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Development of E-Commerce Technology in World of Online Business

E S Soegoto 1 , A Christiani 2 and D Oktafiani 3

Published under licence by IOP Publishing Ltd IOP Conference Series: Materials Science and Engineering , Volume 407 , International Conference on Informatics, Engineering, Science and Technology (INCITEST) 9 May 2018, Bandung, Indonesia Citation E S Soegoto et al 2018 IOP Conf. Ser.: Mater. Sci. Eng. 407 012031 DOI 10.1088/1757-899X/407/1/012031

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1 Departemen Manajemen, Universitas Komputer Indonesia, Indonesia

2 Departemen Teknik dan Ilmu Komputer, Universitas Komputer Indonesia, Indonesia

3 Departemen Bahasa dan Sastra Inggris, Universitas Komputer Indonesia, Indonesia

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The purpose of this research is to identify the development of e-commerce technology in the business world, and the benefits of the application of e-commerce in the online business. The method used in this research was the descriptive method, to present a complete overview of the situation related to some variable situations examined. The result of this research was to identify how far the development of e-commerce technology in the world of online business, as well as the benefits provided by the application of e-commerce in the world of online business. The research was done by discussing the payment transaction system, sales, and reservations made by the business by using e-commerce technology.

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The impact of COVID-19 on the evolution of online retail: The pandemic as a window of opportunity

Levente szász.

a Faculty of Economics and Business Administration, Babeș-Bolyai University, 400591, Cluj-Napoca, Teodor Mihali str, 58-60, Romania

Csaba Bálint

b National Bank of Romania, 030031, Bucharest, Lipscani str. 25, sector 3, Romania

Ottó Csíki

Bálint zsolt nagy, béla-gergely rácz, dénes csala.

c Department of Engineering, Lancaster University, Engineering Building, Lancaster University, Lancaster, LA1 4YW, United Kingdom

d Economics Observatory, School of Economics, University of Bristol, Beacon House, Queens Road, Bristol, BS8 1QU, United Kingdom

Lloyd C. Harris

e Alliance Manchester Business School, University of Manchester, Booth St W, Manchester, M15 6PB, United Kingdom

Associated Data

Data will be made available on request.

Pandemic-related shocks have induced an unexpected volatility into the evolution of online sales, making it difficult for retailers to cope with frequently occurring, drastic changes in demand. Relying on a socio-technical approach, the purpose of this paper is to (a) offer a deeper insight into the driving forces of online sales during the pandemic, and (b) investigate whether pandemic-related shocks accelerate the long-term growth of online retail. Novel, high-frequency data on GPS-based population mobility and government stringency is used to demonstrate how time spent in residential areas and governmental restrictions drive the monthly evolution of online sales in 23 countries. We deconstruct these effects into three main phases: lure-in, lock-in, and phase-out. Lastly, using time series analysis, we show that the pandemic has induced a level shift into the long-term growth trend of the online retail sector in the majority of countries investigated.

1. Introduction

The outbreak of the pandemic caused by the spread of a novel type of coronavirus, SARS-CoV-2, has induced an unprecedented shock to the global economy in terms of its speed and encompassing nature, having a significant impact on virtually all countries and economic sectors. During the pandemic, businesses and consumers have been forced continuously to adapt to the immediate and drastic changes brought about by this crisis. Furthermore, there is a general consensus that there will be long lasting global effects and the world economy will return to a “ new normal ” ( Roggeveen and Sethuraman, 2020 ; Sneader and Singhal, 2021 ).

As with similar health-related and economic crises in the past, it is widely accepted that online retail represents a sector that plays a crucial role ( Li et al., 2020 ; Guthrie et al., 2021 ), providing vital access for customers to essential products ( Kirk and Rifkin, 2020 ; Martin-Neuninger and Ruby, 2020 ). Given its significant role, the present paper focuses on the evolution of online retail during the COVID-19 pandemic and analyses the short-term and potential long-lasting effects of this crisis.

Most of the existing papers studying the interaction between the early-stage of the pandemic and the online retail sector report that in several countries the outbreak of COVID-19 led to an unprecedented surge in online retail demand (e.g., Gao et al., 2020 ; Hobbs, 2020 ; Hwang et al., 2020 ). These observations are supported by commentators suggesting that in 2020 the “share of e-commerce in retail sales grew at two to five times the rate before COVID-19” ( Lund et al., 2021 ). However, only a few studies acknowledge that, beyond the general upswing, the pandemic has increased the volatility of online sales evolution. Furthermore, literature offers little guidance on which factors can explain these changes in online sales during a crisis when traditional market mechanisms do not function as usual. In response, therefore, this paper aims to use large-scale, longitudinal data covering 23 different countries and multiple waves of the pandemic to investigate the drivers of short-term online retail evolution during COVID-19 .

While some researchers have tentatively begun to explore these short-term effects (e.g., Chang and Meyerhoefer, 2021 ; Eger et al., 2021 ), the longer-lasting implications of the pandemic on the online retail sector have yet to be studied empirically. Most scholars emphasize the need to investigate whether the pandemic has truly altered the evolution trajectory of online retail or if the current crisis is merely a single shock after which the sector will return to its traditional evolutionary path as consumers and retail businesses return to their “old habits” in the post-pandemic period ( Sheth, 2020 ; Eger et al., 2021 ; Reardon et al., 2021 ; Schleper et al., 2021 ). Consequently, given the uncertainty of what the “ new normal ” might bring for the online retail market, this paper also intends to use the most recent time series to investigate whether the pandemic has altered the long-term evolution of the sector .

In pursuing the two objectives (investigation of short-term drivers and long-term trend implications), this paper adopts Geels’ (2002) multi-level perspective (MLP) as a theoretical lens to investigate technological transitions in a complex socio-technical context. We interpret the pandemic as a force capable of opening a “window of opportunity” ( Dannenberg et al., 2020 ). Such windows constitute powerful tensions created at the level of the socio-technical landscape that bring a unique possibility for a technological novelty to break through and become more dominant in mass markets ( Geels, 2004 ). Hence, we explore the interplay between the window of opportunity opened by COVID-19 and the growth of the online retail sector. More specifically, we aim to investigate (a) the short-term driving forces behind the exponential evolution of the online retail sector during the pandemic, and (b) whether the pandemic has truly created a window of opportunity for a positive shift in the long-term evolution of online retail. Along with pursuing these objectives we also aim to provide a theoretical contribution to the literature on windows of opportunity, a central concept that has received only limited attention in previous MLP studies ( Geels, 2011 ; Dannenberg et al., 2020 ). In this regard, our paper aims to offer a more detailed insight into how a technological transition path might behave during such a period and to provide a means to evaluate the potential long-term effect of windows of opportunity.

2. Literature review

2.1. the impact of covid-19 on online retail.

Given the crucial role of online retail channels during a pandemic, researchers have examined a variety of ways in which COVID-19 has influenced online shopping. As COVID-19 was first identified in China, initial studies investigated how the outbreak of the crisis has reshaped the retail landscape in China with emphasis on the increasing importance of online channels ( Gao et al., 2020 ; Guo et al., 2020 ; Hao et al., 2020 ; Li et al., 2020 ; Jiang and Stylos, 2021 ). These studies focused on how the outbreak of the pandemic influenced online shopping ( Gao et al., 2020 ; Guo et al., 2020 ), and how online channels helped the population to cope with the emerging health-crisis ( Li et al., 2020 ; Hao et al., 2020 ).

Given the narrow focus of initial studies, authors called for further research in other countries better to understand the global impact of the pandemic on online retail ( Gao et al., 2020 ; Li et al., 2020 ; Jiang and Stylos, 2021 ). Subsequent studies taking this research avenue offered a good cross-section globally by covering multiple different countries but investigated almost exclusively the short-term impacts of COVID-19 on online retail, using data from the first wave of the pandemic ( Table 1 ). Moreover, observers typically argued that the major driving forces behind the exponential proliferation of online channel use in the context of COVID-19, can be grouped in two distinct, but intertwined categories: (a) governmental regulations and restrictions, and (b) pandemic-induced changes in customer behavior. In line with this observation, Shankar et al. (2021) also contend that “many shoppers move a large portion of their business online during the COVID-19 outbreak either by choice or due to regulation …” . Therefore, the next two subsections review the studies that attribute the changes in online sales to one of these two factors.

Summary of the literature on the impact of COVID-19 on the evolution of online retail.

2.1.1. Studies highlighting the impact of changing customer behavior

Adopting a behavioral perspective, Chang and Meyerhoefer (2021) illustrated how the first wave in Taiwan (where no strict stay-at-home orders or business closures were imposed) has shifted consumers’ attention towards online channels. In the early weeks of the pandemic the surge in the number of confirmed cases increased both sales and the number of customers of online food commerce. The change in customer behavior was also induced by the media, as COVID-19 related press articles and Google searches also positively correlated with online food sales.

In a similar manner, Sheth (2020) argued that the pandemic had several powerful and immediate effects on consumer behavior: while facing constraints, consumers improvised and replaced old habits with new ones, such as switching to online retail channels, enabling thereby the “store to come home”. In line with this, Jiang and Stylos (2021) proposed that individual pressures during lockdowns force consumers to create a “new retail purchasing normality” involving higher digital engagement and increased online purchases. Consultancy papers also supported this view. A multi-country survey conducted by McKinsey & Company demonstrated that the pandemic induced a major shift in consumer behavior, at least two thirds of customers having tried new, mostly online forms of shopping ( Sneader and Singhal, 2021 ).

In terms of shifting consumer behavior, Tran (2021) proposed that fear of the pandemic can also drive online purchasing intentions aiming to improve the health safety of the consumer and the surrounding community. Researchers focusing on the second wave of the pandemic ( Chopdar et al., 2022 ; Eger et al., 2021 ) also connected the fear of the virus to increased online shopping. One exception is identified by Mehrolia et al. (2021) , concluding that a considerable majority of Indian customers decided not to order food through online channels during the first wave of the pandemic due to the fear connected to food delivery.

Hao et al. (2020) focused on a different aspect of customer behavior. Their study points out that panic buying (i.e., ordering more than the short-term necessity of the household due to fear), which is a common consumer response during disasters, is more associated with online food retail channels than with traditional channels. Following this idea, Guthrie et al. (2021) use the react-cope-adapt model ( Kirk and Rifkin, 2020 ) to illustrate that during the first month of the pandemic in France consumers reacted by panic buying, dramatically increasing the online purchasing of essential products. This period was followed by coping with the crisis which led to an increase of online orders related to non-essential products. The adapt phase was supposed to show a sustained modification of online purchasing behavior. However, due to limited data available, the authors concluded that long-term behavior changes require further investigation.

2.1.2. Studies highlighting the impact of government regulations

During the pandemic, several governmental restrictions had an immediate impact on online retail. For example, Martin-Neuninger and Ruby (2020) and Hall et al. (2021) identify government-related factors, namely the lockdown period and travel restrictions, as primary reasons behind the surge in online shopping in New Zealand. Hobbs (2020) also argued that initial stay-at-home and distancing orders issued in Canada led to an uptake of the online food retail: while online grocery deliveries were already used by early adopters in the pre-pandemic era, during the outbreak many late-adopter customers tried this channel for the first time. Jílková and Králová (2021) reported similar phenomena in the Czech Republic for all generational cohorts. In summary, unexpected regulations imposed by governments determined an immediate increase in demand for online shopping: existing customers started to use online channels more frequently, while new customers, including older and less tech-savvy generations, turned to online channels for the first time ( Hwang et al., 2020 ; Pantano et al., 2020 ).

From the retailer’s perspective, Reardon et al. (2021) provided several case examples of Asian and Latin American food industry firms strengthening their e-commerce business models or reconfiguring their entire food supply chains as a response to early-stage lockdown policies. Based on a survey among small Belgian retailers, Beckers et al. (2021) found that restrictions have doubled online orders during the first wave of the pandemic. To match the increase in demand, half of the retailers not using online channels before the pandemic opened one during the first months of COVID-19. Based on a literature review, Kirk and Rifkin (2020) also predicted that in order to conform to social distancing regulations, online retail coupled with contactless distribution methods would substantially gain ground during the pandemic. However, results related to the long-lasting effects of the pandemic on online retail are still “speculative in nature” ( Hobbs, 2020 ). Many of the customers who made the shift due to the restrictions might continue to utilize online channels in the long run. Other customers might return to traditional channels as soon as possible ( Beckers et al., 2021 ; Mehrolia et al., 2021 ). Thus, whether online retail can capitalize on the pandemic in the long run is still a subject of debate.

2.1.3. Summary and research questions

A summary of the key studies is provided in Table 1 in chronological order, highlighting the short-term drivers (i.e., government regulations and/or customer behavior, beside the papers narrowly focusing on the effect of the pandemic itself) and potential long-term implications related to the growth of the online retail sector.

Based on the literature, we derive two main conclusions that serve as basis for our research questions. First, as demonstrated in Table 1 , there is a plethora of mostly anecdotal, non-empirically-based evidence that during the pandemic (and beside the pandemic itself) two major factors, i.e., government restrictions and consumer behavior changes, drove a significant initial surge in online shopping. Second, extant studies failed to offer insights into how these factors drive online sales during the entire period of the current pandemic ( Schleper et al., 2021 ). Therefore, we cover the full period of COVID-19 to date and provide more conclusive empirical evidence on how these two factors influence the evolution of online retail.

RQ1. How do changes in customer behavior and government regulations drive the evolution of online retail during the pandemic?

Moreover, the long-term implications of this change in online retail use have remained, so far, a subject of anecdotal speculation ( Table 1 ). However, changes to the retail sector might become a constant in the “ new normal ”, and further research is needed “to understand the short-term and long-term impact of the pandemic on consumer behavior and provide guidance on how retailers should cope with those changes” ( Roggeveen and Sethuraman, 2020 ). Hobbs (2020) suggested that COVID-19 prompted sceptics and late-adopters to use online retail channels, and these new customers are likely to continue to shop online even after the pandemic. More cautious voices, however, asked the question whether the pandemic has “swung the pendulum too far and too fast towards online shopping” ( Gauri et al., 2021 ), which may potentially result in an unsustainable boost to online retail. Thus, the extent to which this shift will lead to a fundamental leap in the long-term role of online retailing is unknown.

RQ2. What trend-shifting impact does the pandemic have on the long-term evolution of online retail?

In answering RQ1 and RQ2 we also aim to extend the scope of existing research ( Table 1 ) in four different aspects. Given that COVID-19 is a global phenomenon, we aim to cover a larger geographical region compared to the majority of previous studies focusing on a single country. Second, in contrast with existing research mostly investigating a single branch of the online retail sector, we propose to analyze the online retail sector as a whole, covering the sales of all types of products. Third, we integrate novel measures into the analysis that have emerged during this pandemic (mobility indicators, government stringency index) to be able better to explain the evolution of the online retail sector during this crisis. Fourth, we investigate a longer period before and during the pandemic than previous studies to infer long-term implications.

2.2. A socio-technical approach to study the evolution of online retail during COVID-19

The multi-level perspective (MLP) has been established as insightful in studying COVID-19 related developments in the online retail sector ( Dannenberg et al., 2020 ). Consequently, we use the MLP as a theoretical lens to study the short and long-term evolution of online retail. Geels (2002) argues that the central tenet of MLP is that technological transitions are not only dependent on the development of the technology itself, but also pivot on the broader socio-technical context. In line with this view, technological transition represents a change from one socio-technical configuration (regime) to other: beyond the substitution of an older technology with a newer one, such transitions include changes in other socio-technical dimensions such as infrastructures, policies, user practices, and markets ( Geels, 2002 , 2004 ).

According to the MLP, technological transitions are shaped by the interaction between developments unfolding on three analytical levels ( Geels, 2002 , 2004 , 2011 ):

• • Technological niches represent the micro-level of the MLP. Niches are quasi-protected spaces where radical innovations are developed (e.g., R&D laboratories, subsidized development projects, or specific user categories supporting emerging innovations). They are unstable socio-technical configurations where innovations are carried out by a limited number of actors. Processes in the niche are gradually linked together and stabilize in time into a dominant design that allows for the radical innovation to break through to the next level.
• • Socio-technical regimes represent the meso-level of the MLP. Regimes refer to “the semi-coherent set of rules that orient and coordinate the activities of social groups” ( Geels, 2011 ) creating thereby a “deep structure” that ensures the stability of the current socio-technical system. Nevertheless, the semi-coherence of these rules allows for a dynamic stability which enables further incremental innovation, with small adjustments accumulating into stable technological transition paths. A socio-technical regime is formed by the co-evolution of different sub-regimes, each with its own set of rules and dynamics: user and market, technological, science, policy, and socio-cultural sub-regimes. According to Geels (2004) , the socio-technical regime can be understood as the meta-coordination of the different sub-regimes that determines technology adoption and use.
• • The socio-technical landscape represents the macro-level of the MLP. The landscape provides a wider, technology-external context for the interactions of actors within the niche and the socio-technical regime. Actors cannot influence elements of the landscape on the short-run, and changes at the landscape level take place usually slowly, representing longer-term, deep structural tendencies (e.g., macroeconomic processes, cultural patterns, political trends).

An important implication of the MLP is that the future evolution of a (new) technology does not only depend on the processes within the niche, but also on the interactions between different levels; including the regime and landscape levels. Geels and Schot (2007) contend that the general pattern of technology transition involves all three levels: (1) niche innovations align and gain internal momentum, (2) landscape developments put pressure on existing regimes, and (3) regimes destabilize creating an opportunity for niche innovations to break through to mass markets.

In terms of the interplay between COVID-19 and online retailing, another important concept linked to the MLP is the “window of opportunity”. Geels (2002) argues that windows of opportunity are created when tensions appear in the current socio-technical regime or when landscape developments put a pressure on the current regime for internal restructuring. These tensions loosen the rules of the socio-technical regime and create opportunities for technologies to escape the niche-level and become more deeply embedded in the regime. Competition with the existing technology becomes more intensive, triggering wider changes in the regime, where the new technology may replace the old one in the long run ( Geels, 2004 ).

Dannenberg et al. (2020) conclude that COVID-19 represents a critical landscape development that puts pressure on the socio-technical configuration of the retail sector. In line with our literature review, they suggest that two sub-regimes were particularly affected: policy regime (government regulations) and, user and market regime (sudden change in customer behavior). The authors further argue that these two major changes have opened a window of opportunity for online grocery retail to gain substantial market share. In this regard, RQ1 aims to investigate how the developments within these two dimensions influence the evolution of the online retail sector during the opening up of a window of opportunity ( Fig. 1 ). Given that, to date, the MLP offers little insight into the evolution of a technology during a window of opportunity ( Dannenberg et al., 2020 ), answering RQ1 should enrich this theoretical framework by explicating the forces that drive technology transitions during tensions in the landscape and the socio-technical regime (i.e., during a window of opportunity).

COVID-19 and the trajectory of online retail evolution (adapted from: Geels, 2002 ; Dannenberg et al., 2020 ).

Concerning the long-term impact of this window of opportunity, we investigate whether it enables the online retail sector to gain a significantly higher share of the whole retail sector on the long run (technology trajectory in Fig. 1 ) to the detriment of offline channels ( Helm et al., 2020 ). However, in the long run, MLP is not necessarily about mapping “winning” technologies that entirely replace/reconfigure existing regimes: it is just as possible that the breakthrough of a new technology will lead to a symbiosis with incumbent socio-technical regimes ( Geels, 2002 ; Genus and Coles, 2008 ). Thus, in our case, the question is more about the relative share of online retail and physical retail within the retail sector (cf. omnichannel retailing, Gauri et al., 2021 ). Beside speculation, current literature offers little guidance in this regard. Dannenberg et al. (2020) suggest that even if the pandemic has led to an upswing of online shopping, there is no indication for a fundamental long-term shift from physical to online retail. The authors, however, base their assumptions on a limited set of data, both from a temporal (March–May 2020) and from a geographical/sectoral perspective (German grocery retail). On the other hand, many other authors advocate a breakthrough of online retail as a result of taking advantage of the window of opportunity created by the pandemic (e.g., Chang and Meyerhoefer, 2021 ; Hobbs, 2020 ; Tran, 2021 ). Answering RQ2 is designed to explicate and illuminate further this debate.

3. Data and variables

3.1. data used in short-term analysis (rq1).

To investigate RQ1, we use as dependent variable the monthly evolution of online retail sales during the pandemic (Feb 2020–Jan 2022) in European countries. We rely on Beckers et al. (2021) who define online retail channel use as the selling of goods via mail, phone, website, or social media. Therefore, we adopt NACE-level retail trade data published by Eurostat using the index of deflated turnover (i.e., turnover in real terms, 2015 = 100) for the “Retail sale via mail order houses or via Internet” sector. Seasonally and calendar adjusted time series data is used to assess the monthly changes during the pandemic in this sector, shortly denoted from now on “online retail” ( ΔOnline_retail ). In terms of countries, the Eurostat database was deemed the most suitable to study our research questions as it provides online retail data for 23 European countries (20 countries of the European Union, plus Norway, UK, and Turkey, covering thereby all major economies from Europe). This sample offers a rich variety of pandemic-related contexts: each of these countries was hit by the pandemic to a different extent and the reaction of authorities was also fairly diverse ( Hale et al., 2021 ). Fig. 2 illustrates the evolution of the ΔOnline_retail variable in these countries.

Monthly changes in online retail turnover during the pandemic in the countries investigated.

To investigate this volatile evolution, two novel measures are used as explanatory variables that have been introduced recently as a response to the need to track social phenomena more frequently and more precisely during the pandemic.

The first variable is a proxy of changes in general customer behavior: population mobility . Shankar et al. (2021) argue that during a period characterized by dramatic and frequent changes in shopping behaviors, high-frequency, mobile GPS data can offer better information for retailers. Therefore, we integrate into our analysis the mobility data provided by Google® through their Community Mobility Reports ( Google, 2021 ), comprising several types of mobilities grouped by the destination/location of the mobility. Based on Beckers et al. (2021) who argue that COVID-19 has temporarily put an end to hypermobility cutting short consumers’ physical range around their homes, we select the residential component ( ΔResidential ) from the different forms of mobility, arguing that the changes in residential mobility (i.e., amount of time spent at home) could be the strongest component to explain changes in online shopping. Given that there might be some time needed for online shopping behavior to adjust to changes in mobility, the one-month lagged version of the variable is also used in our model ( ΔResidential(-1) ).

The second explanatory variable incorporated in our analysis is related to government restrictions . We use data from the Oxford COVID-19 Government Response Tracker, more precisely the values of the COVID-19 Stringency Index which aggregates the stringency of lockdown-type governmental measures, such as school closures, travel restrictions, bans on public gatherings, workplace closures, etc. ( Hale et al., 2021 ). This represents the most suitable proxy to measure the type of regulations connected by previous literature to online channel use during the pandemic ( Table 1 ). The index provides a multi-country panel of daily frequency, measured as a percentage value; 100% representing the highest level of stringency. To match the frequency of the dependent variable, the monthly change of the index is computed as explanatory variable ( ΔGovernment_stringency ). The one-month lagged variant is also introduced in the analysis ( ΔGovernment_stringency(-1) ).

Beside the two novel explanatory variables generated during the pandemic, we integrate several control variables in our analysis. These variables assess the income and purchasing power of the population (GDP/capita and unemployment level in each country), the level of urbanization (density of the population in each country), the level of education (percentage of the population attending tertiary education), the pervasiveness of online channels (Internet penetration), and the actual pervasiveness of online shopping (Online retail share in the retail sector) ( Hortaçsu and Syverson, 2015 ). Data for all countries analyzed are retrieved from the Eurostat database. The unemployment variable has a monthly frequency ( Δ Unemployment ), while the other variables ( GDP/capita, Internet penetration, Tertiary education, Population density, Online retail share ) change on a yearly basis. Descriptive statistics for the monthly variables are provided in Table 2 . The correlation matrix is included in Appendix A.

Descriptive statistics of the main variables included in the short-term analysis.

3.2. Data used in long-term analysis (RQ2)

To evaluate the trend-shifting potential of the pandemic in the online retail sector, the same retail trade data is used as for the short-term analysis, covering however a longer period of time between Jan 2000 and Jan 2022 ( Online_Retail ). To offer an overview of the long-term evolution of our focal variable, we present a boxplot containing data for all countries aggregated to annual averages, normalized on a 0–100 scale ( Fig. 3 , left). Primary visual inspection suggests that two periods can be distinguished in terms of the dynamism of the sector (2000–2010 characterized by slower growth pace versus 2011–2021 showing stronger momentum), while the relatively higher values of the last two boxplots indicate that it is beneficial to investigate whether the pandemic has induced a level shift into the evolution of online retail.

Long-term evolution of online retail turnover (left) and online retail market share (right) in the countries investigated (normalized: min = 0, max = 100).

Furthermore, to assess whether the online retail sector could exploit the window of opportunity opened by the pandemic, we compute another variable as a proxy measuring the share of online retail in total retail sales. For this purpose, we calculate the ratio between the indices of deflated turnover of online retail and the “Retail trade, except of motor vehicles and motorcycles” sector, this latter being a proxy for total retail sales ( Online_Retail_Ratio ≈ Online_Retail/Total_Retail ) ( Fig. 3 , right). The ratio approach is also consistent with theory (symbiotic technologies: Geels, 2002 ) and previous research ( Hortaçsu and Syverson, 2015 ).

4. Analysis and results

4.1. short-term analysis (rq1), 4.1.1. panel regression analysis.

To illuminate the impact of mobility and government restrictions on the monthly evolution of online sales, we have elected to implement a panel regression model. We have performed three random-effects and three cross-section fixed-effects panel regressions. We opted for the panel specification because it enables us to harness the rich structure of our data and to account for the unobserved heterogeneity present in the data. We perform 2 × 3 = 6 regressions because of the different methodology (fixed vs. random effects), and the 3 combinations resulting from including only the government stringency variables, only the residential mobility variables, and both. Five control variables were nearly collinear in the fixed effects case; therefore Table 3 presents only the estimates for these variables in the random effects case. Our main specification is the following:

where C i j t and β ( C ) j are the independent variables and their coefficients, i is the index of countries, t of time, and j of the equation variables.

Regression models.

Notes: t-values in parentheses; *significant at 0.05; **significant at 0.01.

Results of the fixed effects specifications of our panel regression model (equations 1 to 3) indicate that our first variable of interest, residential mobility, and its one-period lag, have a significant impact on the monthly change in online retail sales, both variables having the expected positive sign. The same can be pointed out for the government stringency and lag variables. However, when we include both residential mobility and government stringency, only the first remains significant, due to high collinearity between the two explanatory variables. The results are similar in the random effects case (equations 4 to 6). The goodness-of-fit statistics (adjusted R-squared, F-statistic) are quite high for panel regressions, indicating that the explanatory variables introduced in the panel explain a large proportion of the variation of the monthly change in online retail sales.

Thus, results altogether indicate that both residential mobility and government stringency are significant predictors of online retail channel use: as residential mobility increases (i.e., people spend more time at home) and, alternatively, as government stringency increases (i.e., anti-COVID-19 measures become stricter) the use of online retail channels increases. Furthermore, the impact of all control variables is insignificant, meaning that mobility and government stringency indicators provide a better explanation for the variation of online retail sales during the pandemic than traditional variables that have been used to explain the evolution of the online retail sector in pre-pandemic periods.

4.1.2. Detailed analysis of short-term effects

While panel regression results show that both residential mobility and government stringency are good predictors of the evolution of online sales, relationships between variables are rarely perfectly linear. Therefore, we provide a more detailed analysis on the interplay between these variables. Fig. 4 illustrates the monthly evolution of online sales (vertical axis) together with the monthly percentage change in residential mobility (horizontal axis) for the entire period of the pandemic, each dot representing one country in one month.

Monthly evolution of online sales and residential mobility during the pandemic in the countries investigated.

Beside the general positive relationship between the two variables, the scatter plot also indicates that three different forces can be identified that shape the evolution of online retail sales during the pandemic. First, there are periods in which mobility is restricted more and more to residential areas, and consumers adapt by significantly increasing their monthly spending on online retail channels (as high as +30–50% during the first wave of the pandemic). This process is exactly what was expected during the pandemic: as the mobility range of people is restricted primarily to their homes, they turn to online retail channels more frequently. This process is termed the “lure-in” phase. Typical months during which the lure-in phase was dominant were Mar 2020, Apr 2020, Oct–Nov 2020, Nov 2021, and Jan 2022 ( Fig. 5 ).

Monthly evolution of online sales and residential mobility during different phases of the pandemic in the countries investigated.

However, it is also observable that when consumers are not confined to residential areas and start increasing their mobility outside their homes (i.e., residential mobility decreases), a decrease in online spending does not follow automatically, as people tend to continue to use, or even increase the usage of, online retail channels. Additionally, in many cases a large drop in residential mobility is paired with no significant change in online retail sales. These cases are labelled as the “lock-in” phase, which means that temporarily consumers remain users of online channels even if their mobility would allow them to use offline channels more intensively. Thus, mobility restrictions have an immediate (lure-in), but also a lagged (lock-in) impact on online retail channel use, in line with the significance of lagged variables in our panel regression model ( Table 3 ). The most typical months in which several European countries went through this lock-in phase were May 2020, Jun 2020, Feb 2021, Mar 2021 ( Fig. 5 ). This phase is not as consistent on a monthly basis as the lure-in phase, several countries experiencing a negative change in online channel use, concurrently with the decrease of residential mobility.

Lastly, there is also a “phase-out” period denoting cases where online retail use decreases, while time spent at home generally decreases. During these months a part of the former online shopping volume of customers is most probably replaced by (or allocated back to) offline channels. Furthermore, in some rare instances residential mobility has a slight increase, while consumers still decrease their online spending. Predominantly phase-out months include Jul 2020, May–Jul 2021, Dec 2021 ( Fig. 5 ).

The same three phases can be observed if the residential mobility indicator on the vertical axis is replaced by the government stringency index ( Fig. 6 , Fig. 7 ). In summary, there is a clear lure-in phase which was noticeable especially during the beginning of the first and second wave of the pandemic (Mar–Apr, 2020; Oct–Nov 2020): sudden drops in mobility and severe governmental restrictions clearly prompt customers to shop online. This effect has some “stickiness” (lock-in phase) because as governmental restrictions are eased, certain customers continue to use (or even increase the use of) online retail channels. Nevertheless, after a relatively short period the lock-in effect fades and customers drop their online shopping volume significantly (phase-out), countervailing to some extent the argument of the pandemic-induced upward boost of the online retail sector. Thus, while illuminating in other respects, this analysis, in itself, is unhelpful regarding the longer-term implications of the pandemic for the online retail sector. The next section aims to address this deficiency.

Monthly evolution of online sales and government stringency during the pandemic in the countries investigated.

Monthly evolution of online sales and government stringency during different phases of the pandemic in the countries investigated.

4.2. Long-term analysis (RQ2)

To investigate the potential trend-shifting impact of the pandemic in the online retail sector, a two-step approach is applied. First, to establish a basis for comparison, we analyze the 20-years trend of the sector without considering the specific effect of the pandemic. Second, based on the long-term trend established, we focus on the period of the pandemic, and use outlier detection methods to estimate whether the pandemic has induced a level shift in the long-term trend of the sector.

4.2.1. Long-term trend analysis

Online retail sales and online retail market shares show an increasing tendency during the last 20+ years ( Fig. 3 ). While the retail sector as a whole had a slight increasing tendency during this period, the average annual growth rate of the online retail sector was clearly higher. This difference is most visible during the last ten years when the online retail sector has been constantly on an increasing trajectory, thereby raising its market share within the total retail sector. Thus, the online retail sector has been benefitting from continuous market share gains with a relatively lower growth pace in the early period (2001–2010), and with rapid increases in the last period (2011–2021). These differences are illustrated in Fig. 8 .

Average annual growth rates in the retail sector in European countries (%).

Next, we use unit root tests to statistically demonstrate that there is an underlying long-term growth trend in the data ( Chatfield and Xing, 2019 ), both in terms of monthly online retail turnover ( Online_Retail ) and in terms of online retail market share ( Online_Retail_Ratio ). Applying the most widely used Augmented Dickey-Fuller (ADF) test, we aim to show that there is a systematic, persistent stochastic trend in the time series (i.e., an upward tendency in our case). Unit root test results confirm that in most of the countries investigated the null hypothesis of one unit root cannot be rejected: the p-values are above 0.05 in 23 cases out of 24 in case of the Online_Retail variable and in 21 cases out of 24 for Online_Retail_Ratio . Thus, for the vast majority of countries neither Online_Retail , nor Online_Retail_Ratio is stationary, indicating that there is an (upward) long-term stochastic trend in the time series. Furthermore, unit root test results also imply that any positive or negative shock (such as the pandemic) during the period investigated has a persistent effect on the trend. Nevertheless, further investigation is needed to determine whether this shock applies for the pandemic period as well.

4.2.2. Outlier detection during the pandemic

Outlier detection is used to determine whether the pandemic has caused a level shift in the Online_Retail , and especially in the Online_Retail_Ratio time series. For this purpose, we use ARIMA 1 models with specific dummy regressors on both time series, implemented in JDemetra+ which is a proprietary software developed by the National Bank of Belgium in cooperation with the Deutsche Bundesbank and Eurostat. The software has been officially recommended since 2015 to the members of the European Statistical System and the European System of Central Banks as a tool for seasonal adjustment and other connected time series issues, such as outlier detection. In general, outliers are represented by abrupt changes in a time series caused by unexpected natural or socioeconomic effects, such as the pandemic. Three main types of outliers can be identified ( Fig. 9 ): (a) additive outlier (AO), which changes the time series for one period only, returning to the original trend afterwards, (b) level shift (LS) that causes a permanent (upward or downward) change in the level of a time series, and (c) transitory change (TC) whose effect of changing the time series is faded out over a limited number of periods ( IMF, 2018 ). Here, we specifically look for LS type outliers: a positive LS would suggest that online retail turnover and its market share registered a sudden increase during the pandemic, and that therefore the pandemic has accelerated the underlying growth trend of online retail.

Level shift versus other outlier types (source: IMF, 2018 ).

JDemetra+ uses the traditional TRAMO 2 methodology ( Gómez and Maravall, 1996 ; Findley et al., 2017 ) where TRAMO is designed to perform outlier detection as well. 3 Although this is a widely used framework in economics and connected disciplines, its applications in retailing are quite scarce which offers us the possibility to shed additional light on the effect of the pandemic on the online retail sector. In particular, TRAMO uses regression models with ARIMA errors as follows:

where z t is the original data series, β = ( β 1 , … β n ) is a vector of regression coefficients, y t = ( y 1 t , … y n t ) represents n regression variables (in our case LS, AO and TC outliers), while x t is the disturbance that follows the general ARIMA process.

Using the TRAMO method, we analyze the full Jan 2000–Jan 2022 time period for outliers in each country involved in the analysis, complemented by the aggregated time series on the EU-27 level. Both Online_Retail and Online_Retail_Ratio time series were analyzed for all three types of outliers. However, in light of RQ2, only LS type outliers are listed in Table 4 that were identified during 2020. It should be noted that 2021 LS outliers are not (yet) taken into consideration here because they are situated at the end of our time series data (i.e., further data is needed by TRAMO to determine whether a 2021 LS will remain significant and persist in the long run). In contrast, LS outliers in 2020 have already proven that they induced a persistent upward shock into the long-term trend of the online retail sector. Table 4 lists all significant level shifts (p < .05) detected during 2020. Full results are presented in Appendix B .

Level shift (LS) detection during the pandemic.

The results of LS detection indicate that at the level of the EU-27, as well as in most of the countries investigated there was at least one positive LS in the online retail trend during the first year of the pandemic. This strongly suggests that COVID-19 has induced a boost both to online retail turnover and to its market share, supporting the window of opportunity concept. Out of the 23 countries analyzed, only 9 where had no significant LS. However, these cases represent smaller European countries, the largest ones (Germany, France, Italy, Spain, UK) all experiencing positive significant LSs. Furthermore, some of the countries (Italy, Lithuania, Norway) experienced multiple significant LSs during 2020 which further strengthens our conclusion related to the long-term implications of the pandemic. While there are two anomalous negative LSs in the Online_Retail_Ratio as well ( Table 4 ), we suggest that these do not contradict our results, as these are all overcompensated by multiple positive LSs in the same countries (Italy and Norway), the magnitude of which is significantly higher than that of the negative LSs. Nevertheless, these negative LSs could be a sign of a significant “phase-out” effect, as discussed in the short-term analysis.

5. Summary and discussion

Two important gaps were addressed in this paper: (RQ1) how can factors related to consumer behavior (mobility) and regulations (government stringency) explain the volatile evolution of online retail sales during the pandemic, and (RQ2) what long-term trend-shifting effects can be identified during the pandemic in the evolution trajectory of online retail.

First, our results confirm that the two indicators proposed to estimate changes in consumer behavior ( Residential mobility ) and in government regulations ( Government stringency ) can significantly explain the hectic short-term evolution of the online retail sector during the pandemic. Released for the first time during the pandemic, these two indicators are significantly above and beyond the explanatory power of traditional variables used to predict online channel use in pre-pandemic periods. The more people are confined to residential areas, and the stricter government restrictions are, the more customers turn to online channels. These results offer empirical support to previous studies that proposed that changes in mobility ( Shankar et al., 2021 ) and pandemic-related government regulations ( Hwang et al., 2020 ) could provide a better measure to estimate changes in online sales.

Second, using these newly introduced variables, our study goes beyond demonstrating the simple linear relationship between these variables and online retail turnover, to describe in more detail how online shopping habits change during the pandemic. This is a novel approach compared to existing studies that simply argue that the pandemic is linked to the increased use of online channels (e.g., Chang and Meyerhoefer, 2021 ; Hwang et al., 2020 ; Eger et al., 2021 ). Using government stringency and mobility data, we offer a more nuanced understanding of how online shopping behavior evolves during the different stages of the pandemic, an issue currently hotly debated in the literature ( Kirk and Rifkin, 2020 ; Guthrie et al., 2021 ; Schleper et al., 2021 ). Three different phases are distinguished in this paper: (1) a lure-in phase; (2) a temporary lock-in phase; and (3) a phase-out period. Furthermore, the same phases seem to repeat during different waves of the pandemic, starting with a strong lure-in phase, followed by a mix of lock-in and phase-out periods.

Third, using advanced outlier detection methods, we show that the faster growth trend that characterized online retail in the past decade has experienced a new positive level shift during 2020 in most of the countries investigated. In only a couple of months during the pandemic, online retail has gained extra market share against offline retail that in normal circumstances would have probably taken several years. Thus, our empirical findings confirm the predictions of some researchers (e.g., Chang and Meyerhoefer, 2021 ; Tran, 2021 ), and actively address the questions posed by other researchers (e.g., Sheth, 2020 ; Guthrie et al., 2021 ), by establishing that the pandemic has indeed induced a persistent upward shift into the growth trajectory of online retail. These level shifts were especially visible in the larger economies of Europe. Thus, our results are concordant with several other studies that suggest that many firms managed to quickly overcome infrastructural challenges and build up the necessary online capacities ( Guo et al., 2020 ; Beckers et al., 2021 ; Reardon et al., 2021 ), while customers will continue to use online retail channels more intensively in post-lockdown periods as well ( Hobbs, 2020 ; Eger et al., 2021 ; Hall et al., 2021 ). Even if some customers return to traditional shopping channels ( Hobbs, 2020 ; Sheth, 2020 ), our results indicate that for a large segment of customers the pandemic-induced shock outweighs the potential phase-out effect, shifting their long-term orientation towards online channels.

6. Conclusion

This paper analyzed short-term drivers (RQ1) and long-term implications of the pandemic (RQ2) in the online retail sector, relying on the MLP’s socio-technical approach as a theoretical lens. COVID-19 is operationalized within the MLP as an exogeneous landscape event that induced a shock on the regime level. This shock opened a window of opportunity for online retail to exponentially grow and significantly increase its share against traditional retail channels.

6.1. Theoretical implications

Our research shows that during a window of opportunity created by a landscape event, forces within the socio-technical regime that shape the long-term trajectory of a technology change radically. Geels and Schot (2007) argue that strong landscape pressures (such as a pandemic) destabilize actual socio-technical regimes creating tensions that open windows of opportunity for technologies to emerge. Our short-term analysis related to RQ1 offers additional insights into how these regime tensions function. Panel regression results indicate that during unstable periods (when windows of opportunity are created by landscape pressures), certain sub-regimes take over the force that shapes technological transitions, while other sub-regimes become negligible. In our study, the policy regime (strict government restrictions) and the user preferences and market regime (reorientation of shopping behaviors due to reduced mobility) were responsible for creating the tension on the regime-level. Conversely, other sub-regimes on the same level, such as technological regimes (e.g., technical infrastructure used in online retail), science regime (e.g., technical knowledge used to operate online transactions), and socio-cultural regimes (e.g., distrust of certain segments of the population in online retail), had no significant impact on the way online retail was evolving. Thus, we propose that windows of opportunity are created when one or more particular regimes exert pressures that take over the place of other regimes in creating the forces that shape technological transitions. When a window of opportunity is open, these new forces remain dominant and might even alter other regimes.

Second, our long-term analysis suggests that COVID-19 can be regarded as a shock-type landscape development that creates tension in the current socio-technical regime to create a window of opportunity for online retail. Results of our long-term analysis suggest that the quasi-stable socio-technical regime of the last decades enabled a gradual and constant growth of online retail in Europe, attaining continuously increasing market shares throughout the years. However, as the pandemic generated a window of opportunity for this sector, online retail was able to capitalize on this opportunity in most countries, receiving a significant boost to its previous growth tendency.

Third, as a more general research implication for retail, our study demonstrates that high-frequency indicators that emerged during the pandemic, such as data on population mobility and on government stringency can be used to better assess fundamental socio-economic processes during crises. These two types of indicators provide a more complex, real-time assessment of ongoing socio-economic processes, making them more suitable to make predictions or explain phenomena in a volatile context.

6.2. Practical implications

Through demonstrating that mobility and government stringency has a positive impact on the evolution of online sales, we offer an important tool to retail practitioners to monitor and anticipate potential large variations in online demand. While mobile GPS data has already been used to track retail store traffic, our analysis suggests that tracking customer movements outside brick-and-mortar stores can also provide an anchor during volatile times. Such high-frequency, near-real-time data could become the primary input for managers to keep up with sudden pandemic-related developments, and potentially with post-pandemic shopping behavior changes as well.

Online retailers that have already capitalized on this pandemic should also take into consideration that a sudden pandemic-related growth in sales could be followed by a temporary lock-in phase. However, retailers should continue to work on keeping (newly acquired) customers, as a phase-out period might rapidly occur. Conversely, our long-term analysis, suggests that actors in the online retail sector should expect that, on average, the phase-out effect is outweighed by the pandemic-induced boost in online sales, creating much potential on the long-run for online retailers to capture the benefits of the positive level shift in the growth trajectory of the sector.

6.3. Limitations and further research

A first set of limitations is related to the nature of data employed in our study. While Eurostat provides the most reliable macroeconomic data, comparable across countries, on the evolution of the (online) retail sector, aspects of the data were not ideal. Several countries had missing data on the most recent values of the online retail turnover index, and some European countries (e.g., Switzerland) could not be involved in our study at all. While all largest retail markets have been included in our sample, results of the study can nevertheless not be universally generalized beyond the 23 countries involved in the analysis.

In respect of GPS-based mobility and government stringency data, we have shown that these variables are suitable to explain the large variations in online retail sales during the pandemic. However, whether and to what extent these data can be used to keep up with developments in the online retail sector beyond the pandemic remains unknown but represents a promising direction for future research.

Another set of limitations stems from the results described in this paper. While our outlier detection could empirically demonstrate the pandemic-induced level shift in the long-term evolution of the online sector, statistically significant shifts were not observed in all the countries investigated. It remains an important future research avenue to explain why some countries, including the largest European economies, experienced level shifts during the pandemic, while others have not.

Lastly, this paper focused on the evolution of the online retail sector, explaining its volatile evolution during the pandemic and demonstrating how the sector could take advantage of the window of opportunity created by COVID-19. Our results could provide a starting point for investigating other technologies and solutions, such as video conferencing, home delivery or VR-solutions, to evaluate whether and to what extent they have capitalized on pandemic-induced opportunities, thereby shaping how the “ new normal ” might look like in a post-pandemic world.

Acknowledgement

This research was partially supported by the PN-III-P1-1.1-TE-2019-1773 research project for young independent research teams funded by UEFISCDI Romania.

1 Autoregressive Integrated Moving Average.

2 Time series Regression with ARIMA noise, Missing values and Outliers.

3 A comprehensive description of the procedure and its technical implementation in JDemetra+ is provided by Eurostat’s website .

Appendix A. – Correlation matrix

*significant at the p < .001 level.

Appendix B. Complete results of outlier detection

Outlier detection with TRAMO in the Online_Retail time series (Jan 2000–Jan 2022)

Content of cells: (a) type of outlier: LS – level shift, TC – transitory change, AO – Additive outlier; (b) month of occurrence in parentheses; (c) magnitude of outlier [t-value].

Outlier detection with TRAMO in the Online_Retail_Ratio time series (Jan 2000–Jan 2022)

Data availability

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A comprehensive review on the role of online media in sustainable business development and decision making

• Published: 09 June 2022
• Volume 26 , pages 10789–10803, ( 2022 )

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• Haiyu He 1  

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People are using platforms of social media like Twitter, Facebook, or Instagram as a regular way to reach very much targeted and new prospective customers. The role of online media is to facilitate the opportunity in making a connection with followers and fans every time they log in. Online media keep social posts informative and sometimes entertaining which the followers' glade to see the contents in their feeds. Online media facilitate with low commitment and easy way to prospective consumers for expressing attentiveness in the business you do for your services or product. Online media can offer a number of marketing opportunities for businesses of all dimensions. These opportunities include promotion of the brand, communication to the consumer about service or product, attracting customers, and building strong connections with available consumers. Apart from this, several other benefits can be gained from the business of online media such as the ability to target, broad reach, fast, personal, low cost or free, and easy to use. An overview of the existing approaches, tools, and techniques to present is the dire need of the modern business in order to develop novel ways and to present the business of services or products in an effective and efficient way. Therefore, this study has presented a comprehensive review of the existing literature associated with the techniques, tools, and approaches used for business with the help of online media and to decision making. Based on this study, new mechanisms will be provided for effective business.

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1 Introduction

Globally, around half of the world's population is using platforms of social media like Twitter, Facebook, or Instagram as a regular way to reach highly targeted and new prospective customers. Online media facilitate the opportunity to make a connection with followers and fans every time they log in. online media suggest low commitment and easy wat to prospective consumers for expressing their interest in the business you are doing for your products. Research works have been carried out, and various approaches were presented. Ibrahim et al. ( 2017 ) have proposed to identify the impact of online dealers’ interaction with online brand communities: at first, by using the collected tweets displayed on Twitter to analyze the whole trends of various brands and the pattern of interaction between end user and companies; after this, they studied that how various types of interaction affect user views. Analysis shows that views which subordinate with trademark appearance, opinion, and consumer package of the online sellers have affected by interaction. The level, distance, category, and arrogance of sellers' interaction with social media operators have an important influence on their views and also derived several important managerial and practical implications from these study analyses. Ahmad et al. ( 2018 ) have proposed a study to fill the space by viewing the implementation of social media among minor and intermediate companies in the Middle East area, precisely, in the UAE. Furthermore, this work examines the embracing existences over the word of mouth and social existence concept using thorough focus-group private consultations with businesspersons of companies. An additional important role condensed by the work is its inspection of social media implementation in meanings of the industry performance of companies.

Research has presented that social media plays a vital role in entrepreneurship (Fischer and Reuber 2011 ). It has been presented inductive, theory-building processes to develop prepositions concerning when businesspersons adopt Twitter so how effectuation methods are impacted. High levels of Twitter-based engagement can tend to effective churn and also theorize that there is one aspect, observed time affordability, that expects the level of social engagement by the use of Twitter. In addition, endorse two elements which are community orientation and community norm adherence that restrained the significances of social engagement using Twitter. Odoom et al. ( 2017 ) have proposed a study to develop understanding and provide prolonged acceptance of the inspirations and overall performance compensations of social media gathered via SMEs with an empirical observation from a rising economic system. The assumption from the research establishes that the interdependencies of social media inspirations, as well as sound effects of social media usage, are positive but rough across product-based and service-based SMEs. Burton and Soboleva ( 2011 ) have proposed the study that although speedy progress of Twitter is in managerial use, theoretical or empirical research studies show that how various firms use Twitter. It is a proposed study to analyze and associate the use of Twitter in 12 accounts held by six firms in the USA and Australia, which is a representation on present models of cooperative communications. This study is based on an examination of an unsystematic sample of tweets sent by each account. The results show various methods in which the cooperating abilities of Twitter can be used to converse with customers.

Wamba and Carter ( 2013 ) have proposed a study to create emerging writings on social trade, social media, and the distribution of modernization to recognize the character that organizational, supervisory, and ecological features of SMEs show in the implementation of Twitter. For testing the prototype, arrange an investigation to four hundred fifty-three SME managers from the US, UK, Australia, and India. The outcomes show that businesses' ingeniousness, phase, and topographical position have a significant influence on adopting Twitter via SMEs. Gilbert ( 2012 ) has proposed to discover that how a tie strength model established for one social medium gets used to another. Precisely take a Twitter application which kept a facebook strength model on the basis of its design and estimated tie strength for more than 200,000 relationships from people in 52 countries and to keep eyes on the mapping of relational features of Facebook to features in Twitter. This initial evidence shows that significant relational properties may be visible in the same way throughout various social media, and a result, that permits new social media sites to develop interactive findings from old ones. Pourkhani et al. ( 2019 ) have proposed a study to inspect the rank and growth of technical educations on the role of social media in businesses and applied scientific methods by using library techniques and scientometrics displays. Technical services in the discipline of social media claim in a commercial from 2005 to the last of January 2019, the trends and achievements of this field by using the bibliometric library. For the better understanding in this area, Business Horizons Magazine published 1269 articles and 73 articles journals on the topic of social media usage.

The current study has provided a complete evaluation of the available literature related to the methodologies, tools, and approaches utilized for business and decision making with the use of online media. New business mechanisms will be developed based on the findings of this investigation.

The paper is structured as follows: Sect.  2 briefly discusses the online media and business transformation. The detail associated with the factors influencing adoption of online media is given in Sect.  3 . Section  4 shows the organizational communications on social media. Analysis of the role of online media in business is given in Sect.  5 . The paper is concluded in Sect.  6 .

2 Online media and business transformation

Online media are offering a number of opportunities for marketing and businesses of all dimensions. Aral et al. ( 2013 ) have proposed that social media are the main technique through which people connect and advertised, etc. Information technology has an effective impact on businesses. This problem became considered to encourage progressive inquiries of the connection between social media and commercial enterprise alteration. The agenda of the research is to accept the connections among social media, organizations, and the public. We position this study encompassing the issue within this study charter and find an area where additional research is desired. Attewell ( 1992 ) has proposed a study to develop an alternative model that emphasizes the importance of management learning as potential roadblocks to modernization. Companies delay internal installation of challenging technology as they wait for sufficient methodological knowledge to adequately implement and operate it. The effectiveness of this strategy is demonstrated by a pragmatic education of the diffusion of entrepreneurship computing in the USA, which includes broadcasting review and ethnographic data on the explosion of business computing, the learning methods and skills required, and the changing institutional observes that aided diffusion. Michaelidou et al. ( 2011 ) have proposed that many tools are used to support business-to-business brands including communicative technologies. This research addresses the space by keeping eye on business-to-business small-medium enterprises and their social networking usage. Results from a mail review show that over a quarter of commerce-to-commerce SMEs in the UK are used social networking sites to get trademark goals and attract new consumers. Practically half of the small-medium enterprises that presently used social networking sites have shown their aim to raise the marketing and underlining the importance of social networking sites in business to the business framework.

Dijkmans et al. ( 2015 ) have proposed a study to investigate that whether and when a business's online actions to get involved customers are valuable for business status. The social media actions in turn are positively linked to business status, specifically among non-consumers, and also converse the implications of the outcomes for social media guidelines in the travel and tourism industry. Gruber et al. ( 2015 ) have proposed a study to keep concentrations on disaster supervision and headship by officials, panels, organizations and put on investigation flexibility, authority, and sense-making in the study of the overthrowing and successive arrival of topmost management by the panel of executives. A group of references for headship and disaster supervision in the modern corporate situation by viewing in what way a disaster is caused to move by media is provided by this case study. Twitter is examined by way of reason of actual bulletin and material, which have an important effect on firms and their approaches. Panagiotopoulos et al. ( 2011 ) have proposed to analyze the vibrant aspects of cooperation in the framework of 2011 insurrections in the UK. In August 2011, cities of London and further places in the UK sufftered from widespread sickness as well as lost many lives. Next the riots Twitter accounts elaborate that in what way native establishments make an attempt to condense the possessions of the riots and upkeep public regaining. Usage of Twitter's casual and speedy appraise topographies and created a diversity of instructional and tortious mails with pure calls for the offline or online act. In roundabout belonging, residents were not simply prepared by native experts, but native consultants also dynamically upheld movements started by citizens.

Graham et al. ( 2015 ) have proposed to analyze the usage of social media in local government and unmapped framework. It especially states the implementation and social media tools used for crisis interaction and also part in handling a disaster. Outcomes showed the usage of social media but did not show the number of tools used and absolutely linked with local city officials' valuations of their skill to manage a difficult condition and also their total valuations of the strength of their responses. Conclusions of implications and significance have conversed. Tess ( 2013 ) has proposed that social media is no more seeming than at the university. Higher education sets rise social media technology to facilitate and improve instructions in addition to encouraging active learning for students. To focus combination of social media as an educational tool is claimed by numerous scholars. Empirical evidence, though, has padded in the supportive claim. This study condenses the studious writings and in addition reviews the conclusions of realistic surveys.

Okazaki et al. ( 2020 ) have proposed that using social media the commercial societal accountability (CSR) framework somewhere online CSR negotiations usage as trademarks communicates by means of customers. Eight trademarks' arithmetical CSR interact on Twitter, and these recommended negotiations are existing and are infrequently measure of the procedure with supreme communication among customers are analyzed by study 1. The brands of CSR-related twitters were gratified and catched that maximum is not related to CSR and furthermore is mainly 1-side by study 2 analysis. Hence it is suggested that media conversations must contain, indications of separable customers, viewers' exact and applicable SMS gratified, and also have chances for customers to reconceive worth using the related trademarks.

3 Factors influencing adoption of online media

Several advantages of the internet media company include the capacity to target, broad reach, speed, personalization, cheap cost or free, and ease of use. Dahnil et al. ( 2014 ) have proposed the implementation and usage of social media in marketing as new interaction tools by companies and small-medium enterprises rise globally which gives special chances to SMEs and has an impact on marketing scientists. The work of this study analyzed the educational literature on features that carried social media marketing implementation in SMEs and businesses. It offers a beneficial means to examine the types of study that needs to be followed to make further study growth in a linked zone of social media selling. Prabantoro and Hariyanto ( 2018 ) have proposed a study to identify the famous social media platform used as a medium for interaction nowadays like Twitter, and many other sites are used for marketing interaction media in organizations. Seven tasks of social media existence, distribution, association, uniqueness, discussion, status, and set are covering the framework of this analysis. Study directed taking place SMEs in Vill. of Rawamangun, Distt. Pulogadung, East Jakarta. Many enterprises in Rawamangun in their firms ' marketing interaction actions are using social media. The first choice to social media of SMEs plus the causes for this one used as per media of commercial promoting interaction is the outcome of study analysis. Xiong et al. ( 2018 ) have proposed that the finest medium of communication is social media to form the mechanisms of today’s business toolkit. Business fraud can be identified by tapping into the wisdom of crowds through social media. This article overviews both benefits and restrictions of social media in spotting business scam by analyzing the outdated and social media for near scam event and also explains that how social media give rise to the level of related information quickly. Twitter can increase the quality of the company's awareness. Approaches were identified for administrators to use social media to advance their managerial understanding of administration.

Ndiege ( 2019 ) has proposed that the outcomes of tentative analysis using semistructured interviews with fifteen small-medium enterprises directors in Kenya are presented by this study. The conclusions advised that social media technology usefully supports SMEs to keep a better place strategically. However, in many scenarios, the implementation of such technologies has not been a workful schema because of exterior factors. The revision outcomes are not simply improved and accepting that in what way the SMEs in evolving states are implementing social media equipment as well as suggest an understanding of the policies that SMEs be able to hire to boost social media practices for best tactical standing. Kaya ( 2020 ) has proposed a study to fill the space of writings relevant to the usage of online media in the period of the COVID-19 virus. These investigation goals to reply to the question are that: Is the effect of online media usage altered from normal intervals? By the Cronbach's alpha value, consistency and rationality were calculated, and the value was 0.751 with the usage of a five-point Likert scale. The outcome shows that consumers are alert of fault bulletins and keep an eye on authorized sources. Swani et al. ( 2014 ) have proposed to inspect that how businesspersons used Twitter differently crosswise framework and forecast the key points probably to affect the SMS approaches used in each. Twitter interaction for Fortune Five Hundred Corporations examines in this study. Dealers in B2B and B2C setting display important alterations in their trademark along with retailing tactics to maintain the information searches.

Grant et al. ( 2018 ) have proposed a study to analyze whether stakeholder prospects of two main revelation channels communicate by way of a boss's interaction smartness to affect stockholder verdicts. This study underwrites to the disclosures and evolving literature on online media, and the writings inspecting in what way style structures of revelations affect stockholder verdicts. Study outcomes as well have real-world allegations for businesses as well as administrators emerging messaging tactics for fresh revelation channels such as Twitter. Parveen et al. ( 2015 ) have proposed a study to inspect many purposes of the usage of media along with its influence on a business presentation by using qualitative strategy. The work of research is to concentrate only on the social media administrators' opinions. The high-ranking managers of 6 companies that are using social media are interrogated through which authors catch that usage of social media is for many purposes in companies, like publicizing and campaign, labeling, information search, etc. The outcomes also showed that social media have a larger impact on the business presentation in terms of improvement in buyer relations as well as buyer service deeds. Georgescu et al. ( 2015 ) have proposed a study, that is, the fast growth in the usage of the net for interaction and messaging between persons or groups. The main work of this paper is to give a detailed assessment of the social media effect on organizations, on the basis of analyzing the related works in the discipline. The formation of virtual immediacies relies on the transmission of knowledge, making of optimistic network externalities, growth of company information wealth along with the social and ethical inferences relevant to social media.

4 Organizational communications on social media

Understanding interorganizational communication networks on online social media is a crucial step toward bettering collective action in the areas of hazard communication and public warning among multiple companies. Xiao et al. ( 2019 ) have proposed a study look at how companies interact and affect word of mouth regarding the corporate on Twitter. The airline industry has taken as study framework. Over Twitter, people post tweets about the service delays like flight delays, etc. It predicted that airlines with good corporate social responsibility (CSR) management get more positive WOM on Twitter. But also service delays reduce PWOM while increasing NWOM. Further it investigates to analyze the consequences of ecological, societal, and supremacy CSR on PWOM and NWOM. This examination has realistic implications in notifying organizations regarding the advantages of CSR commitment when it comes to communal judgment at some stage in service delays. Rajput et al. ( 2020 ) have proposed to charted and examine online managerial messaging systems built on exchanges between managerial employers on Twitter to identify employers' roles in the course of Hurricane Harvey. The propose studied the messaging systems and investigated three networks possessions to train the characters of societies in adversities. The first is during Harvey government organizational workers predominantly produce data and on the other hand nongovernmental organizational workers mostly distribute data; the second is during Harvey mostly consumers at the hubs of online administrative communiqué systems as of government administrations; and the third is restricted interaction between government and nongovernmental organizations consumers before and after Harvey. This paper proposals a pragmatic assessment of the character of different organizational workers in online communication links during adversities.

Liu et al. ( 2021 ) have proposed a study to make usage of large data in examining the influence of a superfluity trademark's social media publicizing undertakings on consumer meetings. This study analyzes the impact of concentrating on the show business, collaboration along customization magnitudes of a superfluity brands’ social media undertakings on consumer commitment with trademark-relavent social media content. This study examines 3.78 million Tweets as of the topmost fifteen superfluity marques per the largest amount of Twitter admirers. Outcomes have vital insinuations designed, carriage, and controlling of social media publicizing for superfluity trademarks to involve consumers per social media gratified. Mozas-Moral et al. ( 2016 ) have proposed to specify expanatory elements for success in online social networks. A fuzzy set qualitative examination approves human resource abilities, the strength of organizations social networks doings, gauging success by the skill to attract a large number of admirers. Pentina et al. ( 2013 ) have proposed to spread out trademark association concept to the framework of the Twitter. The writers examine the Twitter faith over consumers intentions to remain the usage of platform and to follow presented trademark on Twitter. The author authorizes the character of matching in character qualities among Twitter consumers and the Twitter marque in bring about belief in Twitter. Nisar and Whitehead ( 2016 ) have proposed to examine that how consumers faith can be done and sustained via social networking sites. In addition tested the relationships among trademarks, consumers, and social media. This article clearly offered the causes for engaging with online marques and analyzed consumer behavior and loyality. The premises tested indicate that brands and consumer satisfaction are both surely related to consumers’ social loyalty.

Molinillo et al. ( 2019 ) have proposed a study to examine the meeting of guests and residents with smart city marques via social media by putting on a digital content analysis technique. The key suggestion of this article is that these Spanish smart cities have substantial space to increase their social media use and improve their communication and trademarking. Larger weight is mandatory on delivering touching SMS, and greater precedence is essential to be set to industry and business occasion travelers and those staying friends and relatives. Uzunoğlu et al. ( 2017 ) have proposed to analyze that whether corporate social responsibility (CSR) relevant to Twitter, SMS from discrete sets, and spaces would have various effects on customer attitudinal and behavioral results. Research is conducted in two stages succeeding this objective containing exploratory and experimental stages. The outcomes indicate that CSR-relevant Twitter SMS on product/economicand ethical group has a greater impact on customers buying intention than SMS on product/purely economic group. Tiago and Verissimo ( 2014 ) have proposed to embrace the viewpoint of the organization to help an appreciative of digital marketing and use of social media plus its welfares and inhibitors. This study indicates on the basis of marketing managers’ investigation that organizations face interior and exterior forces to embrace a digital presence in social media platforms. This paper determines that how some organizations are already achieving just that. Ioanid and Scarlat ( 2017 ) have proposed to determine the aspects that impact organizations, businesspersons, and managers, into the usage of social networks and what welfares gained in case of using it. Thus, 8 items tested by the authors for Twitter and YouTube that even though have no usage by Romanian organizations and upheld by them to impact the online performance of the commerce in different parts with a minimum of 30 percent.

Lee and Hallak ( 2020 ) have proposed a study to embrace a mixed-ways methodology to analyze sightseeing businessperson’s performances in developing online and offline social wealth, and their consequence on company performance. The outcomes launch sightseeing businesspersons networking actions showing in three distinct formations which are: dynamic online teleworkers, personally teleworkers, and the fewer involved. Every individual alignment proved fluctuating effects on predictable commercial progress and presentation with esteems to amount of personnel, trades returns, and total revenue. Mergel ( 2016 ) has proposed to elaborate the interior decisions that are needy before the usage of advanced technologies to upkeep the tactical task of a government organization, in which negotiating and technological variation are combined into the company's standards functioning measures. Qualitative discussions per social media executives in the US central government and numerical technoscience of their online observes enlarge the present concept of social media assumption by totaling two different activities: tactical configuration and routinization. Arora et al. ( 2019 ) have proposed a study to prepare a tool for determining the consultant catalog transversely social media stages like Twitter, etc. Regression approaches are used for causal the effect on the customers are demonstrated. The highlighting artificial intelligence algorithms adding usual minimum squares (OLS), K-NN regression (KNN), support vector regression (SVR), and lasso prototypes are implemented to calculate a collective mark in relation to the consultant catalog. In addition, the ensemble of the four prototypes outcomes in the largest exactness of 93.7 percent was tracked by the KNN regression with 93.6 percent.

5 Analysis of the role of online media in business

The common online social media used for communication, interaction, entertainment, advertisement, and other purposes are Facebook, YouTube, Twitter, Coupon, blogs, coupon sites, location-based marketing sites, online photograph sharing services, and customer review sites. Phua et al. ( 2017 ) have proposed to analyze that customers used one social media platform out of four which are Facebook, Twitter, Instagram, or Snapchat for tracking trademark and impact on trademark-relevant results. The usage of Snapchat by consumers is mostly for time wasting or distributing harms, etc., while the Instagram consumers mostly used it for the following fashion and showing affection. Twitter consumers used it mostly for trademark public proof of identity and participation aim. Suggestions for coming exploration on SNS consumers’ objective-directed consumption activities were conversed. Study has proposed a study to suggest a prototypical to examine the character of social issues that impact on association excellence and societal business aim. A review steered on Facebook, a widespread social networking site, and PLS-SEM technique was functional to empirically check the offered prototypical. The analysis highpoints a new image of customers' manners in the social business period. Real-world inferences of the investigation also highpoint new technical variations in e-commerce platforms and offer new approaches to businesses to embrace these new technical developments (Hajli and Change 2014 ). Garrido-Moreno et al. ( 2018 ) have proposed that the social networking use and review sites, such as TripAdvisor, have turned into all persistent, and hotels are participating huge amounts of money in charming consumers through social media. To focus on the topic, centered on a trial of two hundred twenty-two Spanish hotels, this research observes the genuine effect of social media usage and displays the vital character displayed by societal CRM abilities in the progression of worth formation with these trappings. By construction on the resource-based philosophy, the planned prototype displays the trail among social media usage and organizational performance, in terms of productivity, auctions, and consumer retaining. Liu and Suh ( 2017 ) have proposed to examine content approach which is used to analyze the two hundred forty-three markers as of the top ten smart bloggers permitting to an organized coding pattern. The outcomes of the authors examine showing that supreme top elegance bloggers yet follow outdated prettiness and hierarchic standards; social media offer boundless chances for a female to begin their trademark themselves and let them to bazaar themselves in streak with the upsurge alertness of female’s privileges. On the basis of these conclusions, authors converse hypothetical and real-world suggestions for self-branding through consuming social media.

Roma and Aloini ( 2019 ) have proposed to expand the hypothetical context of consumer-generated content (UGC) measurements along with appraises indication that in what way trademark-relevant UGC features differ through social media. Authors' outcomes proposed the changes by destruction on pictorial and wealthier content, and the fresh styles have prepared Facebook and Twitter extra related to YouTube on assured trademark-relevant UGC measurements. Moreover, the variances by intensifying relevant to the real-time and omnipresent distribution of gradually wealthier content, the new styles have ideal the rise of other brand-relevant UGC features more conspicuously on Facebook and Twitter than on YouTube. Juntunen et al. ( 2020 ) have proposed to elaborate the world top B2B organizations content goals, approaches, and diplomacies on Twitter by structure in the hierarchy of effects approach. At first, incorporate B2B promotion and social media investigation on organizations’ content goals, approaches, and tactics and to analyze the presence of aims, approaches, and strategies in the greatest charming tweets ( N  = 365) of the worlds’ 10 top B2B trademarks, casing 5 businesses, in 2017. At last, quantitatively analyze the usage of varied aims and tactics varies among the most attractive tweets ( N  = 318) and minimum attractive tweets ( N  = 229) of the organizations in 2018. The analysis is an infrequent effort to mix the existent B2B promotion and social media study and associate the maximum as well as minimum appealing B2B social media.

Zu et al. ( 2019 ) have proposed a study to conclude that beginning social media accounts move the performance of a business, measure the impact of businesses change social media contribution strength over their performance, and recognize whether there is a nonlinear interaction among organization performance and social media input strength. The study inventively inspects the nonlinear effect of social media input strength over organization performance, whereas earlier work examined only their linear relationship. Additional involvement of the study is the surviving consumer-oriented fiction mostly used questionnaire data, while this study gathered Weibo data via data mining, building the research more complete and correct. van Zoonen et al. ( 2016 ) have proposed to deliver a typology for work-relevant Twitter usage on the basis of big-scale content investigation where N = 38,124 of sent tweets by four hundred thirty-three workers throughout various organizations. The authors found that work-relevant subjects were predominant in 36.5 percent of overall tweets. Additionally, Twitter improved the combination of private and expert life spheres, as workers frequently tweet about their job exterior steady job 60 min but also tweet on a private label at the job. Charoensukmongkol ( 2014 ) has proposed to analyze that how worker perception of a job place relevant to colleagues help, superintendent help, and work-relevant demands can measure the degree of connection few workers touch to the usage of social media at duty. This paper expands some results of the usage of social media at the job by investigating its linkage with work gratification, performance, and perceptive absorption. As a whole, the indication proposes that the usage of social media at the job may not essentially lead to negative work-relevant results. Misirlis and Vlachopoulou ( 2018 ) have proposed to present a charting literature assessment and a classification for study articles concerning social media metrics and analytics in publicizing. The analysis outcomes expose which is the maximum used subgroup for individual classification, trends, and tendencies. This assessment offers a base classification for scholars and an editable and uninterruptedly augmenting typology for additional investigation in the part. Agnihotri et al. ( 2017 ) have proposed that some lessons have sightseen how salesclerk service behaviors (SSB) and are improved via tools like trades-based equipment and social media of consumer relationship management (CRM). Furthermore, the research inspects the collaborative special effects of trades-based CRM equipment and social media over these performances. Outcomes show that trades-based CRM tools take an optimistic effect on SSBs.

Various common libraries were used to support the search process of the proposed study. These libraries include IEEE, ScienceDirect, Wiley Online, MDPI, and ACM. The overall search process results are shown in Fig.  1 .

Search results in overall libraries

The ACM library was considered for the search process, and the results were presented in different forms. Figure  2 describes the content types with total publications.

Content types

Publishers in the same library are shown in Fig.  3 .

The categories of all publications are given in Fig.  4 .

All publications

The media format in the same library was reviewed, and the details are given in Fig.  5 .

Media format

The IEEE library was used for the search process, and the details of the search process were shown in a different format. Figure  6 describes the content types in this library.

Conent types with publciaitons

The publications topics in this library are presented in Fig.  7 .

Publication topics

The conference locations of this library are depicted in Fig.  8 .

Conference locations with publications

The ScienceDirect library was considered an important part of the search process as this library published quality and peer-reviewed materials. In the same library, the article types with publications are shown in Fig.  9 .

Article types with publications

The years of publications with papers are shown in Fig.  10 .

Years of papers with publications

Publications titles with a total of papers are presented in Fig.  11 .

Papers titles with publications

The subject areas with a total of publications are shown in Fig.  12 .

Subject areas with publications

The MDPI library was searched for finding relevant information. Figure  13 describes the journal names with publications. This library was considered due to the reason that publishes the materials which are peer-reviewed.

Journal names with publications

The subjects' areas along with papers are shown in Fig.  14 . The figure descibes that more articles were published in the enviornment and earth sciences.

Subject areas and papers

The article types with papers are shown in Fig.  15 .

Article types with papers

Figure  16 describes the countries of publications with a total number of papers.

Publication countries with papers

The Wiley Online library was considered as part of the search process, and the details were presented in different forms. Figure  17 describes the article types with a total of papers in the given library.

Figure  18 shows the subject areas of the given library.

Subject areas of the search process in the given library

Figure  19 shows the publications published in.

Publications with the published in

6 Conclusion

Social media platforms such as Twitter, Facebook, or Instagram are used by people as a consistent way to reach targeted audiences and prospective customers. Online media keep social posts informative and sometimes entertaining, which gives the followers' glade to see the contents in their feeds. Online media's role is to produce ease and facilitate the opportunity to create a relationship with supporters and fans. Low commitment and an easy way to communicate are considered in online media to potential consumers for communicating attentiveness in the business of the services or products they do. A number of marketing opportunities are offered by online media for businesses of all sizes. The current study has presented an overview of the current industrial approaches, tools, and techniques of modern business in order to develop novel ways and present the business of services or products in an effective and efficient way. The current study included a variety of analyses, which were presented in a variety of formats. New mechanisms for effective business will be developed based on the findings of this study.

Data availability

Enquiries about data availability should be directed to the authors.

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He, H. A comprehensive review on the role of online media in sustainable business development and decision making. Soft Comput 26 , 10789–10803 (2022). https://doi.org/10.1007/s00500-022-06993-1

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WCU Stories

Business savvy student puts skills to use for ncur project.

 WCU Stories      April 9, 2024

Steven Hudspeth

By Julia Duvall

During his childhood, Western Carolina University student Steven Hudspeth, a junior from Gastonia, used to go on day trips with his grandmother and siblings to see the Blue Ridge Parkway from the Western North Carolina side.

He loved seeing the mountains each trip and kept those childhood memories close.

“We never went to the beach, it was always the mountains that we loved to go visit,” Hudspeth said.

Fast forward a few years later, Hudspeth knew that after graduating from Gaston Community College, he wanted to continue his education at WCU and be surrounded by the mountains from his childhood.

Having his school choice solidified, it was now time for Hudspeth to decide on a major and concentration. Always having an interest in business, he knew he wanted to help people achieve their financial goals, so he chose the financial planning concentration in WCU’s College of Business’ finance program.

“I was meeting with my adviser, Dr. (Gary) Curnutt, and he mentioned the National Conference on Undergraduate Research and all of the opportunities available to students,” Hudspeth said. “Initially, I was unsure if that was something I would want to do but learning more about NCUR and having Dr. Curnutt send me all of the information, I became very interested in submitting a research project.”

Hudspeth’s research paper, “The Association Between Online Payments Using Different Payment Types and Six Consumer Assessment Characteristics,” looks at the relationship between online payments using various types of payments, such as cash, checks, debit cards, credit cards, prepaid cards, money orders and bank account number payments during COVID-19, and six characteristics used to assess payment choice.  

“During COVID-19, there was a significant uptick in online payments due to the restrictions stemming from the pandemic, which shifted how businesses catered to their customers in person and online,” he said. “While researching, I ended up stumbling upon the 2020 Survey of Consumer Choice published by the Federal Reserve Bank of Atlanta,” Hudspeth said.

Hudspeth used that model for his study to see the association between online payments and the six consumer characteristics which are security, acceptance, getting and setting up, cost, convenience, and payment record.

“Collaborating on research with Steven has been incredibly rewarding,” Curnutt said. “Throughout our research, Steven has consistently demonstrated qualities that exemplify a great researcher. He possesses a profound intellectual curiosity, seeking to deepen his understanding of fundamental theories and their implications on model design, as well as the assumptions underlying our hypotheses.”

Preliminary results indicated a statistically significant association between payment records and cash, payment records and credit cards, security and prepaid cards, acceptance for payment and bank account number or routing number payments.

“After we ran the model characteristics some things lined up with our hypothesis,” Hudspeth said. “Essentially our hypothesis was based on perceived ease of use or perceived usefulness. The control variable for education level showed a significant statistical association between all education levels and all payment types.”

After working on this project, Hudspeth said he would like to continue his higher education journey in financial planning and conduct more research.

“I've thoroughly enjoyed working with Steven and I certainly look forward to future research collaborations,” Curnutt said.

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ZDNET's editorial team writes on behalf of you, our reader. Our goal is to deliver the most accurate information and the most knowledgeable advice possible in order to help you make smarter buying decisions on tech gear and a wide array of products and services. Our editors thoroughly review and fact-check every article to ensure that our content meets the highest standards. If we have made an error or published misleading information, we will correct or clarify the article. If you see inaccuracies in our content, please report the mistake via this form .

The best AI image generators to try right now

If you've ever searched Google high and low to find an image you needed to no avail, artificial intelligence (AI) may be able to help. 

With AI image generators, you can type in a prompt as detailed or vague as you'd like to fit an array of purposes and have the image you were thinking of instantly pop up on your screen. These tools can help with branding, social media content creation, and making invitations, flyers, business cards, and more.

Also: ChatGPT no longer requires a login, but you might want one anyway. Here's why

Even if you have no professional use for AI, don't worry -- the process is so fun that anyone can (and should) try it out.

OpenAI's DALL-E 2 made a huge splash because of its advanced capabilities as the first mainstream AI image generator. However, since its initial launch, there have been many developments. Other companies have released models that rival DALL-E 2, and OpenAI even released a more advanced model known as DALL-E 3 , discontinuing its predecessor. 

To help you discover which models are the best for different tasks, I put the image generators to the test by giving each tool the same prompt: "Two Yorkies sitting on a beach that is covered in snow". I also included screenshots to help you decide which is best. 

Also: DALL-E adds new ways to edit and create AI-generated images. Learn how to use it

While I found the best overall AI image generator is Image Creator from Microsoft Designer , due to its free-of-charge, high-quality results, other AI image generators perform better for specific needs. For the full roundup of the best AI image generators, keep reading. 

The best AI image generators of 2024

Image creator from microsoft designer (formerly bing image creator), best ai image generator overall.

• Powered by DALL-E 3
• Convenient to access
• Need a Microsoft account
• In preview stage

Image Creator from Microsoft Designer is powered by DALL-E 3, OpenAI's most advanced image-generating model. As a result, it produces the same quality results as DALL-E while remaining free to use as opposed to the $20 per month fee to use DALL-E. 

All you need to do to access the image generator is visit the Image Creator website and sign in with a Microsoft account. 

Another major perk about this AI generator is that you can also access it in the same place where you can access Microsoft's AI chatbot, Copilot (formerly Bing Chat) . 

This capability means that in addition to visiting Image Creator on its standalone site, you can ask it to generate images for you in Copilot. To render an image, all you have to do is conversationally ask Copilot to draw you any image you'd like. 

Also:   How to use Image Creator from Microsoft Designer (formerly Bing Image Creator)

This feature is so convenient because you can satisfy all your image-generating and AI-chatting needs in the same place for free. This combination facilitates tasks that could benefit from image and text generation, such as party planning, as you can ask the chatbot to generate themes for your party and then ask it to create images that follow the theme.

Image Creator from Microsoft Designer f eatures:  Powered by:  DALL-E 3 |  Access via:  Copilot, browser, mobile |  Output:  4 images per prompt |  P rice:  Free 

DALL-E 3 by OpenAI

Best ai image generator if you want to experience the inspiration.

• Not copyrighted
• Accurate depictions
• Confusing credits

OpenAI, the AI research company behind ChatGPT, launched DALL-E 2 in November 2022. The tool quickly became the most popular AI image generator on the market. However, after launching its most advanced image generator, DALL-E 3, OpenAI discontinued DALL-E 2. 

DALL-E 3 is even more capable than the original model, but this ability comes at a cost. To access DALL-E 3 you must be a ChatGPT Plus subscriber, and the membership costs $20 per month per user. You can access DALL-E 3 via ChatGPT or the ChatGPT app.

Using DALL-E 3 is very intuitive. Type in whatever prompt you'd like, specifying as much detail as necessary to bring your vision to life, and then DALL-E 3 will generate four images from your prompt. As you can see in the image at the top of the article, the renditions are high quality and very realistic.

OpenAI even recently added new ways to edit an image generated by the chatbot, including easy conversational text prompts and the ability to click on parts of the image you want to edit. 

Like with Copilot, you can chat and render your images on the same platform, making it convenient to work on projects that depend on image and text generation. If you don't want to shell out the money,  Image Creator by Designer  is a great alternative since it's free, uses DALL-E 3, and can be accessed via Copilot.

DALL-E 3 features: Powered by:  DALL-E 3 by OpenAI |  Access via:  ChatGPT website and app |  Output:  4 images per credit |  Price:  ChatGPT Plus subscription, $20 per month

ImageFX by Google

The best ai image generator for beginners.

• Easy-to-use
• High-quality results
• Expressive chips
• Need a Google account
• Strict guardrails can be limiting

Google's ImageFX was a dark horse, entering the AI image generator space much later than its competition, over a year after DALL-E 2 launched. However, the generator's performance seems to have been worth the wait. The image generator can produce high-quality, realistic outputs, even objects that are difficult to render, such as hands. 

Also: I just tried Google's ImageFX AI image generator, and I'm shocked at how good it is

The tool boasts a unique feature, expressive chips, that make it easier to refine your prompts or generate new ones via dropdowns, which highlight parts of your prompt and suggest different word changes to modify your output.

ImageFX also includes suggestions for the style you'd like your image rendered in, such as photorealistic, 35mm film, minimal, sketch, handmade, and more. This combination of features makes ImageFX the perfect for beginners who want to experiment. 

ImageFX from Google: Powered by:  Imagen 2  | Access via:  Website |  Output:  4 images |  Price:  free 

DreamStudio by Stability AI

Best ai image generator for customization.

• Accepts specific instruction
• Open source
• More entries for customization
• Paid credits
• Need to create an account

Stability AI created the massively popular, open-sourced, text-to-image generator, Stable Diffusion. Users can download the tool and use it at no cost. However, using this tool typically requires technical skill. 

Also :  How to use Stable Diffusion AI to create amazing images

To make the technology readily accessible to everyone (regardless of skill level), Stability AI created DreamStudio, which incorporates Stable Diffusion in a UI that is easy to understand and use. 

One of the standouts of the platform is that it includes many different entries for customization, including a "negative prompt" where you can delineate the specifics of what you'd like to avoid in the final image. You can also easily change the image ratio -- that's a key feature, as most AI image generators automatically deliver 1:1. 

DreamStudio features: Powered by:  SDXL 1.0 by Stability AI  | Access via:  Website |  Output:  1 image per 2 credits |  Price:  $1 per 100 credits |  Credits:  25 free credits when you open an account; buy purchase once you run out

Dream by WOMBO

Best ai image generator for your phone.

• Remix your own images
• Multiple templates
• One image per prompt
• Subscription cost for full access

This app took the first-place spot for the best overall app in Google Play's 2022 awards , and it has five stars on Apple's App Store with 141.6K ratings. With the app, you can create art and images with the simple input of a quick prompt. 

An added plus is this AI image generator allows you to pick different design styles such as realistic, expressionist, comic, abstract, fanatical, ink, and more. 

Also :  How to use Dream by WOMBO to generate artwork in any style

In addition to the app, the tool has a free desktop mobile version that is simple to use. If you want to take your use of the app to the next level, you can pay $90 per year or $10 per month.

Dream by WOMBO f eatures: Powered by:  WOMBO AI's machine-learning algorithm |  Access via:  Mobile and desktop versions |  Output:  1 image with a free version, 4 with a paid plan |  Price:  Free limited access

Best no-frills AI image generator

• Unlimited access
• Simple to use
• Longer wait
• Inconsistent images

Despite originally being named DALL-E mini, this AI image generator is NOT affiliated with OpenAI or DALL-E 2. Rather, it is an open-source alternative. However, the name DALL-E 2 mini is somewhat fitting as the tool does everything DALL-E 2 does, just with less precise renditions. 

Also :  How to use Craiyon AI (formerly known as DALL-E mini)

Unlike DALL-E 2, the outputs from Craiyon lack quality and take longer to render (approximately a minute). However, because you have unlimited prompts, you can continue to tweak the prompt until you get your exact vision. The site is also simple to use, making it perfect for someone wanting to experiment with AI image generators. It also generates six images, more than any other chatbot listed. 

Craiyon f eatures: Powered by:  Their model |  Access via :  Craiyon website  |  Output:  6 images per prompt |  Price:  Free, unlimited prompts 

Best AI image generator for highest quality photos

• Very high-quality outputs
• Discord community
• Monthly cost
• Confusing to set up

I often play around with AI image generators because they make it fun and easy to create digital artwork. Despite all my experiences with different AI generators, nothing could have prepared me for Midjourney -- in the best way. 

The output of the image was so crystal clear that I had a hard time believing it wasn't an actual picture that someone took of my prompt. This software is so good that it has produced award-winning art .

However, I think Midjourney isn't user-friendly and it confuses me. If you also need extra direction, check out our step-by-step how-to here: How to use Midjourney to generate amazing images and art .

Another problem with the tool is that you may not access it for free. When I tried to render images, I got this error message: "Due to extreme demand, we can't provide a free trial right now. Please subscribe to create images with Midjourney."

To show you the quality of renditions, I've included a close-up below from a previous time I tested the generator. The prompt was: "A baby Yorkie sitting on a comfy couch in front of the NYC skyline." 

Midjourney f eatures: Powered by:  Midjourney; utilizes Discord |  Access via:  Discord |  Output:  4 images per prompt |  Price:  Starts at $10/month

Adobe Firefly

Best ai image generator if you have a reference photo.

• Structure and Style Reference
• Commercial-safe
• Longer lag than other generators
• More specific prompts required

Adobe has been a leader in developing creative tools for creative and working professionals for decades. As a result, it's no surprise that its image generator is impressive. Accessing the generator is easy. Just visit the website and type the prompt of the image you'd like generated. 

Also: This new AI tool from Adobe makes generating the images you need even simpler

As you can see above, the images rendered of the Yorkies are high-quality, realistic, and detailed. Additionally, the biggest standout features of this chatbot are its Structure Reference and Style Reference features. 

Structure Reference lets users input an image they want the AI model to use as a template. The model then uses this structure to create a new image with the same layout and composition. Style Reference uses an image as a reference to generate a new image in the same style. 

These features are useful if you have an image you'd like the new, generated image to resemble, for example, a quick sketch you drew or even a business logo or style you'd like to keep consistent. 

Another perk is that Adobe Firefly was trained on Adobe Stock images, openly licensed content, and public domain content, making all the images generated safe for commercial use and addressing the ethics issue of image generators. 

Adobe Firefly f eatures:  Powered by:  Firefly Image 2 |  Access via:  Website |  Output:  4 images per prompt |  P rice:  Free 

Generative AI by Getty Images

Best ai image generator for businesses.

• Commercially safe
• Contributor compensation program
• Personalized stock photos
• Not clear about pricing
• Not individual-friendly

One of the biggest issues with AI image generators is that they typically train their generators on content from the entirety of the internet, which means the generators use aspects of creators' art without compensation. This approach also puts businesses that use generators at risk of copyright infringement. 

Generative AI by Getty Images tackles that issue by generating images with content solely from Getty Images' vast creative library with full indemnification for commercial use. The generated images will have Getty Images' standard royalty-free license, assuring customers that their content is fair to use without fearing legal repercussions.

Another pro is that contributors whose content was used to train the models will be compensated for their inclusion in the training set. This is a great solution for businesses that want stock photos that match their creative vision but do not want to deal with copyright-related issues. 

ZDNET's Tiernan Ray went hands-on with the AI image generator. Although the tool did not generate the most vivid images, especially compared to DALL-E, it did create accurate, reliable, and useable stock images. 

Generative AI by Getty Images f eatures:  Powered by:  NVIDIA Picasso |  Access via:  Website |  Output:  4 images per prompt |  P rice:  Paid (price undisclosed, have to contact the team)

What is the best AI image generator?

Image Creator from Microsoft Designer is the best overall AI image generator. Like DALL-E 3, Image Creator from Microsoft Designer combines accuracy, speed, and cost-effectiveness, and can generate high-quality images in seconds. However, unlike DALL-E 3, this Microsoft version is entirely free.

Whether you want to generate images of animals, objects, or even abstract concepts, Image Creator from Microsoft Designer can produce accurate depictions that meet your expectations. It is highly efficient, user-friendly, and cost-effective.

Note: Prices and features are subject to change.

Which is the right AI image generator for you?

Although I crowned Image Creator from Microsoft Designer the best AI image generator overall, other AI image generators perform better for specific needs. For example, suppose you are a professional using AI image generation for your business. In that case, you may need a tool like Generative AI by Getty Images which renders images safe for commercial use. 

On the other hand, if you want to play with AI art generating for entertainment purposes, Craiyon might be the best option because it's free, unlimited, and easy to use. 

How did I choose these AI image generators?

To find the best AI image generators, I tested each generator listed and compared their performance. The factors that went into testing performance included UI/UX, image results, cost, speed, and availability. Each AI image generator had different strengths and weaknesses, making each one the ideal fit for individuals as listed next to my picks. 

What is an AI image generator?

An AI image generator is software that uses AI to create images from user text inputs, usually within seconds. The images vary in style depending on the capabilities of the software, but can typically render an image in any style you want, including 3D, 2D, cinematic, modern, Renaissance, and more. 

How do AI image generators work?

Like any other AI model, AI image generators work on learned data they are trained with. Typically, these models are trained on billions of images, which they analyze for characteristics. These insights are then used by the models to create new images.

Are there ethical implications with AI image generators?

AI image generators are trained on billions of images found throughout the internet. These images are often artworks that belong to specific artists, which are then reimagined and repurposed by AI to generate your image. Although the output is not the same image, the new image has elements of the artist's original work not credited to them. 

Are there DALL-E 3 alternatives worth considering?

Contrary to what you might think, there are many AI image generators other than DALL-E 3. Some tools produce even better results than OpenAI's software. If you want to try something different, check out one of our alternatives above or the three additional options below. 

Nightcafe is a multi-purpose AI image generator. The tool is worth trying because it allows users to create unique and original artwork using different inputs and styles, including abstract, impressionism, expressionism, and more.

Canva is a versatile and powerful AI image generator that offers a wide range of options within its design platform. It allows users to create professional-looking designs for different marketing channels, including social media posts, ads, flyers, brochures, and more. 

Artificial Intelligence

This new ai tool from adobe makes generating the images you need even simpler, dall-e adds new ways to edit and create ai-generated images. learn how to use it, openai makes gpt-4 turbo with vision available to developers to unlock new ai apps.