|Anna Amalyah Agus||1. School of Business and Management, Bandung Institute of Technology, Jl. Ganesha 10, Bandung, Jawa Barat, 40132, Indonesia 2. Department of Management, Faculty of Economics and Business, Universit|
|Gatot Yudoko||School of Business and Management, Bandung Institute of Technology, Jl. Ganesha 10, Bandung, Jawa Barat, 40132, Indonesia|
|Nurbudi Mulyono||School of Business and Management, Bandung Institute of Technology, Jl. Ganesha 10, Bandung, Jawa Barat, 40132, Indonesia|
|Taliya Imaniya||Department of Management, Faculty of Economics and Business, Universitas Indonesia, Kampus UI Depok, Jawa Barat, 16424, Indonesia|
The Corona Virus Disease (COVID)-19 pandemic has
disrupted the business and industry landscape and changed consumers’ behavior.
The purpose of this paper was to explore how the behavior of online shoppers
and sellers changed because of the COVID-19 outbreak. The originality of this
paper lies in combining four main constructs: digital promotion capability,
supply chain capability, customer experience, and performance of the e-commerce
platform. It incorporates intervening factors like seasonal pricing and logistics
outsourcing in the context of COVID 19. The main findings were that, before the
pandemic, customer review ratings had a significant positive effect on the
performance of the e-commerce platform, but not after the outbreak. Meanwhile,
logistics outsourcing does not intervene in the relationship between perceived
supply chain capability and (relative) e-commerce platform performance, unlike
before the pandemic. This research is a longitudinal study before and after the
COVID 19 pandemic, with a call-back sample size of 88 end customer respondents
and 55 seller respondents. Data gathered from previous and current e-commerce
research were processed by multivariate regression using SPSS software.
COVID-19; Customer review rating; Digital marketing, E-commerce performance; Supply chain capability
The COVID-19 outbreak in 2020 disrupted many sectors of business and industry around the world. According to the World Health Organization (WHO), as of April 12, 2020, this virus had infected 1,654,247 people globally and caused as many as 102,193 deaths. The implementation by authorities of social/physical distancing and self-quarantine as public policy to handle COVID-19 has created a business slowdown. The COVID-19 outbreak has changed where and how people buy goods, and it has accelerated structural changes in industry that are felt by everyone (Accenture, 2020b). This also affected consumer channels, how retailers engage with each other (business-to-business relationships), and how firms work with their direct suppliers, wholesalers, and distributors. Another impact has been price gouging caused by low inventory levels and hoarding (Accenture, 2020a).
As so many people were living in quarantined, there was a significant increase in online shopping transactions. According to Nielsen (2020), 50% of respondents said that they visited malls and engaged in entertainment activities less often, followed by 46% who said they ate out less often, and 48% who hung out in cafes less often. COVID-19 is also seen as creating a worldwide economic disaster and uncertainty (Accenture, 2020b). The virus has created breaking points in the value chain, changed consumer patterns, and raised issues of fast cross-functional style assessment (Accenture, 2020a).
In Indonesia, confirmed cases of COVID-19 as of April 12, 2020, were as many as 4,241 people, with 373 deaths. On April 10, 2020, the Greater Area of Jakarta started to implement large-scale social restrictions (known as PSSB), and other provinces soon followed. The growth in demand for processed/canned foods and pharmaceutical products has increased since the first case of COVID-19 in Indonesia appeared (Nielsen, 2020). This is also in line with the finding of Berawi et al. (2020) on the managing multi-impact of COVID-19, Whulanza et al. (2020) and Tunjung et al. (2020) on the innovation of COVID-19 pharmaceutical/healthcare products.
Before the pandemic, this researcher conducted a study of the e-commerce platform ecosystem. It included digital promotion capability, supply chain capability, customer experience, and the effects of seasonal discount pricing and logistics outsourcing. The objects of that study included both sellers and end customers on the e-commerce platform. COVID-19 has created a longitudinal point of view, so the updated research in this paper is expected to provide important insights, especially in the context of Indonesia as the country with the fourth biggest population and one of the fastest growing sources of e-commerce in the world.
This method was adapted from Karjaluoto et al. (2015), that focused on how a digital marketing channel increases a firm’s performance. This is integrated with the work of Zhou et al. (2018), who set Internet trading platforms and made it possible to trade online between customers and suppliers anytime and anywhere despite being in different areas. The approaches of these two groups of researchers contributed to information processing theory and transaction cost economics theory, especially in how digital marketing influences consumer decision making and the buying process. Those approaches also provided support in determining how pricing strategy influences consumer decision making and the buying process. The result is hypotheses H1 and H1a:
H1: Perceived digital promotion capability has correlation with (relative) e-commerce platform performance.
H1a: Seasonal discount pricing has a mediating effect on the relationship between perceived digital promotion and (relative) e-commerce platform performance.
Meanwhile, Bakker et al. (2008) found that supply chain capabilities within an e-commerce platform have a positive correlation with internal readiness in contrast to external pressure from the e-commerce supply chain. This is related to the study from Pentina and Hasty (2009) who found that a higher degree of inter-channel coordination increased retailers’ online sales. While Yu et al. (2017) explain that outsourcing is more important than self-supported operational activities for raising profitability and/or lowering costs in e-commerce. The following hypotheses concern the differences of e-commerce supply chain approaches:
H2: Perceived supply chain capabilities have a positive correlation with e-commerce platform capability.
H2a: Logistics outsourcing has a mediating effect on the relationship between perceived logistics capability and (relative) e-commerce platform performance.
On the other hand, Gudigantala et al. (2016) proposed a theory based on their review of e-commerce literature concerning the point of view of e-commerce firms about web satisfaction, conversion rates, and purchase intention. They also explained that every unit rise in a website satisfaction score is predicted to raise average monthly revenue of $14.26 million, based on the model for an average e-commerce retailer. This leads to three hypotheses (H3, H4, and H4a) considered within an approach called multi-attribute utility theory (MAUT). The first of these is hypothesis H3:
H3: Customer experience (review rating) has a positive correlation with (relative) e-commerce platform performance.
Meanwhile, in the context of marketing, different (digital) communication strategies must be managed for consumers in polychronic and monochronic countries. Polychronic culture (multitasking culture where people like to do many tasks concurrently, i.e., French and Americans) and high context culture are more convenient for adopting and distributing through Internet retailing and on adopting Business-to-Consumer e-commerce (Gong, 2009). One study found seven factors for how marketing communication increases the purchasing desire of online consumers (Sahney et al., 2013): economic motivation (competitive pricing), social motivation (supportive social environment), product motivation (product availability), pragmatic motivation (convenience, perceived norms (family/friend influence), situational motivation (time pressure, lack of mobility, geographical distance, need for special items), service excellence motivation (value based perception), and demographic motivation (demographic parameters). The hypotheses below contribute to MAUT on how the consumer decision-making process wanders on e-commerce platforms. They also reflect information processing theories on how digital marketing influences the consumer decision making and buying process; they are:
H4: Perceived digital promotion capability has a positive correlation with customer experience (review ratings).
H4a: Seasonal discount pricing has a mediating effect on the relationship between perceived digital promotion and customer experience (review ratings).
Hartmann and Herb (2014) conceptually explain how social capital between partner firms and service buyers in a service triad affects the risk from the service buyer’s opportunism concerning the supplier’s behavior in order to reduce the risk. There are two main e-commerce logistic models that were classified by Yu et al. (2017). The first is a self-support model. It is more effective in executing and controlling strategy, but it has a higher cost. The second is an outsourcing model. It costs less, but it also provides less control of business operations. This model is also important in e-commerce logistics. Hypotheses H5, H5a, and H6 concern explaining the triad impact approaches form (Hartmann and Herb, 2014). They are also contributing to multi-attribute utility theory, especially on how the consumer decision-making process wanders in e-commerce platforms, and also an information processing theory on how the digital marketing process influences the consumer decision-making buying process, which are:
H5: Perceived supply chain capability has a positive correlation with customer experience (review ratings).
H5a: Logistics outsourcing has a mediating effect on the relationship between perceived logistics capability and customer experience (review ratings).
H6: Perceived digital promotion capability has an unknown correlation with perceived supply chain capability.
The whole hypotheses, research variables relations and main measurements are illustrated in Figure 1.
Figure 1 Conceptual framework (Agus
et al., 2020)
There are clear differences in the data for the end customers before and after the COVID-19 outbreak. They include: (1) Before the pandemic, customer experience (review rating) had a significant positive effect on (relative) e-commerce platform performance. However, the outbreak has changed customers’ behavior to buying what they need to buy under certain conditions. In this case, past customer experience (review ratings) does not have an effect. Buying is based on what products are needed right now; (2) Before the pandemic, logistics outsourcing intervened in the relationship between perceived supply chain capability and (relative) e-commerce platform performance, but not after the outbreak. The transformation of the supply chain after the pandemic may be the reason for this, as there were territorial restrictions and appeals to stay at home. This has made the firms implement the strategies of demand sensing and flexible manufacturing close to the consumer (Accenture, 2020b).
The other results show that the correlation is the same before and after the COVID-19 outbreak. For the sellers, there is no difference in the data before and after the pandemic began. In such a situation, sellers are encouraged to supply products or run promotional campaigns on items that the customers need most. The economics are disrupted in most sectors, and customers are worried about the effects of the pandemic. Therefore, their behaviors may change (especially in the problem recognition stage of a customer’s decision-making process). Customers may buy items needed to support them in this situation while they rethink and postpone buying items they want.
One limitation of this
research is that there were limited numbers of respondents in the sample. A
second limitation is that data were collected near the beginning of the COVID-19
outbreak. For future research, it is suggested to increase the respondents in
the sample, study the three phases of COVID-19, and process the data using
structural equation modeling (SEM) to see how the results differ.
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