers compared to traditional commerce, e-commerce fuels entrepreneurship and inspires businesses of all sizes to join the competition. Although online shopping brings a better economy, higher employment rate, and convenience to customers, its impact on city logistics and transportation systems is debatable (Savelsbergh and Van Woensel, 2016 ). The diffusion of online shopping would increase congestion, illegal parking of delivery trucks, emissions, and problems for logistic providers, due to the widespread presence of trucks for deliveries within the urban environment (Ma et al., 2022 ). Policymakers need to find a balance between promoting remote work and online shopping activities and addressing their potential challenges. The findings of this study help them to gain insights into effective strategies in both cases if they need to promote or demote these activities. Perhaps the most important conclusion that can be drawn from the findings is the positive causal interrelationship between WFH and online shopping frequency. It suggests that working from home reinforces e-commerce growth. Policymakers can leverage this casual interrelationship to regulate E-commerce. For instance, businesses can increase their market share if they target remote workers as their online customers (PYMNTS, 2023 ). The findings highlight the influence of several factors on WFH behavior. Particularly, the employer’s flexibility toward remote work was found to be significant in employee’s decisions on WFH frequency. The frequency of WFH would increase when employers actively support it by granting employees the flexibility to choose their work environment. As highlighted by McKinsey, since the COVID-19 pandemic pushed the majority to WFH, many employees have requested more workplace flexibility (De Smet et al., 2022 ). Consequently, it becomes crucial for companies to develop WFH policies that effectively balance the satisfaction of both employers and employees. To implement a successful WFH program, employers should consider policies that accommodate the needs and preferences of their business and their workforce. This may involve offering guidance and resources to facilitate remote work, such as ensuring access to essential technology and tools, while also addressing potential productivity challenges encountered at home. Employees need adequate equipment and resources at home to accomplish their work effectively. Therefore, employers should evaluate the required technology in light of budget and employee productivity at home (Bayern, 2020 ). Ensuring that employees have access to reliable internet connections, appropriate software, and comfortable workstations can contribute to their productivity and overall job satisfaction. Furthermore, the availability of childcare facilities and support can enhance the WFH experience for working parents. According to a recent report by Harvard Business School, the effects of childcare have intensified since the COVID-19 pandemic has eliminated the safety net of schools and employer-provided daycare for working parents. Given that one-third of the US workforce includes parents with children under the age of 14, employers can consider incorporating a solid form of childcare solutions into their business infrastructure (Modestino et al., 2021 ). This can include providing resources for remote learning assistance, flexible work schedules, or even subsidizing external childcare options to support working parents in balancing their professional responsibilities with their childcare responsibilities (Del Boca et al., 2020 ). In the online shopping context, the findings highlight the role of convenience in online shopping frequency. Convenience is an important factor that may include various elements contributing to a positive and satisfying online shopping experience. For instance, taking proactive measures to optimize online shopping platforms and ensure they meet the evolving expectations of customers. For example, retailers can enhance their online platforms by keeping them up-to-date, visually appealing, and equipped with user-friendly features. This necessitates regular updating of the website’s design, and incorporating a powerful search engine that allows customers to find products easily (Beauchamp and Bednarz, 2010 ; Duarte et al., 2018 ). Moreover, implementing autocomplete functionality makes the search process efficient and time-saving (Kollmann et al., 2012 ). Online shopping platforms can also be improved by providing clear and complete product information. Detailed descriptions, high-quality product images, and accurate specifications play a crucial role in helping customers make informed purchase decisions (Duarte et al., 2018 ; Jiang et al., 2013 ). Additionally, real, and legitimate reviews provide valuable insights into the product’s quality, performance, and suitability, building trust and confidence among potential buyers (Duarte et al., 2018 ). Moreover, an easy check-out process can promote customer experience with online shopping platforms. Simplifying the steps required to complete a purchase, offering various secure payment options, and minimizing form filling can significantly enhance the overall shopping experience (Berry et al., 2002 ; Duarte et al., 2018 ). Furthermore, utilizing the power of data-driven product recommendations significantly elevates convenience in online shopping. By analyzing customer behavior, preferences, and purchase history, retailers can offer personalized suggestions tailored to everyone’s interests and needs. This facilitates the shopping process and helps customers discover relevant products they might have otherwise missed, increasing the likelihood of making additional purchases (Lee and Kwon, 2008 ). Online retailers can increase their market share by providing robust services to their customers. For example, offering a flexible return policy can significantly enhance the return process for customers. This can include free return services, allowing sufficient time for customers to make return decisions, and offering convenient drop-off locations (Tyrrell, 2022 ). However, it is worth noting that the “buy online, return in store” policy, while offering convenience to customers, can impact retail business profits due to added costs, ECR retail loss said (Jack et al., 2019 ). In addition to facilitating returns, online retailers can prioritize effective customer service channels. Having available agents to engage in online chat, phone conversations, and email correspondence significantly enhances the overall customer experience (Tyrrell, 2022 ). Interestingly, according to AMC Technology, 57% of customers prefer to contact retail businesses via online platforms such as social media or email rather than using voice-based customer service (AMC Technology, 2022 ). Furthermore, addressing concerns related to delivery fees and times is crucial for online retailers. Providing customers with accurate delivery time estimates and offering expedited delivery services can foster trust and confidence in the retailer. Chain Store Age emphasizes the importance of meeting customers’ delivery expectations, as it serves as a fundamental factor in building a strong and loyal customer base (Amato-McCoy, 2016 ). 6. CONCLUSION The growing popularity of WFH and online shopping has presented new opportunities for convenience, flexibility, and sustainability. This study is the first to our knowledge that explores the interplay between WFH and online shopping, the two popular ICT-based behaviors, since the COVID-19 pandemic. This study also examines the recent evolutions of WFH and online shopping activities using data from a comprehensive survey administered in late 2021 by our research group. Our analysis assesses the impacts of individual and household characteristics, job-related factors, travel habits, home-technology features as well as attitudinal factors. We employed a Generalized Structural Equation Model (GSEM), which accounts for both observed and unobserved (i.e., latent) variables, and thus captures complexities in decision-making. The impacts of attitudinal factors were captured indirectly through latent variables. We considered four psychological latent constructs: WFH comfort, WFH unproductiveness, online shopping enjoyment, and online shopping inconvenience. Additionally, we constructed a latent variable named home-tech availability based on accessibility to different attributes of technology like high-speed internet and computers at home. “WFH Comfort” measures positive perceptions, such as a more comfortable home workspace, fewer distractions at home, and no commuting, which has been shown to cause a higher frequency of remote work. Conversely, “WFH Unproductive” represents negative WFH attitudes including more multi-tasking, lack of comfortable workspace, and difficult communication with co-workers, which decreases WFH frequency. Furthermore, access to adequate technological resources at home plays a role in the frequency of WFH and online shopping practices. In the context of online shopping, the “Online Shopping Enjoyment” latent variable is a measure of positive online shopping experience impacted by timesaving and flexibility, offering a variety of options, and other factors. Online shopping enjoyment drives frequent engagement, while online shopping inconvenience latent variable, which is associated with perceived challenges of this activity, decreases engagement frequency. The results show that such challenges include product accessibility, return process, and delivery fees. The findings demonstrate a significant positive causal relationship between WFH and online shopping such that the frequency of WFH positively affects the incidence of online shopping. For observed factors, we found demographics, such as age and ethnicity, influence WFH and online shopping behaviors, with Hispanic employees less likely to WFH and 60-year-old and above individuals less likely to shop online, while Black/African American individuals show a preference for online shopping. Physical difficulties and high-income households are associated with higher frequencies of both WFH and online shopping. Additionally, job category, employer flexibility, and commute mode choice habits were identified as important factors in WFH decisions. This study can be extended in some ways for future research. First, those employees who do not have the option to WFH (i.e., 0 WFH days) and those who are required to do so full-time (i.e., 5 WFH days out of 5 working days) were excluded from this study due to the predetermined choice of WFH. Future research can investigate such employees’ online shopping habits and how their WFH frequency influences these habits. Second, while our study provides valuable insights into the dynamics of WFH and online shopping behaviors, it is important to acknowledge certain limitations primarily stemming from the survey design. For example, our study incorporates crucial attitudinal factors, such as WFH productivity positive/negative factors and motivations/inhibitors related to online shopping frequency, however, we faced limitations due to our measurement approach. The use of a binary scale, while practical, constrains the variability and depth of information compared to a more nuanced 5-Likert scale. This limitation might affect how detailed and clear our analysis is and how well we can understand the small, important details in how people feel and think. Additionally, our survey design did not collect information on potential influential factors in WFH frequency, such as extra pay, benefits, or other incentives. Future research endeavors could benefit from employing more nuanced measurement scales and exploring a broader array of factors to enhance the comprehensiveness of our understanding of these domains. Declarations This research is conducted by ASU and UIC and funded by grants from the National Science Foundation and the U.S. Department of Transportation. This study has been reviewed and approved by both the ASU Institutional Review Board and the UIC Institutional Review Board (Protocol #568) for the protection of study participants. If you have any questions about your rights as a subject/participant in this research, or if you feel you have been placed at risk, you may contact the Institutional Review Board at ASU’s Office of Research Integrity and Assurance, at (480) 965‑6788, or the UIC Institutional Review Board, at (312) 996-1711. ACKNOWLEDGEMENT This research was supported in part by the National Science Foundation (NSF) RAPID program under grants no. 2030156 and 2029962, awarded to the University of Illinois at Chicago and Arizona State University. Also, this study was supported by the Center for Teaching Old Models New Tricks (TOMNET), a University Transportation Center sponsored by the U.S. Department of Transportation through grant no. 69A3551747116, as well as from the Knowledge Exchange for Resilience at Arizona State University. This COVID-19 Working Group effort was also supported by the NSF-funded Social Science Extreme Events Research (SSEER) network and the CONVERGE facility at the Natural Hazards Center at the University of Colorado Boulder (NSF Award #1841338) and the NSF CAREER award under grant no. 155173. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders. Conflicts of Interest None Author Contribution The authors confirm their contribution to the paper as follows: study conception and design: M. M., A. D., R. S., and A. M.; data collection: M. M., D. S., S. D., R. P., and A. M.; analysis and interpretation of results: M. M., A. D., R. S., and A. M.; draft manuscript preparation: M. M., A. D., R. S., and S. A. All authors reviewed the results and approved the final version of the manuscript. The authors do not have any conflicts of interest to declare. References Amato-McCoy, D.M., 2016. 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Transp Res Part C Emerg Technol 6, 47–68. https://doi.org/10.1016/S0968-090X(98)00007-2 Walls, M., Safirova, E., Jiang, Y., 2007. What Drives Telecommuting?: Relative Impact of Worker Demographics, Employer Characteristics, and Job Types. https://doi.org/10.3141/2010-13 111–120. https://doi.org/10.3141/2010-13 Footnotes Some individuals selected multiple races as their identification, hence the summation of the percentages of all choices is not equal to 100. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Jun, 2024 Read the published version in Transportation → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3974111","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274562081,"identity":"ead3d9be-3b83-4892-8122-d9f447797517","order_by":0,"name":"Motahare Mohammadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYHACNiD+b8fPwNgAZDATrYU5WbKBVC2MGw6AOURo0Z12+NiDnzlszMY3khs/MFRYJzYQ0mJ2Oy3dsHcbD5/ZjcRmCYYz6cRoyTGT4N0mwQzU0sbA2HaYOC2Sf7cZMG6eAdLyj0gt0rzbEhg3SIC0NBClJS1NWnbbgWSJMw+bJRKOpRsToSX5mOTbbQfs+NvTH374UGMtS1ALKkggTfkoGAWjYBSMAlwAAO36PnyGwnVEAAAAAElFTkSuQmCC","orcid":"","institution":"University of Illinois at Chicago","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Motahare","middleName":"","lastName":"Mohammadi","suffix":""},{"id":274562082,"identity":"f54019ee-afed-4d64-a882-96fe0cf1ec6c","order_by":1,"name":"Amir Davatgari","email":"","orcid":"","institution":"University of Illinois at Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Amir","middleName":"","lastName":"Davatgari","suffix":""},{"id":274562083,"identity":"6157cb25-c8d0-45ba-b7aa-6ab5413399e0","order_by":2,"name":"Sina Asgharpour","email":"","orcid":"","institution":"University of Illinois at Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sina","middleName":"","lastName":"Asgharpour","suffix":""},{"id":274562084,"identity":"6c8ee78e-96c8-45ac-be41-9ff460dae080","order_by":3,"name":"Ramin Shabanpour","email":"","orcid":"","institution":"University of North Florida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ramin","middleName":"","lastName":"Shabanpour","suffix":""},{"id":274562085,"identity":"273b0f9f-537b-4dba-aeef-8bb0ab54c125","order_by":4,"name":"Abolfazl Mohammadian","email":"","orcid":"","institution":"University of Illinois at Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Abolfazl","middleName":"","lastName":"Mohammadian","suffix":""},{"id":274562086,"identity":"41cfff04-be74-474a-9306-768582032ddf","order_by":5,"name":"Sybil Derrible","email":"","orcid":"","institution":"University of Illinois at Chicago","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sybil","middleName":"","lastName":"Derrible","suffix":""},{"id":274562087,"identity":"5b3309a6-d900-464f-be03-134ae2449465","order_by":6,"name":"Ram Pendyala","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ram","middleName":"","lastName":"Pendyala","suffix":""},{"id":274562088,"identity":"14f3fd78-bdbe-4c61-b5ec-621699e2b055","order_by":7,"name":"Deborah Salon","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Deborah","middleName":"","lastName":"Salon","suffix":""}],"badges":[],"createdAt":"2024-02-21 00:35:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3974111/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3974111/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11116-024-10506-4","type":"published","date":"2024-06-26T00:34:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51765850,"identity":"6fc5b694-4b2a-457a-8029-c649b341155d","added_by":"auto","created_at":"2024-02-28 18:39:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":203232,"visible":true,"origin":"","legend":"\u003cp\u003eModel \u003cem\u003ef\u003c/em\u003eramework\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3974111/v1/7d0f11992d2691a786c9cc85.png"},{"id":51765854,"identity":"72667ba7-cd1b-4911-b5a4-12ae18391305","added_by":"auto","created_at":"2024-02-28 18:39:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":370010,"visible":true,"origin":"","legend":"\u003cp\u003eSurvey flowchart\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3974111/v1/e6722bb9d27f5ae82b328ebb.png"},{"id":51765852,"identity":"100982db-e516-4f58-8b2e-dbf0f2facfd1","added_by":"auto","created_at":"2024-02-28 18:39:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":591168,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of respondents in the study sample across the U.S.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3974111/v1/6ac791c15b648ecb0338254c.png"},{"id":51766040,"identity":"08389447-7175-43c0-b652-b861525f3d9e","added_by":"auto","created_at":"2024-02-28 18:47:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":64725,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of marginal and joint distributions of WFH and online shopping frequency\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3974111/v1/d2d21b7df6670a9eabe0afe5.png"},{"id":51765855,"identity":"4669c0d5-85b2-415f-829a-642be7121349","added_by":"auto","created_at":"2024-02-28 18:39:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135477,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of psychological latent constructs indicators\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3974111/v1/9142ef966d91879a99a92291.png"},{"id":51766041,"identity":"e14a36e7-2aff-44cb-b5b2-5daaf118d741","added_by":"auto","created_at":"2024-02-28 18:47:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":22786,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of home-technology availability latent construct indicators\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3974111/v1/80915d928fa62b9dd4b48fb1.png"},{"id":59146612,"identity":"1e8e6246-cd19-48e4-be26-4b0e165ff247","added_by":"auto","created_at":"2024-06-27 00:34:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2421421,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3974111/v1/ae49540c-ff66-4f68-9561-0619a3c8821f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Interaction Between the Recent Evolution of Working from Home and Online Shopping","fulltext":[{"header":"1.\tINTRODUCTION","content":"\u003cp\u003eWork-from-home (WFH) and online shopping are rapidly growing. Since 2009, the number of employees working from home and the share of e-commerce in retail sales in the United States have increased by 159% and 72%, respectively\u0026nbsp;(B2B Insights, 2022; Census, 2022, 2009). The rapid advancement of information and communication technologies (ICT) over the last few decades is one reason for these changes\u0026nbsp;(OECD, 2021, 2019). Another reason is the impact of the COVID-19 pandemic on our lives. The COVID-19 pandemic forced people to perform essential daily activities such as working and shopping at home\u0026nbsp;(Bhatti et al., 2020; Javadinasr et al., 2022; Mohammadi et al., 2022; Salon et al., 2021). These changes in individuals’ daily activities have an impact on many travel behaviors, such as the number of trips per day, destination, mode, and route choice decisions, and thus have an impact on the entire transportation system.\u003c/p\u003e\n\u003cp\u003eDespite the overall increasing trend of WFH adoption over the past decade, the dynamics of WFH experienced notable fluctuations because of the COVID-19 pandemic’s ever-changing nature. Following global lockdowns and the implementation of social distancing measures, WFH witnessed a remarkable upsurge\u0026nbsp;(Jain et al., 2022); however, emerging evidence in the post-pandemic era indicates a decline as individuals are gradually returning to traditional workplaces\u0026nbsp;(Huang et al., 2023; Rahman Fatmi et al., 2022). For instance, a U.K. survey reveals that teleworking constituted an average of 15% of work time before the pandemic, rose to 89% at wave 1, and subsequently decreased to 72.5% at wave 3 of the pandemic\u0026nbsp;(Baumann et al., 2023). Online shopping, however, experienced a sustained increase suggested by researchers during the lockdown period as well as the post-pandemic era\u0026nbsp;(Brůhová Foltýnová and Brůha, 2024; Diaz-Gutierrez et al., 2023).\u003c/p\u003e\n\u003cp\u003eObserving huge shifts to WFH and online shopping, these ICT-based activities as part of travel demand management, provide transportation planners and policymakers with an opportunity to address transportation system issues such as urban congestion and GHG emissions\u0026nbsp;(Hensher et al., 2022; Shabanpour et al., 2018). Hence, it is critical to understand what influences individuals' decisions on WFH and online shopping, as well as the interrelationship between these two behaviors. WFH and online shopping might have a mixed relationship. These two behaviors, for example, can have a positive impact on each other, because people who WFH have more access to ICT devices, are already familiar with how to use such technology, and are interested in using them. According to the latest PYMNTS and Strip report, observing the digital behaviors of over 30,000 consumers in 11 countries, 44% of consumers stated that they were working from home. Furthermore, within this group of individuals who are highly connected through online work, 73% participated in online retail shopping\u0026nbsp;(PYMNTS, 2023).\u003c/p\u003e\n\u003cp\u003eAdditionally, WFH may contribute to online shopping incidence in another way. Most people stop for shopping while commuting\u0026nbsp;(Golob and Regan, 2001; PYMNTS, 2019), and those who WFH might either avoid shopping or do online shopping rather than in-person shopping. There are some contradictory findings in the literature, for example, WFH may reduce online shopping because it is stated that workers at home spend more time on non-commuting trips\u0026nbsp;(Hensher et al., 2022), potentially increasing in-person shopping. The complex connectivity of these two behaviors needs to be studied as it determines changes in activity patterns and their implications on transportation systems.\u003c/p\u003e\n\u003cp\u003eThe discussion above motivated us to comprehensively analyze the concurrent trends of WFH and online shopping frequency, shedding light on their recent evolution as well as exploring how these two behaviors contribute to changes in each other. Online shopping can be categorized into two classes: grocery and non-grocery. Since online grocery shopping emerged recently following the COVID-19 pandemic and its benefits are still unknown or underestimated\u0026nbsp;(New York Times, 2022; Tyrväinen and Karjaluoto, 2022), in this study, we capture the non-grocery online shopping behavior, and we use the terms “online shopping” and “online non-grocery shopping” interchangeably. We focus on non-grocery-only online shopping that entails purchasing items beyond everyday essentials and food products, including categories such as apparel and footwear, consumer electronics and appliances, health and beauty products, home, and garden items, as well as leisure and personal goods\u0026nbsp;(Bourlier, 2020).\u003c/p\u003e\n\u003cp\u003eWe use the data drawn from a 2021 nationwide “COVID FUTURE” survey administered by the research group to track US households’ activity-travel behavior evolution since the COVID-19 pandemic.\u0026nbsp;The collected information covers various aspects including individual and household characteristics, job-related factors (e.g., employer WFH policies), travel habits, different ways people go about their daily activities, and their lifestyle attitudes. This comprehensive dataset allows us a detailed look at many factors that play a role in WFH and online shopping decisions.\u0026nbsp;A Generalized Structural Equation Model (GSEM) is developed, which provides a robust framework for concurrently examining complex relationships among variables, yielding comprehensive insights in research studies.\u003c/p\u003e\n\u003cp\u003eTo capture the complexity of individuals’ decision-making, we incorporated both observed and unobserved (i.e., latent) variables that significantly influence these two ICT-based behaviors. Estimated latent variables, representing a combination of positive and negative attitudes toward WFH and online shopping, along with home-tech factors account for unobserved patterns, recognize the influence of different technology attributes, and comprehensively explore the underlying factors. For example, individuals who are more productive when WFH due to no commuting time, more comfortable workspace at home, etc., might work remotely more often, which is captured by the “WFH Comfort” Latent variable. Similarly, one might be encouraged to shop online as they perceive this behavior as convenient and beneficial due to a combination of reasons such as timesaving and, a variety of available options online, all of which form “Online Shopping Enjoyment” latent. On the other hand, different technological attributes such as high-speed internet, and computers may facilitate the choice of WFH and online shopping, and that is integrated by the “Home Tech Availability” latent variable into the model.\u003c/p\u003e"},{"header":"2. BACKGROUND","content":"\u003cp\u003eWorking from home and online shopping have been popular as early as the 1980s, attracting many researchers and urban planners\u0026rsquo; interest due to their potential impacts on individuals\u0026rsquo; activity-travel behavior. There is a vast body of literature on WFH and online shopping (Asgari et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Balbontin et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Forsythe and Shi, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Friesz et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Hensher et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Maf\u0026eacute; and Blas, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Mohammadi et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Paleti, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pouri and Bhat, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Sener and Bhat, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sener and Reeder, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Shabanpour et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Siegel, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Walls et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). All these studies use statistical analysis to explore factors affecting individuals\u0026rsquo; decisions for WFH and online shopping. In the following, we summarize the studies conducted on WFH and online shopping, then we present the existing research gaps.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1. WFH Literature\u003c/h2\u003e \u003cp\u003eWFH literature can be categorized into two groups: studies before the COVID-19 pandemic (Asgari et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Peters et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sener and Bhat, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Shabanpour et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Varma et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Walls et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and after the pandemic (Balbontin et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mohammadi et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nguyen, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nguyen and Armoogum, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These two groups are differentiated due to a better understanding of the literature after the pandemic about WFH behavior.\u003c/p\u003e \u003cp\u003eStudies conducted before the pandemic mostly focused on non-attitudinal factors influencing WFH behavior. For example, many emphasize the importance of demographic characteristics in employee preferences for WFH. Particularly, individuals with higher levels of education, those belonging to higher income groups, and individuals with children were found to be more inclined to adopt WFH (He and Hu, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Loo and Wang, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Few studies investigated the effects of perceptions and attitudes on WFH frequency. For example, Mokhtarian et al. (Mokhtarian and Salomon, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) found that job suitability, perceived benefits, and productivity influence WFH decisions.\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic pushed many non-essential workers to work at home providing the opportunity to explore the revealed preferences of WFH (Beck and Hensher, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Javadinasr et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mohammadi et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Salon et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As many who were new to WFH started practicing it, the role of attitudes and perceptions have been highlighted after the pandemic literature. For example, Mohammadi et al. (Mohammadi et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated the long-term impacts of the COVID-19 pandemic on WFH frequency in the US using data from waves 1 and 2 of the COVID Future survey. They developed a generalized structural equation model (GSEM) in which impacts of WFH productivity and changes in perceived risk of exposure to COVID-19 along with other observed factors on preferences toward WFH are captured. They found that both perceived WFH productivity and COVID-19 risk positively and significantly influence the frequency of this behavior.\u003c/p\u003e \u003cp\u003eBalbontin et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) modeled the decision to WFH or commute during pandemic restrictions in Australia by looking into the impacts of two latent variables: WFH-loving attitude and risk perception in using public transit. They found that WFH loving attitude which is mostly defined by the company\u0026rsquo;s flexibility toward the workplace significantly increases WFH probability. Risk perception in using public transit was also found to be an important factor influencing WFH or commuting with private vehicles. Nguyen (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) explored adoption, perceptions, and attitudes toward WFH during and after the pandemic in Vietnam. In line with the previous study, He found the importance of the company\u0026rsquo;s policy for the workplace to WFH choice. Moreover, he indicated the critical role of difficulties with being focused and accessing the data at home in perceiving WFH as a good solution during the pandemic.\u003c/p\u003e \u003cp\u003eA line of literature studying the post-pandemic WFH employs the stated preference approach to explore the impact of the COVID-19 pandemic on individuals\u0026rsquo; future WFH behaviors. For instance, Folt\u0026yacute;nov\u0026aacute; and Brůha (Brůhov\u0026aacute; Folt\u0026yacute;nov\u0026aacute; and Brůha, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) investigated the expectations of Czech Republic residents regarding WFH after the pandemic, using a multi-wave survey conducted in 2020 and 2021. Their findings highlighted job type, age, and education as key predictors of WFH adoption in the post-pandemic era. Similarly, Rahman Fatmi et al. (Rahman Fatmi et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) studied the lasting impact of COVID-19 on individuals\u0026rsquo; preferences for WFH in post-pandemic times. Analyzing data from a survey in British Columbia, the study developed a random parameter logit and found factors such as age, gender, commute time, dwelling size, and location characteristics to be significant in WFH preferences.\u003c/p\u003e \u003cp\u003eSimilarly, Jian et al. (Jain et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated the enduring impacts of COVID-19 on employees\u0026rsquo; post-COVID WFH intentions, using information from 1,364 workers in Greater Melbourne. The authors employed SEM to examine the role of psycho-social latent variables in employees\u0026rsquo; intention to increase WFH post-COVID. Their findings revealed that perceived behavioral control (e.g., job type, technology, access to materials) and Subjective Norms (e.g., employer and family support) as key determinants for future WFH intentions. Asgari et al. (Asgari et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) characterized the impact of the pandemic-induced remote work on future teleworking decisions, leveraging a survey conducted in Florida in May 2020. Their analysis revealed that the post-pandemic WFH adoption is influenced by several attitudinal factors including pro-WFH, pro-technology, interaction enjoyment, and productivity attitude. Moreover, Kong et al. (Kong et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) employed a survey collected from Washington State in 2020. Using an SEM, they demonstrated that positive or negative perceptions towards WFH before and during the pandemic significantly affect individuals\u0026rsquo; future decisions to continue WFH after the pandemic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Online Shopping Literature\u003c/h2\u003e \u003cp\u003eSimilar to WFH, online shopping literature can be divided into before (Curr\u0026aacute;s-P\u0026eacute;rez et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Forsythe and Shi, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Maf\u0026eacute; and Blas, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and after the pandemic studies (Andruetto et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Asgari et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dias et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Diaz-Gutierrez et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Meister et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Melović et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Qalati et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shabanpour et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The establishment of protective measures induced people not only to WFH but also to shop from home via online platforms (Bhatti et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rossi et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hence, studies conducted after the pandemic could better capture preferences for shopping online. Since the literature on preferences for online shopping is limited, we also review studies on online grocery shopping.\u003c/p\u003e \u003cp\u003eShabanpour et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) investigated how online grocery shopping behavior has changed because of the pandemic using data from a survey conducted in the Chicago region. They found that individuals who had not previously engaged in online grocery shopping were inclined to alter their habits and switch to online shopping during the pandemic. Furthermore, they indicated the significant role of income and COVID-19 restrictions in online shopping frequency during the pandemic. Another study conducted by Asgari et al. (Asgari et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in South Florida employs SEM to explore both observed and latent attitude variables. Their findings underscore the significance of experience with online grocery platforms as a crucial factor influencing continued use. Additionally, the study reveals that positive attitudes toward technology and perceptions regarding various aspects of online grocery shopping, such as convenience, efficiency, usefulness, and easiness, play important roles in driving the likelihood of future engagement in online grocery shopping.\u003c/p\u003e \u003cp\u003eAndruetto et al. (Andruetto et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) examined the variations in online shopping patterns for both grocery and non-grocery items during the first wave of the pandemic in Italy and Sweden. Their findings revealed a significant transition from traditional in-person shopping to online shopping in both countries. However, the extent of this shift is more pronounced in Italy compared to Sweden, attributable to the more stringent restriction policies enforced in Italy. Melovic et al. (Melović et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) investigated factors influencing Millennials\u0026rsquo; online shopping behavior in Montenegro using SEM. They illustrated that perceived risks and barriers of online shopping such as delivery and quality of the product purchased online significantly impact millennials\u0026rsquo; preferences. Moreover, they found that while the frequency of online shopping does not differ based on gender, the behavior does. Using a similar modeling approach, Qalati et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) studied the relationship between perceived service quality, perceived website quality, and perceived reputation and online shopping adoption, illustrating the significant impacts of these factors on online shopping choice.\u003c/p\u003e \u003cp\u003eDias et al. (Dias et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) studied the interrelationships between online and in-person activity engagement, specifically focusing on shopping and eating meals. The authors found that in-person and online activities are both complement and substitute for each other. Moreover, their findings revealed that income, built-environment variables such as the presence of shopping centers in the neighborhood, and household structure-related factors including the presence of children or the number of household members, affect shopping behavior.\u003c/p\u003e \u003cp\u003eDiaz-Gutierrez et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) studied factors affecting post-pandemic online shopping behavior using data collected from Washington State residents. They estimated SEM showing that the perceived health risk, perception toward online shopping, and online shopping frequency before the pandemic are the most significant factors influencing post-pandemic online shopping frequency. Moreover, Meister et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) employed a stated choice experiment to design a survey implemented in Switzerland in the first wave of the pandemic. According to their study, shopping time and cost, wait time, and perception of infection risk are the most significant factors affecting individuals\u0026rsquo; decision to conduct online grocery shopping after the pandemic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Research Gap and Contributions\u003c/h2\u003e \u003cp\u003eThe studies mentioned above provide interesting results on either WFH or online shopping behavior. However, all these studies are focused on a statistical analysis of either WFH or online shopping behavior. The study most closely aligned with our current effort is the quantitative research by Sener and Reeder (Sener and Reeder, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), who explored the behavioral linkages across ICT choice dimensions, specifically WFH and online shopping. Their use of copula modeling represents a sophisticated statistical technique that allows for testing different forms of dependence between these two behaviors. The study utilized data from the 2009 National Household Travel Survey (NHTS), which includes individual/household demographics, commute characteristics, attitudinal factors, and residential neighborhood variables. They found a positive and asymmetric fit of the best model emphasizing the presence of unobserved factors influencing the underlying processes of WFH and online shopping behaviors.\u003c/p\u003e \u003cp\u003eHowever, like any other study, there are certain aspects to consider. First, the temporal context of the study, originating in 2012, raises questions about the current relevance of their findings, given the dynamic nature of technology and evolving societal trends (OECD, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Second, Sener and Reeder\u0026rsquo;s pioneering work in examining behavioral linkages between telecommuting and teleshopping sets the stage for understanding these ICT choices. However, the current effort advances this understanding by incorporating a more flexible modeling approach. The GSEM used in this study proves advantageous as it can estimate complex nonlinear relationships simultaneously. This offers unparalleled flexibility in capturing the dependence between decisions and estimating latent variables simultaneously. The incorporation of latent variables, reflecting attitudes and technology availability, enhances the analysis. It offers a more nuanced understanding of the factors influencing work-from-home and online shopping behaviors, capturing the previously unseen aspects of these dynamics.\u003c/p\u003e \u003cp\u003eIn summary, this study brings a triple-layered contribution to the existing literature. Firstly, it looks into the unexplored area of understanding the intricate interactions between two crucial ICT behaviors: WFH and online shopping. Secondly, taking advantage of a dataset recently gathered in the United States during October and November 2021, our research provides a current snapshot of the evolving landscape, encapsulating the complexity of WFH and online shopping patterns. Finally, we navigate through the often-overlooked aspects, characterizing the impact of travel habits, job-related characteristics, at-home technologies, and attitudinal/perceptual factors on both WFH and online shopping behavior. These nuanced investigations contribute to a richer understanding of the dynamic factors shaping work-from-home and online retail activities.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. METHOD","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Survey\u003c/h2\u003e\n \u003cp\u003eThe data source for this study is a three-wave nationwide survey administered by our research group in the United States throughout 2020 and 2021 using the Qualtrics online platform. In the first wave conducted from April 2020 to June 2021, a total of 9,265 responses were gathered. 2,877 and 2,728 participants rejoined for the second and third wave, respectively. Individuals were invited to participate through a quota-sample survey panel, direct random email invitations, and media coverage of the study project. This study focuses on wave 3 of the survey, which was administered in October and November 2021.\u003c/p\u003e\n \u003cp\u003eCOVID Future survey puts together: (1) socio-demographics such as age, gender, race, education, employment, household income, and household structure, (2) job characteristics such as job category and employer flexibility for WFH, (3) health issues such as disability status, covid infection, (4) home environment characteristics such as availability of a computer, high-speed internet connection, and the sufficiency of technological devices, (5) WFH choice and frequency and attitudes toward WFH, (6) employee\u0026rsquo;s productivity when WFH during the pandemic, how it has changed compared to before the pandemic and the perceived reasons of the change, (7) commute and non-commute trip features, (8) Online shopping frequency including online ordering grocery, non-grocery, and food as well as attitudes toward online shopping, (9) Online school, (10) telemedicine (11) long-distance travel behavior, (12) relocation, and (13) lifestyle attitudes.\u003c/p\u003e\n \u003cp\u003eIn this study, we elaborate on the sections relevant to our scope, specifically focusing on work-from-home, employee productivity, technology accessibility, job characteristics, Travel habits, and online shopping frequency attitudes. Interested readers can download this survey data and perform additional analysis. Other works based on this survey data can be found in (Bhagat-Conway et al., 2021; Chauhan et al., \u003cspan class=\"CitationRef\"\u003e2021b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Conway et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Javadinasr et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mirtich et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mohammadi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Salon et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn addressing the primary objectives of this study, we look into work-from-home (WFH) and online shopping activities. In the WFH section, our approach involves a systematic inquiry into employer flexibility, with questions presented in the following order: 1- Do you have the option to WFH?, 2- Are you required to WFH all the time?, 3- Are you required to WFH some of the time?, 4- Can you WFH whenever you want?, and 5- Can you WFH only some of the times?. Following this, those who had the option to WFH were asked how many days in the last 7 days they worked from home In optimizing model performance, a statistical significance test prompted the categorization of WFH frequency into the ordinal scale as follows: \u0026ldquo;0 WFH days per week\u0026rdquo; as 0, \u0026ldquo;1\u0026ndash;2 WFH days per week\u0026rdquo; as 1, \u0026ldquo;3\u0026ndash;4 WFH days per week\u0026rdquo; as 2, and \u0026ldquo;5 or more WFH days per week\u0026rdquo; as 3.\u003c/p\u003e\n \u003cp\u003eMoving on to non-grocery online shopping behavior, respondents were provided with specific categories to express the frequency of ordering non-grocery items for delivery in the last 7 days. The response options included: \u0026ldquo;0\u0026rdquo;, \u0026ldquo;1 time,\u0026rdquo; \u0026ldquo;2\u0026ndash;3 times,\u0026rdquo; and \u0026ldquo;4 times or more.\u0026rdquo; The same categorical framework was employed in the modeling process when estimating online shopping frequency. In the following, we discuss further questions related to the observed and latent variables incorporated in the model. Moreover, we provide an overall scheme of the developed framework.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Analytical Framework and Sample Description\u003c/h2\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1. Analytical Framework\u003c/h2\u003e\n \u003cp\u003eThe analytic framework focuses on understanding the inter-relationship between WFH and online shopping frequency decisions while incorporating individuals\u0026rsquo; demographics, household characteristics, job-related factors, travel habits, home technology-related factors, as well as lifestyle attitudes. The impacts of home technology-related factors, and lifestyle attitudes are not directly observed, and so are viewed as latent constructs. In this study, five latent constructs are used: (1) different attributes of the technology that is available at home referred to as Home Tech Availability, (2) individuals\u0026rsquo; perceived reasons for their increased productivity at home referred to as WFH Comfort, (3) individuals\u0026rsquo; perceived reasons for their decreased productivity at home referred as WFH Unproductiveness, (4) individuals\u0026rsquo; perceived reasons for shopping online frequently referred as Online Shopping Enjoyment, and (5) individuals\u0026rsquo; perceived reasons for not shopping online frequently referred as Inconvenient Online Shopping.\u003c/p\u003e\n \u003cp\u003eThese latent constructs are likely to impact WFH and online shopping decisions through comfort levels, interest, and trust in the use of ICT devices as well as general personality temperament. While being intuitive, these constructs were also based on previous studies on technology adoption and usage as well as the psychology related to WFH and online shopping (Asgari et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Berry et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Diaz-Gutierrez et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Duarte et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jain et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jiang et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kong et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Meister et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Melović et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mohammadi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, the decision to settle on these specific latent variables was based on analyzing the correlation between attitudinal factors and home-tech attributes with the main outcomes. Moreover, we carefully examined the goodness-of-fit measures and the significance level of the factors during the modeling process to ensure the model\u0026rsquo;s statistical adequacy. Therefore, these latent constructs and their set of indicators presented in this paper are the most appropriate ones. We discuss the latent constructs and the choice of indicators in detail later in the following. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e represents the overall framework of this study.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2. Sample Description\u003c/h2\u003e\n \u003cp\u003eThe analysis uses the portion of the survey that was employed in wave 3 of the survey and has the choice to decide about their WFH frequency. Hence, those employees who did not have the option to WFH, and those who were required to WFH all the time were excluded. The sample of this study includes 488 respondents. To ease understanding, Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e represents survey flowchart, and how the final sample is maintained.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the spatial distribution of respondents across the United States. As seen, respondents are fairly distributed over California, Arizona, Texas, Illinois, New York, Ohio, Michigan, and many other densely populated states. Only a few states have a low number of observations. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the summary statistics of respondents\u0026rsquo; key socio-demographic characteristics in the study sample. As mentioned earlier, this study focuses on those who are employed and have the option to WFH, hence, our sample exhibits a potential bias towards white, high-income, and educated households. This deliberate choice enables an in-depth exploration of WFH and online shopping dynamics within this specific context. While findings may not generalize broadly, they provide valuable insights into the behaviors and attitudes of individuals with WFH opportunities, contributing to a comprehensive understanding of this targeted demographic.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary statistics of respondents\u0026rsquo; key characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePart-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnder \u003cspan\u003e$\u003c/span\u003e50K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e50K - \u003cspan\u003e$\u003c/span\u003e100K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e100K - \u003cspan\u003e$\u003c/span\u003e150K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than \u003cspan\u003e$\u003c/span\u003e150K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eRace\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite/Caucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack/African\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNative American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompleted graduate degree(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBachelor\u0026rsquo;s degree(s) or some graduate school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome college or technical school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompleted high school or GED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSome grade/high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eHousehold Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026lrm;(a) and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026lrm;(b) show the distribution of the dependent variables, online shopping, and WFH frequency, in the study sample. According to Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026lrm;(a), the majority of respondents shop online 0 times, once, twice, or three times per week. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026lrm;(b), respondents are almost evenly dispersed across the various WFH frequency categories, with a higher density existing in 0 and 5 or more WFH days per week. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026lrm;(c) illustrates the joint distribution of WFH frequency and online shopping behavior, providing an insight into how WFH and online shopping behaviors\u0026rsquo; impacts on each other can be mixed. As shown, most of the observations are distributed over the diagonal and the lower triangle of the joint distribution matrix. More distribution over the diagonal or close to it, indicates positive correlation between WFH and online shopping. In particular, we observe this joint distribution with high correlation in the tails (zero WFH/zero online shopping and 5\u0026thinsp;+\u0026thinsp;WFH/2\u0026ndash;3 online shopping areas), implying that they are positively associated with one another. However, in certain dense areas, such as 5\u0026thinsp;+\u0026thinsp;WFH/0 online shopping, 3 to 4 days WFH/0 online shopping, and 0 WFH/1-time online shopping, the positive correlation is less evident. This illustrates the nuanced and potentially complex nature of the relationship between WFH frequency and online shopping behavior. Furthermore, this aligns with our estimated results, where we found a causal relationship more significant than a correlation.\u003c/p\u003e\n \u003cp\u003eMoving on to latent constructs, in creating our first latent constructs, the correlation matrix analysis navigated us to explore how people view working from home, especially in terms of their productivity at home. To understand changes in productivity, we asked respondents to rate their work output at home during the pandemic compared to before, using a 6-Likert scale from \u0026ldquo;significantly lower\u0026rdquo; to \u0026ldquo;significantly higher\u0026rdquo;. For those who noticed changes, we asked them about the factors contributing to decreased productivity and those fostering increased productivity. Respondents selected the factors increasing/decreasing their WFH productivity among the set of reasons that they were given. These reasons are reported through a binary scale. We acknowledge that capturing these reasons by a binary scale limits the variability, and information available compared to a 5-Likert scale.\u003c/p\u003e\n \u003cp\u003eThe latent WFH comfort is a measure of how convenient employees perceive working from home. This latent is derived from factors that contribute to an increase in productivity. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the indicators for WFH comfort and other psychological latent constructs. More than 60% of individuals who reported higher productivity attributed it to the absence of commuting time, while around 25% credited the ability to get more sleep. Another frequently reported factor influencing productivity improvement is the flexibility of working hours, selected by 50% of respondents.\u003c/p\u003e\n \u003cp\u003eConversely, WFH unproductiveness aims to capture how employees perceive their lack of productivity when working from home. Approximately 40% of respondents identified more multitasking as a significant reason for decreased productivity (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Other notable factors contributing to WFH unproductiveness are challenging communication with coworkers and an uncomfortable workspace, both selected by a considerable percentage of respondents.\u003c/p\u003e\n \u003cp\u003eIn the context of online shopping, we sought insights into respondents\u0026rsquo; expectations regarding the frequency of ordering items for delivery post-COVID-19. Those expecting increased online shopping were asked about the driving factors, while those expecting a decrease were questioned about the reasons behind this shift. Respondents were provided with various reasons, enabling a thorough exploration of the diverse motivations and inhibitors influencing their expectations and behaviors in the evolving online shopping trend after the COVID-19 pandemic.\u003c/p\u003e\n \u003cp\u003eThe online shopping enjoyment latent construct represents the overall positive and pleasurable experiences individuals derive from engaging in online shopping activities. It may include the satisfaction, joy, and fulfillment associated with the online shopping process. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, Notably, time-saving, with about 90% of respondents acknowledging it as a motivating factor, and the convenience of 24/7 shopping, noted by around 80% of participants, contribute significantly to the enjoyment of online shopping. However, the option to read reviews before making a purchase was the least selected motivation, with only 45% finding it influential among those expecting more online shopping in the future.\u003c/p\u003e\n \u003cp\u003eThe inconvenient online shopping latent construct reflects the challenges and hindrances individuals face that make the online shopping experience less convenient and, consequently, less enjoyable. For those anticipating less online shopping, more than 90% expressed that the desire for immediate access to purchases discourages them from shopping online frequently (see Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Additionally, 65% mentioned shipping fees as a significant inhibitor, while difficulty with online shopping platforms was the least chosen reason, selected by approximately 10% of respondents.\u003c/p\u003e\n \u003cp\u003eIn addition to psychological latent variables, we introduced a latent variable named home-technology availability, aiming to measure the overall accessibility and sufficiency of technological resources within individuals\u0026rsquo; home environments. To construct this latent variable, we used participants\u0026rsquo; responses to the following questions: 1) Do you currently have high-speed internet service at your home? 2) Which of the following devices do you have access to at your home? Please select all that apply: Computer (desktop or laptop), Tablet, Smartphone; and 3) Do you have enough internet-connected devices in your home for all the people who want to use them? Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the distribution of significant indicators used in estimating the \u0026ldquo;Home-Technology Availability\u0026rdquo; latent variable. About 98% of individuals have access to high-speed internet and computers (i.e., laptops or desktops) at home. Moreover, 97% of individuals have adequate internet-connected devices at home for all intended users. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provides definitions and summary statistics for key exogenous explanatory variables used in estimating the main outcomes of the study.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDefinition and summary statistics of explanatory variables used in the final model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Dev.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndividual Characteristics:\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge: 60 or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: if the individual is 60 years old and above; 0 o/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace: Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: if the individual is Hispanic; 0 o/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace: Black/African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: if the individual is Black/African American; 0 o/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHaving physical difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: if the individual has physical difficulty; 0 o/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Characteristics\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: if the household income is higher than \u003cspan\u003e$\u003c/span\u003e 100,000; 0 o/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChildren: 13\u0026ndash;17 years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of 13\u0026ndash;17-year-old children in the household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTravel Habits\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommute mode: Private vehicle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: if the individual commutes with a private vehicle; 0 o/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActive mode usage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequency of walk, bike, and shared bike usage in the last 7 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob-related Factors\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJob Category: Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: if the individual works in the Education industry; 0 o/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployer flexibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: if the individual always has the option to WFH; 0 o/w\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Model Specification\u003c/h2\u003e\n \u003cp\u003eThis study explores the interplay between WFH and online shopping behaviors using the Generalized Structural Equation Model (GSEM), a statistical tool for analyzing complex relationships among variables. GSEM extends Structural Equation Modeling (SEM) to handle a wider range of data types and distributions, proving valuable for diverse outcomes like categorical or ordinal variables. The GSEM serves as a comprehensive framework for modeling relationships between latent constructs, observed factors, and observed outcomes within a unified structure. We introduce ordered logit links to GSEM due to the ordinal nature of our primary outcomes: WFH frequency, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{1}\\)\u003c/span\u003e\u003c/span\u003e, and online shopping frequency,\u0026nbsp;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{2}\\)\u003c/span\u003e\u003c/span\u003e. The following formulation represents the cumulative probabilities associated with different response categories.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P\\left({Y}_{1}\\le j\\right)= P \\left({Y}_{1}\\le j∣{L}_{1}, {L}_{2},{L}_{3}, X\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\forall j\\in \\{1, 2, \\dots ,{J}_{1}\\}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P\\left({Y}_{2}\\le k\\right)= P\\left({Y}_{2}\\le k∣{L}_{4}, {L}_{5}, X, {Y}_{1}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\forall k\\in \\{1, 2, \\dots ,{J}_{2}\\}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{2}\\)\u003c/span\u003e\u003c/span\u003e denote WFH and online shopping frequency variables, respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(j\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(k\\)\u003c/span\u003e\u003c/span\u003e are the number of discrete values that WFH and online shopping frequency variables can take, respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{L}\\)\u003c/span\u003e\u003c/span\u003e represents latent constructs, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{X}\\)\u003c/span\u003e\u003c/span\u003e is the vector observed factors. In equation \u0026lrm;(1), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P({Y}_{1}\\le j)\\)\u003c/span\u003e\u003c/span\u003e represents the probability that the WFH frequency, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{1}\\)\u003c/span\u003e\u003c/span\u003e, falls within or below a specific category j. This probability is conditioned on WFH, and technology-related latent constructs, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({L}_{1}, { L}_{2}, { L}_{3}\\)\u003c/span\u003e\u003c/span\u003e and observed factors, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{X}\\)\u003c/span\u003e\u003c/span\u003e. Similarly, in equation \u0026lrm;(2), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(P({Y}_{2}\\le k)\\)\u003c/span\u003e\u003c/span\u003eshows the probability that the online shopping frequency, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{2}\\)\u003c/span\u003e\u003c/span\u003e, is within or below category k, conditioned on online shopping related latent constructs, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({L}_{4}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({L}_{5}\\)\u003c/span\u003e\u003c/span\u003e, observed factors, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{X}\\)\u003c/span\u003e\u003c/span\u003e, and the estimated WFH frequency, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{1}\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eWithin the GSEM framework, the measurement model establishes connections between latent constructs and the observed variables. In this study, latent constructs, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varvec{L}\\)\u003c/span\u003e\u003c/span\u003e, serve as critical components, representing underlying factors that shape WFH and online shopping behaviors. While these constructs are not directly observable, they are inferred from a set of observed variables (i.e., indicators), creating a link between theoretical concepts and empirical data. This is expressed as:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003eWhere X is a vector of the observed variables, \u0026Lambda; is the matrix of factor loadings, indicating how each observed variable is associated with the latent constructs, and \u0026Phi; is the vector of latent constructs. These are underlying factors that are not directly observable but inferred from the observed variables. \u0026Delta; is the vector of error terms or measurement errors associated with observed variables. It captures variability not explained by the latent constructs. The structural model explores the relationships among latent constructs, however, since we do not consider any relationship between latent constructs, the structural model simplifies to:\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003eWhere \\(\\zeta\\) is the vector of error terms or disturbances associated with the latent constructs. These account for any unobserved factors or variability in the latent constructs. The estimation process employs Maximum Likelihood Estimation (MLE), a statistical method seeking parameter values that maximize the likelihood of observing the given sample data. The likelihood function, \\(\\varvec{\\Gamma }\\), quantifies how well the model explains the observed data and is calculated as:\n \u003c/div\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAikAAAA1CAYAAACEGHvXAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABfaVRYdFNuaXBNZXRhZGF0YQAAAAAAeyJjbGlwUG9pbnRzIjpbeyJ4IjowLCJ5IjowfSx7IngiOjU1MywieSI6MH0seyJ4Ijo1NTMsInkiOjUzfSx7IngiOjAsInkiOjUzfV19RD36wQAAB4xJREFUeF7t3c9LFH8cx/GP33uBu54iJNJLRBRkBVFBQZuUBGFRVIcOQdQhsENSBF2KNPFWqdChQz+sU6foxyFoO2QUKB06ZCISnvxR/QN+fX38zLaOo+64js7OPB+wzMxnt/2RO+PL9+czn6mammYAAABi5j+3BAAAiBVCCgAAiCVCCgAAiCVCyioYHx83jY2NJpPJmIGBAVNfX2/Xh4aG3CMAAAAhZRV8//7dPHjwwExOTpoXL16Yvr4+U1dX5+4FAADC2T2rRBWU5uZmMzg4aCsru3btsusAAGAGlZRV8unTJ5PL5ey6Kila7+3ttYEFAAAQUlbN+/fvTVNTk9sypquryy6z2axdAgCQdnT3AACAWKKSAgAAYomQAgAAYomQAgAAYomQEoGPHz+aqqqqSG56bgAA0oCQEoE9e/aY/v5+09DQ4FqM0fjkpdwOHTpk/311dbXJ5/P2uQEASANCSkS2bt1qHj586LbKpxlqCSgAgDQhpERIQWW5rF+/3q0BAJAOhBQAABBLhBQAABBLhBQAABBLhBQAABBLhBQAABBLhJSEGR8fN5cuXSpM/vbq1St3zz+aEO7UqVNmYGDAtSyNnj+soaEh09jYaHp7e10LACCI/xirY6d3bNdNx1MdS3XMT6pEhpT6+vpZP8jiW9KdOXPGTExMFCaCW7t2rbtnhr7QLS0t5v79+8t6irRHIenu3buFn0Emk7E7kNpl48aN5smTJ+bDhw+J3rEAYKm8Wct//vzpWmbazp07N2uyTx1P9Qfnhg0b7DE3kaY/aOLk8/kpfTQtPc+ePbNtK02vWc7rTgeNOZ9lPt7nnv5iu5bZdH91dfXU2NiYaylP0OfS+21oaCi8B+811eanx3Z3d7stAIAEHccvXrzo1oK1t7cv+phKlJruHqXNuro6t5VM9+7ds59R6TrIrVu3zJ07d0w2m3Uty0tVmjdv3pjnz58X3oNmyX38+LH58uXLnC6ey5cvm2vXrhWqLACQdqowT4eNWcdxVVG6urpsdWW+CvTVq1fN27dvE3d9t1SNSRkcHHRrc/n7+oJuekxc6f0pHKg8qHWFsmLqu1SAOHjwoGv5R+FB3TI7duyw2woN+qxhy4cvX760IWl0dLSwI2npdTnp/mKHDx+2y3fv3tklAKSdwsjp06fd1ox169bZ7h3dvLCiY7pfLpczT58+dVvJwMBZ5/Xr14UvwXw3PSau9P4UELq7u+26v2rx+fNnu/RXWRRIvn37Zisdummgrca1tLe3Lxjqgvz+/bsQbLwdSUvR+Bjd77dz5077+gCQdl4VRKGkWPFxW8d3HU+DKir79u2z1ZQkIaQkhFK1qiibN292LbONjIwUrqhcTF0/t2/ftjuBrtp88+ZNG1DKHVR7/fp1uzOpbLmYr1+/ujUASK9fv37Zpf+PST9dcFaV8SD6PZAkhBSn0rt7vEpJOVdKVqlQoSUooKhCUsopy3ofeg8KPuJdvVnti+14AIDF6Via9DGWHkKKU+ndPRrvEVQpKbbQAFUFEJUJvbDjp66fxaorx44dM5OTk3MGbmlb7U1NTa4FAODnXe0+aLxJMd0/35jBpIWXRIeUv3//urXkU8A4cOCA25qrtrbWjjkJooDS2tpqB94qTGhciuY68XYUVZH8A3GDHD9+3O4gmofFCyp6bm0rQHkDZf22b9/u1gAgvbxKuE4+WIiq3qpS+2n+Kd2XJIkMKUePHrXLs2fPlj2raiXQZ1S42L17t2uZSwNUxZ/QlcYVUDTBmkqIJ0+eNEeOHLGhxuue0RiV/fv32/WFqKuor6/PniWkn4HCTXNzszlx4oR9/iDqV92yZYvbAoB00zg+/xk6/uEI+qM0qPtc7f4zgypd1ZT6MRAZfaFkqf/N+nLqF3k+n593vElPT4+db0QzzS5Ez6Vqi86nD0NVFD2/v7tHn62cr48qNgqSP378iGzuFgCoNDq2agBsmHF8OqPz0aNHsR6WsBSMSalwGmfS0dFR0lk0N27cMG1tbaEnT1M6X7NmzbJPEqQziaKcXA4AKpECirrOSz3mqntex/ekBRQhpFQwfYFrampsH+SVK1dc6/xUiVE/pioqYYOKzsnftGmT2yqfnk/dQhcuXHAtAABRBUVVal2rZzGqoAwPD4ee16pS0N0TsZXo7glL4UZT6Ad14URN4UiTxWnnK2UwLgAgvQgpEYtjSAEAoBLQ3QMAAGKJkAIAAGKJkBJzYQe4AgCQFISUCBVPJLfUSeW8WWK9C08BAJAWhJSIKJScP3/ebRm7HiaoaGbY4ktxd3Z2Lvs8JQAAxBln90RAYWLv3r1ua7ZSz9Lxzgry4ywfAEBaEFIAAEAs0d0DAABiiZCyAjRtcdjZVfVvdIViAADSipCyAhRQFDpE41U03sR/08yyHl0sSnSRKQAA0ooxKRFT4GhtbTX9/f2hr5Oj8MKPBwCQVoSUFZDJZMzExITbKh0hBQCQZnT3RExzo+RyObdVWncPAACgkhK5np4e8+fPH1NbWxtq8KzCzbZt28zY2JjJZrOuFQCA9KCSsgLa2trcWmlUVVFAkZqamsJAWgAA0sOY/wFASJyhxD2GfwAAAABJRU5ErkJggg==\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(f\\left({x}_{i}|{\\Theta }\\right)\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003erepresents the probability density function of the observed data \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i}\\)\u003c/span\u003e\u003c/span\u003e given the model parameters \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\Theta }\\)\u003c/span\u003e\u003c/span\u003e. The product is taken over all observed data points. The MLE process is performed iteratively to adjust parameter values, optimizing the likelihood. We used a specialized software, R with Lavaan package to perform the MLE process for estimating the parameters.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. RESULTS","content":"\u003cp\u003eTo cover the complexity of the underlying factors before looking into the broader implications reflected in the main outcomes, we will first discuss the GSEM outcomes related to the latent constructs. Then, we will proceed to the results for the main outcomes of our study.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Latent Constructs\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the results for the latent constructs. Cells marked with \u0026ldquo;_\u0026rdquo; indicate that the corresponding row variable is set to 1 during the estimation process. Fixing one indicator of a latent construct to 1 in GSEM enhances interpretability by providing a standardized scale for the latent variable. This fixed loading serves as a reference point, making it easier to understand the relative importance of each indicator in contributing to the overall construct. Additionally, it helps compare the contributions of different indicators within the same construct, facilitating clearer insights into the factors influencing the latent variable. All variables are significant at 99%, 95%, and 90% level.\u003c/p\u003e \u003cp\u003eThe latent variable WFH comfort captures the perceived reasons for increased productivity when working from home. It draws on a set of indicators, each offering insight into the factors contributing to a more comfortable and efficient remote work experience. The findings highlight the significant role of several factors: the perceived availability of extra time for sleep, indicating the potential positive impact on overall well-being (Forbes, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); the belief in a more comfortable home workspace, emphasizing the importance of physical surroundings for productivity (Apollo Technical, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); the reduction of distractions, underlining the value of a focused work environment (Gordon, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); the flexibility in working hours, allowing for personalized schedules (McKinsey \u0026amp; Company, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); and the absence of commuting time, emphasizing the time-saving benefits of remote work (Barrero et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As seen in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, in terms of the impact significance on the WFH comfort latent variable, the results highlight that no commuting time and flexible working hours hold the most significance, while getting more sleep has the least impact.\u003c/p\u003e \u003cp\u003eWFH unproductiveness latent variable captures perceived reasons for decreased productivity while working from home. Among the prominent contributors are responsibilities linked to dependents or childcare, highlighting the intricate balance individuals must maintain while working from home. Employees who take care of someone else at home might face numerous interruptions or even reduced working hours (Igielnik, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Modestino et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, discomfort in the home workspace emerges as a significant factor, acknowledging the impact of the physical environment on productivity (Toniolo-Barrios and Pitt, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The perception of reduced job demands for remote workers sheds light on the potential challenges individuals face in maintaining a sense of engagement and motivation in a remote setting (Nur et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, being more multi-tasking can lead to divided attention, increased stress, and reduced efficiency, contributing to the perception of decreased productivity while working from home (Park et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). While difficulties in communication with co-workers underscore the importance of effective virtual collaboration (J\u0026auml;msen et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Regarding the significance of the impacts of each of these indicators, the results show that care responsibilities and multi-tasking have the most impact on WFH unproductiveness, while job demanding less has the least impact.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndicators of latent constructs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatent constructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ_value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eWork-from-home comfort\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore comfortable workspace at home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFewer distractions at home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo commute time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.208***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlexible working hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.022***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGet more sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.662***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eWork-from-home unproductiveness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLack of comfortable workspace at home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore multitasking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.206***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJob demanding less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChild, dependent, or elderly care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.834***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDifficult communication with co-workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.588**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eOnline shopping enjoyment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaves time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan read reviews before purchase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHave a wider variety of choices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.957**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvoid going to stores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.663**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCan shop 24/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.699**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eOnline shopping inconvenience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReturn process frustrating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShipping fees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.205***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDifficulty with online shopping platform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.958***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInaccurate online information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.348***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWant immediate access to purchases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.346***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eHome-technology availability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSufficient internet-connected devices for all household members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-speed internet service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.287***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComputer (i.e., laptop or desktop)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.088***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ***, **, * ==\u0026gt; Significance at 99%, 95%, 90% level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe online shopping enjoyment latent variable is developed based on motivations that drive more frequent online shopping since the pandemic compared to what was before. Motivations that turned out to be significant are the ability to shop 24 hours a day, providing flexibility in shopping times (Smart Insights, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); the ability to save travel time, allowing for efficient shopping without commuting (Kumar and Kashyap, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); the motivation to avoid going to stores, emphasizing the convenience of online transactions (Emrich et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); the ability to read reviews before shopping, enabling informed decision-making; and the numerous options available online, providing a wide range of choices (Jiang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These motivations collectively contribute to the perception of online shopping as a pleasant and convenient experience. Among these motivations, the results show that saving time and having a wider variety of choices online hold the most significant impacts.\u003c/p\u003e \u003cp\u003eConversely, the inconvenient online shopping latent variable is constructed based on factors that hinder frequent online shopping. Significant inhibitors include frustrating return process, indicates dissatisfaction with the return procedure (Duarte et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); concerns about shipping fees, which may discourage online purchases (Lewis, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); the need for immediate access to the product, suggests a preference for immediate access (Dublino, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); the perception that information online is not accurate, highlights concerns about the reliability of information available online (Dublino, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and difficulty with the online shopping platform, reflects challenges in navigating online platforms (Smart Insights, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These inhibitors collectively contribute to the perception of online shopping as inconvenient. Furthermore, the findings indicate that the strongest inhibitors are the need for immediate access to the product and a frustrating return process, respectively. In contrast, inaccurate online information emerges as the weakest factor. Understanding the motivations and inhibitors of online shopping provides valuable insights into the factors influencing online shopping behavior, facilitating targeted strategies to enhance the overall online shopping experience.\u003c/p\u003e \u003cp\u003eLastly, the technology available at home latent variable is positively and significantly associated with three factors: high-speed internet, sufficient devices at home, and computers at home. This construct reflects the acknowledgment that the presence of these technological elements in one\u0026rsquo;s home environment contributes positively to the overall level of technological accessibility. Specifically, high-speed internet indicates the availability of a robust internet connection, facilitating seamless connectivity (Bournea, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Sufficient devices at home emphasize the importance of having an adequate number of devices, ensuring accessibility and convenience for various tasks (HAYES, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Finally, the computer-at-home variable highlights the significance of possessing a personal computer, a fundamental tool for a wide range of professional and personal activities (Pew Research Center, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The results show that computers at home, high-speed internet, and sufficient devices at home have the most significant impact on the technology available at home latent variable, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Main Outcomes\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the estimated results for two main outcomes, WFH and online shopping frequency. A complete set of variables and variables interactions were examined, and a variety of socio-demographics, commute and non-commute trip features, job-related factors, and attitudes/perceptions were found to be significant to WFH and online shopping choices. All the variables are statistically significant at 95%, 90%, and 85% levels. Cells marked with \u0026ldquo;_\u0026rdquo; indicate that the specific row variable does not directly impact the outcome variable in the corresponding column. A comprehensive discussion of the model results for each outcome is provided, as well as additional insights into the similarities and differences between WFH and online shopping behaviors. The similarities and differences are discussed in terms of the variables that influence WFH and online shopping frequency at the same time and those that are significant only to one of these choices.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJointly modeled WFH and online shopping frequency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWFH Freq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eOnline Shopping Freq.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ez-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ez-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual Demographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge: 60 or above \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace: Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.558***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace: Black African\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.383**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave Physical Difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.607*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.917**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eHH Characteristics\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.329***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.375***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Children Aged 13\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.231**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eJob-related Factors\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Category: Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.302*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployer Flexibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.078***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eTravel Habits\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivet Vehicle Commuter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.951***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive Mode Usage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eLatent Constructs\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWFH Comfort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWFH Unproductiveness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.052*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline Shopping Enjoyment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.795**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline Shopping Inconvenience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.172***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome Technology Availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.539***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eInteraction\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWFH Freq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.101**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eThreshold parameters\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMu (01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMu (02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMu (03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.754***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep_value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparative Fit Index (CFI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTucker-Lewis Index (TLI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoot Mean Square Error of Approximation (RMSEA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandardized Root Mean Square Residual (SRMR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e***, **, * ==\u0026gt; Significance at 95%, 90%, 85% level.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1. Individual Characteristics\u003c/h2\u003e \u003cp\u003eThe estimated results show a negative correlation between age and online shopping frequency with people who are more than 60 years old are less likely to shop online (Statista, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In fact, this generation may not be into using ICT devices which results in their unfamiliarity and discomfort with ICT and may decrease their overall propensity toward online shopping (Vaportzis et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite the frequent discussion of previous studies, the results did not reveal any statistically significant differences between gender groups in WFH and online shopping frequency (Maf\u0026eacute; and Blas, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong races, Hispanic employees were found to be less likely to WFH. This could be because such individuals likely work in construction, maintenance, and manufacturing occupations which require their presence in the field (Census, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The online shopping results, however, highlighted the tendency of Black/African American people to shop online. In fact, the potential racial profiling and false accusation of shoplifting in stores led minorities to avoid going to stores (Elan, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Individuals with physical difficulties were found to be more likely to WFH and shop online which could be because of the challenges they face when traveling, performing the job at the workplace, and shopping in stores (Bohra and Willingham, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; DIVERSEability Magazine, 2018; Peters et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2. Household Characteristics\u003c/h2\u003e \u003cp\u003eAmong household characteristics, High-income households were found to be positively associated with both WFH and online shopping frequency. This association can be explained by a couple of potential facts. First, higher-income households may have better access to WFH and online shopping technologies, resulting in a higher frequency of using such technology to both work and shop at home (Swenson and Ghertner, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Second, as the household income rises, the number of workers in the household may increase. Hence, due to the lack of time available to travel to work or stores, such households have a higher propensity for WFH and online shopping (Sener and Reeder, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Third, high-income workers may hold higher positions in business, giving them the bargaining power to negotiate for workplace flexibility (Peters et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Shabanpour et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The results showed that the presence of 13- to 17-year-old children in the household has a negative impact on online shopping frequency. Insights from Pew Research Center report emphasize that teenagers tend to prefer in-store shopping experiences over online alternatives. This observed preference among teenagers contributes to the lower prevalence of online shopping in their households (Desilver, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1. Job-related Factors\u003c/h2\u003e \u003cp\u003eThe job category is very important in WFH decision-making. Those working in education were found to be less likely to WFH frequently. The data source used for model estimation was collected in the fall of 2021 when the Centers for Disease Control and Prevention (CDC) recommended that US schools reopen for in-person learning regardless of their ability to implement all COVID-19 protective measures (The Guardian, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; U.S. Department of Education, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Since then, education employees, the majority of whom are teachers, have been required to work in person at least some of the time. Aside from employee preferences for WFH, the frequency of this ICT choice is also determined by the employer\u0026rsquo;s decision. The findings revealed that employers\u0026rsquo; flexibility toward employees\u0026rsquo; workplaces is significant to WFH incidence. The constant availability of the option to WFH was demonstrated to increase the frequency of this behavior, which is consistent with the findings of Balbontin et al. (Balbontin et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1. Travel Habits\u003c/h2\u003e \u003cp\u003eRegarding travel habits, mode choice habits were found to be significantly correlated with WFH and online shopping decisions. The findings revealed that those who commute by private vehicle are less likely to WFH. One possible explanation is that having a private vehicle causes daily commute convenient, resulting in a lower frequency of WFH (Balbontin et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mohammadi et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). On the other hand, it was found that those who frequently use active modes including walking, biking, and shared bikes as their primary means of transportation are more likely to shop online. This could be due to a combination of factors. The first consideration is the potential difficulty of carrying shopping bags while walking or biking (Ramadan et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Second, The challenges associated with active mode transportation may contribute to a higher likelihood of individuals opting for online shopping instead of traveling. Third, active mode users may be environmentally friendly and do online shopping to eliminate auto travel (Sener and Bhat, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2. Latent Constructs\u003c/h2\u003e \u003cp\u003eThe findings indicate that home technology availability plays a significant role in influencing the frequency of WFH. This suggests a positive and indirect association between the presence of high-speed internet services, possession of a laptop or desktop, and having sufficient internet-connected devices for all household members at home with the incidence of WFH. Essentially, these technological factors contribute to making information and communication technology (ICT) more accessible, resulting in a more convenient WFH experience. This could be because a well-equipped technological environment fosters the accessibility and ease of remote work, making it a more feasible and attractive option for individuals (Blitchok, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe positive and significant association between WFH frequency and the latent variable WFH comfort indicates that individuals who experience a greater level of comfort in their remote work environment are more inclined to engage in WFH more frequently. This comfort is influenced by various factors, including the extra time for more sleep, the convenience of avoiding commuting, the presence of a comfortable and well-equipped home workspace, the flexibility offered by personalized working hours, and the reduced distractions experienced at home. When individuals perceive WFH as a comfortable and productive arrangement, it naturally becomes a preferred choice. This association highlights the intricate interplay between perceived benefits and the decision to embrace remote work regularly, emphasizing the need for environments that foster both comfort and productivity for widespread WFH adoption. (Barrero et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jain et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mohammadi et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, the negative association between WFH frequency and the latent variable WFH unproductiveness suggests that as the perceived unproductiveness of remote work increases, individuals are less likely to choose WFH more frequently. In other words, when individuals face challenges and perceive their remote work as unproductive, they are inclined to avoid engaging in WFH more often. This negative relationship indicates that factors contributing to WFH unproductiveness, such as caregiving responsibilities, an uncomfortable home workspace, multi-tasking, communication difficulties, and the perception of reduced job demands, act as deterrents to the frequency of remote work. The higher the perceived obstacles to productivity, the lower the likelihood of individuals choosing WFH with greater frequency (Jain et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of online shopping, the latent variable of online shopping enjoyment was shown to be significantly and positively correlated with the incidence of this ICT choice. This indicates that the enjoyment derived from online shopping experiences, characterized by constant shopping availability, the ability to read reviews before making a purchase, the convenience of skipping travel to stores, time savings, and the availability of a variety of choices online, all play pivotal roles in influencing individuals\u0026rsquo; decisions to shop online more frequent. These factors contribute to a positive online shopping experience, fostering convenience and accessibility, ultimately shaping preferences for frequent online shopping (Duarte et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Emrich et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, the results show that inconvenient online shopping significantly decreases the probability of frequent online shopping. This reveals that the frustrating return process, shipping fees, the need for immediate access to the product, difficulty with the online shopping platform, and inaccurate online information are all barriers to frequent online shopping. The primary goal of using ICT to shop and work is to provide convenience; thus, when it is perceived as inconvenient due to the aforementioned factors, the propensity to use it decreases (Amato-McCoy, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jack et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tyrrell, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2. Interaction of WFH and Online Shopping\u003c/h2\u003e \u003cp\u003eThe results show a positive and significant causal relationship between WFH and online shopping. In other words, as individuals engage in more WFH activities, there is a corresponding and significant rise in their online shopping behavior. This interaction can be explained by several factors. Firstly, as discussed in section \u0026lrm;1, individuals may be inclined to engage in online shopping when working from home, especially when spending extended periods in front of the computer. The seamless integration of online shopping into the WFH environment offers individuals the convenience to browse and make purchases during breaks or downtime. Secondly, the elimination of natural opportunities to trip chain shopping episodes, typically associated with commuting, when working from home potentially contributes to an increase in online shopping frequency. The absence of commute-related constraints allows individuals to engage in online shopping more readily, shaping a positive causal relationship between the frequency of working from home and the frequency of online shopping (Golob and Regan, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; PYMNTS, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3. Goodness of Fit\u003c/h2\u003e \u003cp\u003eIn this study, we explored how different latent variables interact dynamically, without starting with specific expectations. The Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) show noteworthy values of 0.861 and 0.916, respectively, highlighting the model\u0026rsquo;s proficiency in navigating intricate stochastic relationships. The Root Mean Square Error of Approximation (RMSEA) impressively maintains a low threshold at 0.025, indicating a meticulous alignment between model predictions and observed data. Furthermore, the Standardized Root Mean Square Residual (SRMR) at 0.064 signifies a reasonable fit. In the absence of pre-established hypotheses, these results collectively shed light on the interplay between latent variables and outcomes. This prompts careful consideration of the model's ability to unravel intricate relationships, inviting further scholarly exploration and discourse.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. DISCUSSION","content":"\u003cp\u003eDiscussing the results from a policymaker\u0026rsquo;s perspective, it is important to acknowledge both opportunities and challenges of WFH and E-commerce growth. With more people working from home, there is a potential decrease in the demand for daily commuting, leading to reduced traffic congestion, lower fuel consumption, and fewer carbon emissions (Hensher et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shabanpour et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, the shift to remote work could influence the demand for commercial office space, impacting transportation infrastructure designed to support commuting to and from central business districts (Mischke et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, the reduced demand for public transportation services may pose financial challenges for transit agencies, potentially resulting in service cuts that affect those who depend on public transportation (Javadinasr et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOnline shopping is set to exceed \u003cspan\u003e$\u003c/span\u003e1.8 trillion in sales by 2023 (Bonde and Burno, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). With a larger market share and fewer barriers compared to traditional commerce, e-commerce fuels entrepreneurship and inspires businesses of all sizes to join the competition. Although online shopping brings a better economy, higher employment rate, and convenience to customers, its impact on city logistics and transportation systems is debatable (Savelsbergh and Van Woensel, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The diffusion of online shopping would increase congestion, illegal parking of delivery trucks, emissions, and problems for logistic providers, due to the widespread presence of trucks for deliveries within the urban environment (Ma et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePolicymakers need to find a balance between promoting remote work and online shopping activities and addressing their potential challenges. The findings of this study help them to gain insights into effective strategies in both cases if they need to promote or demote these activities.\u003c/p\u003e \u003cp\u003ePerhaps the most important conclusion that can be drawn from the findings is the positive causal interrelationship between WFH and online shopping frequency. It suggests that working from home reinforces e-commerce growth. Policymakers can leverage this casual interrelationship to regulate E-commerce. For instance, businesses can increase their market share if they target remote workers as their online customers (PYMNTS, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe findings highlight the influence of several factors on WFH behavior. Particularly, the employer\u0026rsquo;s flexibility toward remote work was found to be significant in employee\u0026rsquo;s decisions on WFH frequency. The frequency of WFH would increase when employers actively support it by granting employees the flexibility to choose their work environment. As highlighted by McKinsey, since the COVID-19 pandemic pushed the majority to WFH, many employees have requested more workplace flexibility (De Smet et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, it becomes crucial for companies to develop WFH policies that effectively balance the satisfaction of both employers and employees.\u003c/p\u003e \u003cp\u003eTo implement a successful WFH program, employers should consider policies that accommodate the needs and preferences of their business and their workforce. This may involve offering guidance and resources to facilitate remote work, such as ensuring access to essential technology and tools, while also addressing potential productivity challenges encountered at home. Employees need adequate equipment and resources at home to accomplish their work effectively. Therefore, employers should evaluate the required technology in light of budget and employee productivity at home (Bayern, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Ensuring that employees have access to reliable internet connections, appropriate software, and comfortable workstations can contribute to their productivity and overall job satisfaction.\u003c/p\u003e \u003cp\u003eFurthermore, the availability of childcare facilities and support can enhance the WFH experience for working parents. According to a recent report by Harvard Business School, the effects of childcare have intensified since the COVID-19 pandemic has eliminated the safety net of schools and employer-provided daycare for working parents. Given that one-third of the US workforce includes parents with children under the age of 14, employers can consider incorporating a solid form of childcare solutions into their business infrastructure (Modestino et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This can include providing resources for remote learning assistance, flexible work schedules, or even subsidizing external childcare options to support working parents in balancing their professional responsibilities with their childcare responsibilities (Del Boca et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the online shopping context, the findings highlight the role of convenience in online shopping frequency. Convenience is an important factor that may include various elements contributing to a positive and satisfying online shopping experience. For instance, taking proactive measures to optimize online shopping platforms and ensure they meet the evolving expectations of customers. For example, retailers can enhance their online platforms by keeping them up-to-date, visually appealing, and equipped with user-friendly features. This necessitates regular updating of the website\u0026rsquo;s design, and incorporating a powerful search engine that allows customers to find products easily (Beauchamp and Bednarz, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Duarte et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, implementing autocomplete functionality makes the search process efficient and time-saving (Kollmann et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Online shopping platforms can also be improved by providing clear and complete product information. Detailed descriptions, high-quality product images, and accurate specifications play a crucial role in helping customers make informed purchase decisions (Duarte et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, real, and legitimate reviews provide valuable insights into the product\u0026rsquo;s quality, performance, and suitability, building trust and confidence among potential buyers (Duarte et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, an easy check-out process can promote customer experience with online shopping platforms. Simplifying the steps required to complete a purchase, offering various secure payment options, and minimizing form filling can significantly enhance the overall shopping experience (Berry et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Duarte et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, utilizing the power of data-driven product recommendations significantly elevates convenience in online shopping. By analyzing customer behavior, preferences, and purchase history, retailers can offer personalized suggestions tailored to everyone\u0026rsquo;s interests and needs. This facilitates the shopping process and helps customers discover relevant products they might have otherwise missed, increasing the likelihood of making additional purchases (Lee and Kwon, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOnline retailers can increase their market share by providing robust services to their customers. For example, offering a flexible return policy can significantly enhance the return process for customers. This can include free return services, allowing sufficient time for customers to make return decisions, and offering convenient drop-off locations (Tyrrell, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, it is worth noting that the \u0026ldquo;buy online, return in store\u0026rdquo; policy, while offering convenience to customers, can impact retail business profits due to added costs, ECR retail loss said (Jack et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to facilitating returns, online retailers can prioritize effective customer service channels. Having available agents to engage in online chat, phone conversations, and email correspondence significantly enhances the overall customer experience (Tyrrell, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Interestingly, according to AMC Technology, 57% of customers prefer to contact retail businesses via online platforms such as social media or email rather than using voice-based customer service (AMC Technology, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, addressing concerns related to delivery fees and times is crucial for online retailers. Providing customers with accurate delivery time estimates and offering expedited delivery services can foster trust and confidence in the retailer. Chain Store Age emphasizes the importance of meeting customers\u0026rsquo; delivery expectations, as it serves as a fundamental factor in building a strong and loyal customer base (Amato-McCoy, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e"},{"header":"6. CONCLUSION","content":"\u003cp\u003eThe growing popularity of WFH and online shopping has presented new opportunities for convenience, flexibility, and sustainability. This study is the first to our knowledge that explores the interplay between WFH and online shopping, the two popular ICT-based behaviors, since the COVID-19 pandemic. This study also examines the recent evolutions of WFH and online shopping activities using data from a comprehensive survey administered in late 2021 by our research group. Our analysis assesses the impacts of individual and household characteristics, job-related factors, travel habits, home-technology features as well as attitudinal factors. We employed a Generalized Structural Equation Model (GSEM), which accounts for both observed and unobserved (i.e., latent) variables, and thus captures complexities in decision-making.\u003c/p\u003e \u003cp\u003eThe impacts of attitudinal factors were captured indirectly through latent variables. We considered four psychological latent constructs: WFH comfort, WFH unproductiveness, online shopping enjoyment, and online shopping inconvenience. Additionally, we constructed a latent variable named home-tech availability based on accessibility to different attributes of technology like high-speed internet and computers at home. \u0026ldquo;WFH Comfort\u0026rdquo; measures positive perceptions, such as a more comfortable home workspace, fewer distractions at home, and no commuting, which has been shown to cause a higher frequency of remote work. Conversely, \u0026ldquo;WFH Unproductive\u0026rdquo; represents negative WFH attitudes including more multi-tasking, lack of comfortable workspace, and difficult communication with co-workers, which decreases WFH frequency. Furthermore, access to adequate technological resources at home plays a role in the frequency of WFH and online shopping practices.\u003c/p\u003e \u003cp\u003eIn the context of online shopping, the \u0026ldquo;Online Shopping Enjoyment\u0026rdquo; latent variable is a measure of positive online shopping experience impacted by timesaving and flexibility, offering a variety of options, and other factors. Online shopping enjoyment drives frequent engagement, while online shopping inconvenience latent variable, which is associated with perceived challenges of this activity, decreases engagement frequency. The results show that such challenges include product accessibility, return process, and delivery fees.\u003c/p\u003e \u003cp\u003eThe findings demonstrate a significant positive causal relationship between WFH and online shopping such that the frequency of WFH positively affects the incidence of online shopping. For observed factors, we found demographics, such as age and ethnicity, influence WFH and online shopping behaviors, with Hispanic employees less likely to WFH and 60-year-old and above individuals less likely to shop online, while Black/African American individuals show a preference for online shopping. Physical difficulties and high-income households are associated with higher frequencies of both WFH and online shopping. Additionally, job category, employer flexibility, and commute mode choice habits were identified as important factors in WFH decisions.\u003c/p\u003e \u003cp\u003eThis study can be extended in some ways for future research. First, those employees who do not have the option to WFH (i.e., 0 WFH days) and those who are required to do so full-time (i.e., 5 WFH days out of 5 working days) were excluded from this study due to the predetermined choice of WFH. Future research can investigate such employees\u0026rsquo; online shopping habits and how their WFH frequency influences these habits. Second, while our study provides valuable insights into the dynamics of WFH and online shopping behaviors, it is important to acknowledge certain limitations primarily stemming from the survey design. For example, our study incorporates crucial attitudinal factors, such as WFH productivity positive/negative factors and motivations/inhibitors related to online shopping frequency, however, we faced limitations due to our measurement approach. The use of a binary scale, while practical, constrains the variability and depth of information compared to a more nuanced 5-Likert scale. This limitation might affect how detailed and clear our analysis is and how well we can understand the small, important details in how people feel and think. Additionally, our survey design did not collect information on potential influential factors in WFH frequency, such as extra pay, benefits, or other incentives. Future research endeavors could benefit from employing more nuanced measurement scales and exploring a broader array of factors to enhance the comprehensiveness of our understanding of these domains.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis research is conducted by ASU and UIC and funded by grants from the National Science Foundation and the U.S. Department of Transportation. This study has been reviewed and approved by both the ASU Institutional Review Board and the UIC Institutional Review Board (Protocol #568) for the protection of study participants. If you have any questions about your rights as a subject/participant in this research, or if you feel you have been placed at risk, you may contact the Institutional Review Board at ASU\u0026rsquo;s Office of Research Integrity and Assurance, at (480) 965‑6788, or the UIC Institutional Review Board, at (312) 996-1711.\u003c/p\u003e\n\u003cp\u003eACKNOWLEDGEMENT\u003c/p\u003e\n\u003cp\u003eThis research was supported in part by the National Science Foundation (NSF) RAPID program under grants no. 2030156 and 2029962, awarded to the University of Illinois at Chicago and Arizona State University. Also, this study was supported by the Center for Teaching Old Models New Tricks (TOMNET), a University Transportation Center sponsored by the U.S. Department of Transportation through grant no. 69A3551747116, as well as from the Knowledge Exchange for Resilience at Arizona State University. This COVID-19 Working Group effort was also supported by the NSF-funded Social Science Extreme Events Research (SSEER) network and the CONVERGE facility at the Natural Hazards Center at the University of Colorado Boulder (NSF Award #1841338) and the NSF CAREER award under grant no. 155173. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003eAuthor Contribution\u003c/p\u003e\n\u003cp\u003eThe authors confirm their contribution to the paper as follows: study conception and design: M. M., A. D., R. S., and A. M.; data collection: M. M., D. S., S. D., R. P., and A. M.; analysis and interpretation of results: M. M., A. D., R. S., and A. M.; draft manuscript preparation: M. M., A. D., R. S., and S. A. All authors reviewed the results and approved the final version of the manuscript. The authors do not have any conflicts of interest to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAmato-McCoy, D.M., 2016. Delivery time impacts online shoppers\u0026rsquo; purchase decisions [WWW Document]. URL https://chainstoreage.com/news/study-delivery-time-impacts-online-shoppers-purchase-decisions (accessed 7.19.22).\u003c/li\u003e\n \u003cli\u003eAMC Technology, 2022. AMC Technology on Twitter: \u0026ldquo;57% of customers would rather contact companies via digital media such as email or social media rather than use voice-based customer support. Learn how to diversify your contact center and move beyond voice channels\u0026rdquo; [WWW Document]. URL https://twitter.com/AMCTechnology/status/1527686045063602176 (accessed 7.21.22).\u003c/li\u003e\n \u003cli\u003eAndruetto, C., Bin, E., Susilo, Y., Pernest\u0026aring;l, A., 2023. 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URL https://www.ed.gov/news/press-releases/us-department-education-releases-\u0026ldquo;return-school-roadmap\u0026rdquo;-support-students-schools-educators-and-communities-preparing-2021-2022-school-year (accessed 7.19.22).\u003c/li\u003e\n \u003cli\u003eVaportzis, E., Clausen, M.G., Gow, A.J., 2017. Older adults perceptions of technology and barriers to interacting with tablet computers: A focus group study. Front Psychol 8. https://doi.org/10.3389/fpsyg.2017.01687\u003c/li\u003e\n \u003cli\u003eVarma, K. V., Ho, C.I., Stanek, D.M., Mokhtarian, P.L., 1998. Duration and frequency of telecenter use: once a telecommuter, always a telecommuter? Transp Res Part C Emerg Technol 6, 47\u0026ndash;68. https://doi.org/10.1016/S0968-090X(98)00007-2\u003c/li\u003e\n \u003cli\u003eWalls, M., Safirova, E., Jiang, Y., 2007. What Drives Telecommuting?: Relative Impact of Worker Demographics, Employer Characteristics, and Job Types. https://doi.org/10.3141/2010-13 111\u0026ndash;120. https://doi.org/10.3141/2010-13\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Some individuals selected multiple races as their identification, hence the summation of the percentages of all choices is not equal to 100.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"transportation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"port","sideBox":"Learn more about [Transportation](http://link.springer.com/journal/11116)","snPcode":"11116","submissionUrl":"https://submission.nature.com/new-submission/11116/3","title":"Transportation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Work from home (WFH), online shopping, information and communication technology (ICT), SEM, COVID-19","lastPublishedDoi":"10.21203/rs.3.rs-3974111/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3974111/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe growing behaviors of work-from-home (WFH) and online shopping hold significant potential for reducing traffic congestion and emissions. Understanding the frequency and the interplay between these two behaviors is important for successful implementation. This study investigates the recent trends of WFH and online shopping and the underlying factors influencing individuals\u0026rsquo; decisions on these two behaviors. Focusing on non-grocery online shopping, this study uses comprehensive survey data collected across the United States during October and November 2021. We develop a Generalized Structural Equation Model (GSEM) to jointly examine WFH and online shopping frequency and their interaction. Moreover, the study investigates the psychological aspects of WFH and online shopping, introducing four stochastic latent constructs\u0026mdash;WFH comfort, WFH unproductiveness, online shopping enjoyment, and online shopping inconvenience using the attitudinal variables. Results indicate a positive causal relationship, suggesting that increased WFH promotes online shopping engagement. Perceived comfort and productivity at home affect WFH frequency shaped by factors like home workspace, commuting time, childcare responsibilities, and telecommunications with co-workers. Likewise, perceived convenience and enjoyment significantly affect online shopping, influenced by aspects such as timesaving, and the delivery and return process. Technological tools at home also play a role in WFH frequency. Demographic factors like age, race, income, physical disability, and mode choice habits correlate with WFH and online shopping incidence, while job category and employer flexibility influence WFH frequency. These insights can help policymakers to regulate remote work and online shopping activities as they continue to grow.\u003c/p\u003e","manuscriptTitle":"The Interaction Between the Recent Evolution of Working from Home and Online Shopping","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 18:39:38","doi":"10.21203/rs.3.rs-3974111/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"transportation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"port","sideBox":"Learn more about [Transportation](http://link.springer.com/journal/11116)","snPcode":"11116","submissionUrl":"https://submission.nature.com/new-submission/11116/3","title":"Transportation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c0b72909-5eab-4cfc-8526-9fa12913dcda","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-27T00:34:05+00:00","versionOfRecord":{"articleIdentity":"rs-3974111","link":"https://doi.org/10.1007/s11116-024-10506-4","journal":{"identity":"transportation","isVorOnly":false,"title":"Transportation"},"publishedOn":"2024-06-26 00:34:05","publishedOnDateReadable":"June 26th, 2024"},"versionCreatedAt":"2024-02-28 18:39:38","video":"","vorDoi":"10.1007/s11116-024-10506-4","vorDoiUrl":"https://doi.org/10.1007/s11116-024-10506-4","workflowStages":[]},"version":"v1","identity":"rs-3974111","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3974111","identity":"rs-3974111","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}