BUILDING INNOVATION CAPABILITIES ON COLLABORATION AND MARKET ORIENTATION FOR IMPROVING MARKETING PERFORMANCE OF WOOD FURNITURE CRAFT

Article History Received: 4 October 2021 Revised: 19 November 2021 Accepted: 7 December 2021 Published: 29 December 2021

This research aims to test the direct influence of market orientation and innovation capabilities collaboration, with relationship marketing orientation and excellence as mediating variables, on the marketing performance of the micro industry of wood furniture handicrafts. The data was collected using questionnaires submitted by 242 people, and the analysis was carried out using SmartPLS software. The significant impact of collaborative innovation ability and relational marketing orientation on market orientation was shown to have no effect on competitive advantage. The innovation capabilities of collaborating are known to have no significant impact on marketing performance. Competitive advantage has a relationship with marketing performance, and relational marketing orientation has a significant impact on the ability of collaborative innovation. It was also found that relationship marketing orientation has a significant effect on marketing performance. This study is related to improving the marketing performance of the micro industry of wood furniture handicrafts, which has a limited scope, so the results of this study can't be generalized. It was also difficult to determine if there was any bias from the respondents. The marketing orientation of relationships and competitive advantages have contributed significantly to marketing performance. The wood furniture handicraft micro industry must improve collaboration capabilities with dealers and customers and use this as a strategic component to improve the marketing performance of the woodcraft industry. The novelty of this research is related to the capability of collaboration innovation, which is able to improve the marketing performance of small industries.

INTRODUCTION
The effort to maintain and increase competition require SMEs to have competitive advantage and formulate effective business strategies. According to Ehmke (2008), competitive advantage can be achieved by offering customers greater value through lower prices, or by providing additional benefits and services at the same price. If a company wants to gain a competitive advantage, it has to perform above consumer expectations by providing the best service so that it retains customers and ensures that they will not choose alternative companies (Parasuraman, relationship marketing by looking at the level of relationship quality, relationship quality benefits the quality of service provided to customers.
The effect of rapid changes in external environmental factors and competition in the global market today, SMEs can not only depend on their own resources, but they are also required to be able to establish business cooperation more closely with external parties through a collaboration network. Collaboration helps to increase the creation, division and transfer of knowledge, and will support a culture capable of making new discoveries and innovations that further impact the organizations' performance. Companies with the capability of collaborative innovation are able to acquire knowledge resources, which will lead to a competitive advantage. Related to this, this research departs on the existence of some controversies regarding the relationship of market orientation with marketing performance. It is necessary to explore empirical research models to fill research gaps that affect market orientation and marketing performance by providing an update on collaborative innovation capabilities.
According to the phenomenon problems of the decrease in marketing performance in SMEs, it is important to collaborate with customers and suppliers to contribute for the process of innovation in wood furniture craft SMEs.
Forms of collaborative innovation come from consumers and companies by conducting design activities, joint development, and creation of new products or services (Russo-Spena & Mele, 2012). Interaction with consumers will also contribute to innovation by ultimately generating demand. There is a principle in innovation, namely the process of value creation, which is done in collaboration with consumers, and rarely does a company have sufficient knowledge, expertise, skills and resources without doing value creation together without learning and accessing resources from consumers or other information (Zheng, Zhang, Ma, Xie, & Ma, 2011). Collaboration networks can be formed with clients, suppliers, and other companies (competitors) and horizontal networks consisting of research institutes, universities, and governments.

Innovation Capabilities of Collaboration
A company's capability in a resource-based view is one of the important internal factors in managing resources to achieve competitive advantage. When a company's capabilities are good, then the management of resources will be good, which will enable it to achieve competitive advantage. This means that the key to a company's success in achieving competitive advantage comes from the characteristics within the company itself (Fransisca, 2013).
Companies that want to survive in a competitive and uncertain environment have been forced to develop their own innovation capabilities. The capability of companies to create and exploit external opportunities and build competitive advantages by having the ability to innovate will help them to survive. Innovation is the mechanism by which a company adapts to a dynamic environment and is separated into three categories: organizational innovation, innovation level, and innovation capabilities. Research on the level of innovation explains different types of innovations. This type of innovation becomes incremental, radical and moderate innovation. Growth and achieving success and sustainability is the goal of every company. To improve the performance of SMEs, industries needs to emphasize the importance of building collaboration capabilities when innovating. By having a high ability to innovate, a company will be able to respond to the changing environment and develop new capabilities to gain an advantage in business competition. Collaboration competency innovation helps companies to acquire knowledge resources which they can use toward gaining a competitive advantage. Therefore, SMEs in the woodcraft industry must find new ways to increase market share and business performance, and also collaborate on sustainable innovation. One of the key success factors in organizations is product innovation, which is a strategy used to improve business performance and market share (Hassan, Shaukat, Nawaz, & Naz, 2013).

Market Orientation
Market orientation is the development of market intelligence to meet current and future consumer needs and is a measure of behavior and activity that reflects the implementation of marketing concepts related to organizational learning from customers and competitors. Market orientation is a set of values that put the interests of customers above all else to increase the company's profits over a long period and it is the informal obtainment of customers and competitors through the collection, deployment, and response of market intelligence in response to market share. Market orientation includes three elements: market intelligence as part of providing appropriate market information; dissemination of information provided to all units of the company to develop strategic synergies; and response to market intelligence in each unit of the company through the form of marketing strategies according to the market environment (Deshpandé & Farley, 1998).

Competitive Advantaging
The increasing competition at every level requires companies to prepare everything in order to be able to grow and survive competition. Competitive advantage includes focusing on developing superior skills and resources. (Day & Wensley, 1988) explained that competitive advantage can be achieved by creating economic value compared to competitors by generating more benefits through low cost differentiation (Dyhdalewicz & Widelska, 2017).

Relationship Marketing Orientation
A marketing relationship is characterized by a reciprocal, interdependent, committed, and long-term relationship between the seller and the buyer. The concept of relationship marketing has gotten a lot of research attention from scholars. Business philosophy has undergone a shift from a production orientation to a sales orientation, then to a marketing orientation and eventually to relationship marketing orientation (RMO) (Sin, Tse, Yau, Chow, & Lee, 2005). The fundamental benefits of RMO create stronger customer relationships that improve performance outcomes such as sales growth, market share, profit, return on investment, and customer retention.
Further RMO consists of trust, bonding, communication, shared values, empathy, and reciprocity. Relationship marketing orientation proactively creates, develops and maintains relationships between buyers and sellers in the long term and is mutually beneficial on a company performance level.

Marketing Performance
One of the factors that companies use to measure the impact of their strategy is marketing performance (Ferdinand, 2002). Marketing performance provides three dimensions, namely the company effectiveness, sales growth, and the company's maturity. Marketing performance also includes market share and sales volume achieved by the company (Huhtala, Sihvonen, Frösén, Jaakkola, & Tikkanen, 2014). Performance is measured through sales volume, achievement of revenue goals, achievement of sales growth and achievement of market share. The innovation capability concepts and study theories about the concept of market orientation, competitive advantage and relationship marketing orientation can be seen in Figure 1.

RESEARCH HYPOTHESES
Empirical studies show that market orientation has a direct and positive influence on the ability to innovate.
Based on the results of the research conducted, market orientation positively affects additional service-related innovations and radical innovations, and it encourages the achievement of the subjective performance of a company. A competitor-oriented culture can increase innovation, as it is used to develop products that distinguish a company from its competitors. Market orientation has a positive and significant effect on innovation capability.
Based on the opinions and results of previous studies, the following hypothesis is proposed:

H1: Market orientation has an effect on collaborative innovation capability.
The increasingly dynamic market competition demands that every company has the ability to innovate.
Capacity to innovate is one of the most important factors that can affect business performance. Prajogo (2006) stated that the innovation of a process is strongly related to a company's performance. The need to collaborate through competitive methods to create innovation-oriented strategies is more important to maintaining survival.
Product innovation has a positive and significant effect on marketing performance advantage.
Based on the opinions and results of previous studies, the following hypothesis is proposed:

H2: Innovation capabilities of collaborating has an effect on marketing performance.
Achieving market performance not only attracts and generates satisfied customers, but it also forms customer loyalty. Therefore, a company should be able to base their marketing on customer relationships. Relationship marketing emphasizes long-term interactive relationships between service providers and customers, bringing long-term benefits. Market orientation and relationship marketing orientation are two interconnected concepts and are under one philosophical umbrella of marketing. Through market orientation, the value and quality of service that customers receive increases and the company increases the level of value felt by employees, which ultimately results in a higher level of customer service quality. Market orientation is related to creating and satisfying the needs and desires of its customers. Based on the opinions and results of previous studies, the following hypothesis is proposed: The application of market orientation by a company has the potential to create advantages in customer knowledge, and this will be a source in creating products according to customer desires and needs. This can also be a source of competitive advantage, as market orientation is a complementary asset that contributes to the company's superior performance. Based on the opinions and results of previous studies, the following hypothesis is proposed: H4: Market orientation has an effect on competitive advantage.
Companies are required to have the ability to build and maintain relationships with their customers in order to face dynamic market competition. Relationship marketing of a marketing concept emphasizes the creation and development of relationships with customers and stakeholders. Augusto and Coelho (2009) explained that the relationship marketing orientation of a company's technology resources serves to provide sustainable innovation performance. Codourey (2013) stated that relationship marketing orientation strategies, in addition to attracting new customers, also used continuous improvement and innovation. Moreover, Dingler and Enkel (2016) defined customer collaboration as a tool for obtaining information on customer preferences to suggest ideas for new product. Another study on relationship marketing proves theoretically and empirically that customer collaboration improves the success of innovation performance. The marketing orientation of the relationship consists of reciprocity, interdependency, commitment, as well as seller and buyer relationships, dissemination of market information, and coordination between functional information owned by the company. From this, it is possible to support the success of collaboration capabilities in innovation. Based on the opinions and results of previous studies, the hypothesis proposed in this study is as follows:

H5: Relationship marketing orientation has an influence on the capability of collaborative innovation.
Every competing company is required to think and act innovatively. Innovation is as a way for companies to adapt to their environment due to dynamic change. An innovative company has the ability to improve the performance of individuals, the performance of organization as a whole, and increase competitive advantage (Liao, Wu, Hu, & Tsuei, 2009). Larsen and Lewis (2007) explained that creative and innovative companies are more able to compete and survive in a competitive environment. Based on the opinions and results of previous studies, the hypothesis in this study is as follows: H6: Collaboration innovation capabilities have an impact on competitive advantage.
As previously stated, business philosophy has shifted from production orientation to sales orientation and then to marketing orientation and finally to relationship marketing orientation. The establishment of a relationship between consumers and manufacturers will benefit all parties. Sin., Tse, Chan, Vincent, and Heung (2006) found that marketing orientation has a positive and significant effect on marketing performance. Moreover, Nguyen and Liem (2011) explained that the key components of relationship marketing orientation are trust, bonding, shared values, reciprocity, communication and empathy. Based on the opinions and results of previous studies, the hypothesis in this study is as follows:

H7: Relationship marketing orientation has an influence on marketing performance.
Having the ability to compete is the key to maintaining business performance in the long term in increasingly competitive conditions. Every business must have a competitive advantage in order to survive and operate efficiently. Some literature and studies have provided evidence that there is a positive relationship between the superiority of the competitive and the performance of marketing. Based on research conducted by Pardi, Imam, and Zainul (2014), finding competitive advantages can have a positive influence on marketing performance. Furthermore, (Strandskov, 2006) found that competitive advantages, such as firm-specific advantages and relationshipspecific advantages, have an effect on the success of a business. Based on the opinions and results of previous studies, the hypothesis in this study is as follows: H8: Competitive advantage has an influence on marketing performance.

Research Design
This research is aimed at improving scientific knowledge and finding new research fields that, in the long run, can be used to measure the impact of exogenous variables (e.g., market orientation) on endogenous variables (e.g., marketing performance) involving intervening variables (e.g., relationship marketing orientation), competitive advantage, and collaboration innovation capability, by analyzing SMEs in the woodcraft industry. The research population of woodcraft and furniture SMEs is based on businesses in North Hulu Sungai Regency using a sample of 242 respondents.

Variable Operational Definitions
a. Market orientation: the behavior of woodworking handicraft businesses in maintaining their business by fulfilling the needs of the current or future market, which is measured using the following indicators: information collection, dissemination of market information, and informational affairs.
b. Innovation capability collaboration: the ability of woodcraft businesses to build relationships with dealers and customers, and to work together continuously through the absorption of information and knowledge to innovate services, develop new products and determine new markets. This is measured using cooperation, information absorption, coordination of new ideas and the ability to innovate as indicators.
c. Competitive advantage: the ability of businesses to add value to their products compared to their competitors, which is measured using uniqueness/creativity, competitive price, originality, and reliability of service as indicators.
d. Relationship marketing orientation: a company's activities in creating, maintaining and improving longterm relationships with customers to provide mutual profit, measured using indicators including trust, bonding/connection, marketing communication, empathy and reciprocity.
e. Marketing performance: the ability of businesses to achieve marketing targets that have been set for a certain period of time by looking at sales levels, market coverage, the number of new customers and the level of profit of the company compared to competitors, measured using indicators including market coverage, sales value, increase in customers, and increase in company profit.

DATA COLLECTION METHOD AND DATA ANALYSIS
The method of data collection aims to demonstrate systematic scientific facilities by using personally administered questionnaires. In this study, data analysis was carried out using the partial least squares (PLS) model with the help of SmartPLS software. Furthermore, the test results of the significant influence between variables with the calculation of the t-statistics inner model structural fit can be seen in Table 1 and Figure 2.  Figure 3, which is evidence of the presence of significant variable influences. The analysis of the first hypothesis tests the influence of market orientation (X1) on the capability of collaborative innovation (X2). From the results, it is known that the tstatistical value of 1.976 > 1.96. Because the t-statistical value is greater than the t-value, it can be concluded that market orientation has a significant positive effect on the capability of collaborative innovation at a rate of 5% significance, with a large influence (0.180 or 18.0%).     This shows that market orientation is important for companies to support building innovation collaboration for woodworkers. The complexity of competition and the diversity of customer demand means that the craftsmen need to be committed to creating and innovating to ensure the superior quality of their products. The form of collaboration is determined by discussing ideas with dealers, hearing customer opinions, reviewing changes in the market environment, conducting customer satisfaction surveys, focusing on strengthening the culture of functional cooperation or coordinating strategies to ensure that the needs of new customers are met. Collaboration becomes an important strategy for small and medium enterprises (SMEs) to develop innovations because their resources are limited. Through her research, (Laforet, 2011) combined company size and strategic and market orientation factors as innovation predictors. Other studies that focus on market orientation and its relation to innovation include (Carmen & José, 2008;Hilman & Kaliappen, 2015). Findings of this study show that wood-oriented crafts in the market have an impact on the development of collaboration capabilities when innovating.
The analysis of the second hypothesis involved testing the influence of market orientation (X1) on competitive advantage (X3). From the results, it is known that the t-statistical value of 1.344 < 1.96. Because the t-statistical value is smaller than the t-value, it can be concluded that market orientation has no significant effect on competitive advantage at the 5% significance level, with a great influence (0.95 or 0.95%). This shows the orientation of the existing market and that what has been done by the craftsmen of the woodworking industry has not been able to support the creation of a competitive advantage. The results of the above research show that woodworkers have not been maximally responsive to observing various problems and increasingly tight competition. The results of this study do not match the theory presented, where a corporate culture that emphasizes the importance of paying attention to the market orientation will lead to the strengthening of a company's competitive advantage. Contrary to the theory presented (Selnes, Jaworski, & Kohli, 1996), where in its statement through market orientation allows the industry to explore and analyze the external environment in order to understand the pre-reference necessity of the market, how to coordinate the dissemination of information across functions and satisfying its customers as well as responding to changes regarding competitors and market potential. The results of this study also do not support the study by Narver and Slater (1990), where the greater the customer orientation, the more a company will be able to develop a competitive advantage based on differentiation of market necessity. Basically, businesses such as those in the woodworking industry must be market-oriented because with market-orientation it can build a systematic and comprehensive culture to commit sustainably to creating a competitive advantage that is finally able to improve the marketing performance of the woodworking industry.
Analysis of the third hypothesis involved testing the influence of market orientation (X1) on relationship marketing orientation (X4). From the results, it is known that the t-statistical value of 11.044 > 1.96. Because the tstatistical value is greater than the t-value it can be concluded that market orientation has a significant positive effect on relationship marketing orientations at a significance of 5%, with a large influence (0.512 or 51.2%). These objectives can be fulfilled by the way businesses foster good relationships with their customers, employees, suppliers, distributors and distribution partners because good relationships can determine the future of a company.
With a view to building partnerships and providing the best quality for customers, a company can build long-term alliances with customers, suppliers, distributors and distribution partners through relationship marketing orientations (RMO) (Evans & Laskin, 1994). To create a market-oriented company, woodcraft businesses must be able to meet every need of its customers. Through market orientation, product value and quality of service that customers may be concerned about will increase, and eventually the company will also increase the level of value for its employees, which means that the quality level of customer service becomes higher. Moreover, if a business adopts a relationship approach with its customers, it will have access to a quality source of marketing intelligence, which will help to improve their performance in a more competitive market (Olotu, Maclayton, & Ahiauzu, 2011).
Research conducted by Sin. et al. (2006) states that the key factors of success persist in a mature market and depend on maintaining long-term relationships with stakeholders (customers), employees, suppliers, and community representatives. Furthermore, market orientation and relational marketing orientation are both the right blend of a company that will produce a different level of performance compared to other companies.
The analysis of the fourth hypothesis tested the capability of collaborative innovation (X2) against competitive advantage (X4). From the results of the analysis, it is known that the t-statistical value of 8.975 > 1.96. Because the t-statistical value is greater than the t-value, it can be concluded that the collaboration innovation capability negatively and significantly affects relationship marketing orientation at a significance level of 5%, with a large influence (-0.463 or -46.3%). The main objective of a company is the ability of innovation to meet market demand, so innovation becomes one of the strategies used to gain a competitive advantage. Companies that successfully build innovation capabilities through collaboration mean they are one step ahead of their competitors and can use these capabilities as a foundation for achieving long-term competitive advantage. The ability to innovate is a company's mechanism to adapt in a competitive environment. Therefore, every industry is required to create new thoughts and ideas and offer innovative products as well as improve the distribution service process in order to survive, keep customers satisfied and beat the competition. The ability to find creative new ideas and collaborative innovations for each product will create a competitive advantage.
Analysis of the fifth hypothesis tested collaboration innovation capability (X2) against marketing performance (Y). The results of the analysis show that the t-statistical value is 1.897 < 1.96. Because the t-statistical value is smaller than the t-value, it can be concluded that collaboration innovation capability has an insignificant effect on marketing performance at a significance of 5%, with a large influence (0.147 or 1.47%). This shows that the collaboration innovation capability built so far by the players of the woodworking industry has not been able to support the improvement of marketing performance. The existing form of collaborative innovation capabilities still cannot improve from sustainable marketing performance. The form of innovation collaboration through dealers and customers with the aim of creating something new and diverse for product innovation, distribution services and determination of new market potential is not able to drive the improvement of marketing performance, which is reflected in the lack of an increase in sales profit. New customers and annual market share have not provided significant results, and many woodcrafters do not have the ability or competence to innovate and they have not been able to influence from their low ability level of collaboration innovation. The results of this study do not support what is conveyed by Wang and Ahmed (2007), which says the ability of companies to develop new products or markets through the alignment of strategic innovation orientation with innovative processes and behaviors is a form of collaboration capability of innovating.
Analysis of the sixth hypothesis tests the influence of competitive advantage (X3) on marketing performance (Y). The results show that the t-statistical value of 5.764 > 1.96. Because the t-statistical value is greater than the tvalue, it can be concluded that competitive advantage has a significant positive effect on marketing performance at a rate of 5% significance, with a large influence (0.331 or 33.1%). To beat the competition and be able to improve marketing performance, it is not enough for businesses to be skilled in managing products, they must also build profitable customer relationships and achieve a competitive advantage.
The analysis of the seventh hypothesis tests the influence of relationship marketing orientation (X4) on collaborative innovation capabilities (X2). From the results of the analysis, it is known that the t-statistical value of 4.861 > 1.96. Because the t-statistical value is greater than the t-value, it can be concluded that relationship marketing orientation has a significant positive effect on the innovation capability of collaborating at a significance level of 5%, with a large influence (0.364 or 36.4%). Increased competition, relentless turbulence, change, and uncertainty force organizations to build innovation capabilities, so the ability to innovate is an integral part of every company's strategy. A company's ability to develop and implement innovation has become a fundamental determinant of competitiveness for every SME. This study found that relationship marketing orientation is able to influence the ability to collaborate on innovation, which is in accordance with what is stated by (Augusto & Coelho, 2009) -that the orientation of relationship marketing (RM) is a technological resource of a company that serves to provide sustainable innovation performance. This is because relationship marketing orientation improves market sensing capabilities and quality customer relationships, and this capability can be assumed to improve the performance capabilities of higher innovations than those of competitor companies (Baker & Sinkula, 1999).
Therefore, innovation capabilities in SMEs can be sourced from external activities as well as internally in the industry. As a form of corporate behavior related to the external market environment, relationship marketing orientation can be used to anticipate and respond to the needs of customers and support success in innovating (Sharma & Patterson, 1999). In addition, the theory from Bustinza, Gomes, Vendrell-Herrero, and Baines (2019) has stated that the strategic objectives of relationship marketing orientation are not only to attract new customers but also to retain them, which requires improvement and sustainable innovation capabilities. Whereas Naili, Naryoso, and Ardyan (2017) found that relationship marketing is able to maintain continuity in long-term relationships with consumers, suppliers, retailers and customers. Through marketing, the relationships will form a collaboration or relationship between suppliers, suppliers, retailers and customers to achieve innovation. This fact is reinforced by the response of 70.7% of respondents in this study, who stated that the intertwining of customer relationships and the channel plays a major role in developing new ideas to support and create innovation.
The results of this research are in line with those of Un and Asakawa (2015), who have proven that collaboration, especially external collaboration, improves the performance of a company's innovation. Relationship marketing orientation, which is characterized by reciprocity, interdependency, commitment, and long-term relationships between sellers and buyers, increases and disseminates market information by coordinating functional information owned by the company to create value for customers which can support success in building collaboration innovation.
The analysis of the eighth hypothesis tests the influence of relationship marketing orientation (X4) on marketing performance (Y). From the results of the analysis, it is known that the t-statistical value of 0.849 < 1.96.
Because the t-statistical value is smaller than the t-value, it can be concluded that relationship marketing orientation has no significant effect on marketing performance at a significance level of 5%, with a large influence (0.075 or 0.75%). This result indicates that the activities of relationship marketing orientation conducted by the owners or managers of woodworking businesses in North Hulu Sungai Regency do not affect marketing performance. This means that although the activities of relationship marketing orientation (instilling trust and privacy, establishing relationships and periodic communication, addressing customer complaints, implementing effective marketing communications as well as caring about each customer's needs and desires) are done intensively, they will not impact the level of marketing performance of wood furniture handicrafts. Acquiring new customers is just as important as maintaining and improving relationships with current customers to achieve long-term marketing performance success. To increase demand, a business must meet customer expectations and maintain performance by creating permanent relationships with customers in addition to gaining new customers. The results of this study on relationship marketing orientation became part of the marketing management activities that are less effective so that the improvement of the activity does not impact the marketing performance for owners or craftsmen in the woodworking industry.

CONCLUSION
The results of the analysis concluded, Market orientation proved to have a positive and significant effect on innovation capabilities in collaboration, Relationship marketing orientation and competitive advantage.
Collaborative innovation capabilities can positively affect marketing performance as well as competitive excellence.
Relationship marketing orientation can positively affect significantly the capability of collaborative innovation and marketing performance. The advantages of competing positively are significantly able to influence marketing performance. Further results of this study found relationship marketing orientation proved to have no significant effect on competitive advantage, as well as for market orientation proved to have no significant effect on marketing 449 © 2021 Conscientia Beam. All Rights Reserved.
performance. Based on the findings of its research capability on innovation collaborates, competitive advantages and relationship marketing orientation can be a booster in improving the marketing performance of wooden furniture handicrafts in Hulu Sungai Utara Regency. While the implementation of relationship marketing orientation that has been done by wood furniture artisans in Hulu Sungai Utara Regency has not given a meaningful contribution that is influence in creating competitive advantages. Moreover, marketing orientation that has been run by wood furniture artisans is also not able to influence the marketing performance of the wood furniture industry in Hulu Sungai Utara Regency.

LIMITATIONS AND FUTURE RESEARCH
This research focuses only on industrial groups in the field of wood furniture handicrafts and with representatives or samples is very limited in number, so the generalization of the results of this research must be done carefully. Further research should take more broad objects and more samples so that the research results can be generalized better, especially the more complete competition and high dynamics of marketing performance in the small industry craft furniture and other variables that affect it is always changing as the environment changes.
Furthermore, the new concept of innovation capability in collaboration needs to be studied more deeply in the business sector or other business fields, such as service sector industry, batik handicraft industry, rattan furniture or home food industry of local products for further researchers. Future research may expand the area or object of more general research and larger respondents.


An Analysis of the Work Resumption in China under the COVID-19 Epidemic Based on Night Time Lights Data

: Public emergencies often have an impact on the production and operation of enterprises. Timely and effective quantitative measurement of enterprises’ ofﬂine resumption of work after public emergencies is conducive to the formulation and implementation of relevant policies. In this study, we analyze the level of work resumption after the coronavirus disease 2019 (COVID-19)-inﬂuenced Chinese Spring Festival in 2020 with night time lights remote sensing data and Baidu Migration data. The results are veriﬁed by ofﬁcial statistics and facts, which demonstrates that COVID-19 has seriously affected the resumption of work after the Spring Festival holiday. Since 10 February, work has been resuming in localities. By the end of March, the work resumption index of most cities exceeded 70% and even Shanghai, Nanjing and Suzhou had achieved complete resumption of work. Wuhan only started to resume work in the last week of March due to the more severe outbreak. Although the level of work resumption is gradually increasing in every area, the speciﬁc situation of resumption of work varies in different regions. The process of work resumption in coastal areas is faster, while the process is relatively slow in inland cities.

Introduction
In December 2019, COVID-19 outbroke in Wuhan, China [1,2]. The epidemic spread across the whole country with the virus's highly infectious and mass migration movements before the Spring Festival of China. To contain the outbreak, Wuhan went into lockdown and the other regions curbed population flow since 23 January 2020 [3]. Everyone must make fewer trips outside and the Chinese New Year holiday was extended. The COVID-19 outbreak has seriously affected all aspects of society and China's economic activities have been stagnant for a short period [4]. Since the prevention work was carried out strictly, the epidemic situation has witnessed positive changes. Work has been resuming in an orderly manner in many regions for economic development since 10 February. Due to the various difficulties and risks in the process, it takes a long time to recover completely. Therefore, dynamic monitoring of the resumption of work is urgently needed. An accurate understanding of the level of resumption of work can provide necessary quantitative support for steadily promoting the formulation and implementation of relevant policies for enterprises' resumption of work and production.
The studies of resumption of work mostly focus on the individual, with emphasis on the factors influencing individual return to work after injury or illness [5,6]. For the measurement of the resumption of work in this epidemic, it is generally the statistical data collected by the Bureau of Statistics, industry associations and other organizations. Since February 2020, the government has gradually announced the rate of work resumption of enterprises above designated size (here, the enterprise is denoted as the revenue of major business above 2000 million yuan) on the province scale. However, manual statistics are a costly and cumbersome undertaking and the results exhibit hysteresis. Enterprises above the designated size do not represent small and medium-sized enterprises. Therefore, a method is urgently needed to estimate the rework rate.
Scholars tried to use different quantitative methods to study the resumption of work in public emergencies. Nie et al. [7] used 931 sample data of mobile terminal locations to calculate the resumption level of enterprises. However, the sample data is not representative of the entire region. Gan et al. [8] used the migration index to calculate the intensity of the resumption of work in cities and proved that the level of work resumption was significantly related to the shortage of manpower, the number of basic regenerations and GDP. Xu et al. [9] used big data from Baidu Migration to analyse the level of social production and life recovery after the Spring Festival affected by COVID-19 in 2020. Li et al. [10] calculated the work resumption rate for 366 cities in China in 2020 based on Baidu's migration-in and migration-out data and predicted the average monthly income of migrant workers under different scenarios. However, they only estimated the level of resumption of work in February as a whole or on a date and did not get a long-term change in the resumption of work. So far, there are still few studies on the dynamic measurement of enterprises' resumption of work by provinces and cities in the academic circle.
Remote sensing data becomes an important data source with unique timeliness and availability owing to the rapid development of remote sensing technology. It is widely used in the study of natural disasters and epidemic diseases [11,12]. Remote sensing data of night light emissions can be used to monitor the intensity of human activities by detecting the urban night time lights (NTLs), even the low-intensity night light generated by the small-scale residential area, traffic flow, etc. [13,14]. Therefore, NTLs data is widely used in many research fields such as urban development research [15][16][17][18][19][20], socio-economic parameter estimation [21][22][23][24], major events assessment [25,26], fishery monitoring [27,28], holidays in lights [29], mapping fires, gas flares and greenhouse gas emissions [30][31][32][33] and so on. Similarly, NTLs data were applied to the impact of the COVID-19 outbreak on society [34,35]. In studies of major events assessment based on NTLs, scholars mostly focus on the restoration of electric power and economic losses [36][37][38][39], with little attention to the resumption of work. Though the COVID-19 epidemic, unlike natural disasters, did not result in damage to electrical utilities, the decrease in the brightness of the night time light is still noticeable due to the shutdown of almost all industries. Liu et al. [40] used night time light and air quality data to analyze the COVID-19 impacts on human activities and the environment. They found that the lights in residential areas increased, while the brightness of commercial centers decreased significantly during the epidemic period. Elvidge et al. [41] also demonstrated light dimming in China using different reference images, epidemiological images, scatter plots and night time profiles during the COVID-19 Pandemic. However, previous studies have shown that night time lights in China have been growing for several decades [42]. As shown in Figure 1, the luminous intensity of Beijing decreases in February and increases in March using National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) monthly average radiance composite night time lights data. It means that the brightness of the NTLs decreased under the influence of the New Year holiday (Chinese New Year 2020 starts on 25 January) and the epidemic. In addition, with the resumption of work, the brightness of the lights may gradually recover. So, the NTLs can be used as a proxy indicator to monitor the recovery of the economic activity level. Lan et al. [43] used NPP-VIIRS DNB daily data to explore the impact of the epidemic and the extent of return to work in China. The high agreement between urban travel intensity and NTLs data proved the validity of the results. Yin et al. [44] used ratio maps, difference maps, wave indices and recovery rates based on night time lighting data to demonstrate the recovery patterns and spatial distribution characteristics of NTLs. However, none of them took into account that night time lighting in residential areas can affect the resumption rate. It is unrealistic to count the work resumption rate of each area manually. The above study shows that night time lighting data and Baidu map migration data can be applied to the work resumption rate study. To estimate the resumption level as accurately as possible, NPP-VIIRS night time lights together with urban travel intensity (UTI) data from Baidu Map are utilized to calculate the work resumption index (the approximate offline work resumption rate) after the COVID-19-influenced Chinese Spring Festival. First, the NPP-VIIRS DNB night time lights data are calibrated to remove the influence of clouds, moonlight and other factors. Next, the VIIRS images are eliminated residential pixels using urban land use data. Then, the linear regression algorithm is used to calculate the trend of the time series of each pixel of NTLs images to fill in the gaps created by data filtering. Finally, we calculate the work resumption index for 21 cities and 7 provinces in China ( Figure 2) based on NTLs and UTI and verify the results with official statistics and facts.

Data I-NPP-VIIRS Night Time Lights
In this study, the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily night time lights data is used to calculate the work resumption index. Compared to the previous Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS), VIIRS has higher radiometric accuracy and provides onboard calibration, which can guarantee the accuracy and stability of the data. In addition, its radiation measurements range from 3 × 10 −9 W·cm −2 ·sr −1 to 0.02 W·cm −2 ·sr −1 , which ensures that the sensor can detect very low brightness without reaching saturation in high brightness regions. The daily NTLs data used in the study were NASA's daily Black Marble night time lights product suite (VNP46A1) [45] with 500 m spatial resolution and archived at NASA's Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC) (https://ladsweb.modaps.eosdis.nasa.gov/, accessed on 15 March 2020). Because of its high temporal resolution, the daily NTLs data is usually used for dynamic analysis of short-term events [26,46]. The NTLs data provided in this product is sensor radiation, so it is necessary to preprocess the original data to remove the influence of reflected moonlight, cloud and so on. Another product is monthly average radiance composite images using the daily image. Prior to averaging, the DNB data is filtered to exclude data impacted by stray light, lightning, lunar illumination and cloud-cover [47]. However, the lights from aurora, fires, boats and other temporal lights still exist in monthly composites. The temporal light of the city in the study will produce outliers, so the monthly images also need to be pre-processed.

Data II-MODIS Land Surface Reflectance
MODIS land surface reflectance product, MOD09A1 with 500 m spatial resolution was selected as the auxiliary data to preprocess the NTLs, which are available from NASA's LAADS DAAC data center (https://ladsweb.modaps.eosdis.nasa.gov/, accessed on 26 March 2020). MOD09A1 is an 8-day gridded level3 product in the Sinusoidal projection. Each MOD09A1 pixel contains the best possible L2G observation during 8 days as selected on the basis of high observation coverage, low view angle, the absence of clouds or cloud shadow and aerosol loading. Following the method proposed by Zhao et al. [26], the MODIS surface reflectance data was used to calculate moonlight radiation.

Data III-Population Migration
Baidu map big data of population migration (Baidu migration) conducts calculation and analysis using location-based service (LBS) big data, based on the Baidu cloud data analysis platform. It realizes the visualization of personal geographic space movement through mobile phone users' positioning information. The Baidu map migration data include the inter-city move-in and move-out size index and intra-city urban travel intensity. Here, we used the urban travel intensity (UTI) (http://qianxi.baidu.com/, accessed on 17 March 2020), the indexation of ratio of the number of people traveling within the city to the number of people living in a city, to reflect the resumption of work.

Data IV-Other
Administrative boundary data were obtained from Big Earth Data Science Engineering Project (CASEarth) Data sharing service system [48]. In addition, land use data is essential urban land use categories in China mapping (EULUC) shared by Prof. Gong et al. [49]. The data divide urban land into five categories: residence, commercial, industrial, transportation and public management and service, which was used as the base map to eliminate the pixels of NTLs images that do not contribute to the calculation of work resumption index.

Method
The process of calculating the work resumption index is shown as Figure 3: Preprocess with VIIRS-DNB daily night time lights. As mentioned above, the NPP-VIIRS DNB daily data is probably affected by clouds, moonlight radiation ( Figure 4) and other factors, so the original data need to be preprocessed. For moonlight radiation, Miller and Turner [50] proposed MT2009, a lunar spectral irradiation model of Top of Atmosphere (TOA), which is calculated with the distance between sun and moon, the distance between moon and earth, date and time. On this basis, Cao et al. [51] proposed the estimation model of DNB moonlight radiation: where E m is the TOA lunar irradiance derived from the MT2009 model. θ m is the lunar zenith angle. ρ is land surface reflectance. Following the method proposed by Zhao et al. [26], the average of band 1, 2, 4 of the 8-day composite MODIS surface reflectance data was used instead of the TOA reflectance in the original method, which is affected by clouds. The lunar radiation L m is calculated for pixel in each night time light image according to Equation (1). Subtracting L m from the digital number (DN) of the original NTLs pixel (i.e., original NTLs radiation subtracts lunar radiation), the image illuminated by moonlight was eliminated and moonlight corrected images were obtained. Cloud reduces the night time light intensity and blurs light spatial detail features [52]. The cloud-mask flag in VNP46A1 was used to remove the "confidentially" cloudy pixels. Simultaneously, the snow/ice flag of the cloud mask data in VNPA1 was used to exclude areas covered by snow and ice. After the above processing, outliers might be still in the data. To reduce the influence of outliers, the noise of daily NTL data was removed using corresponding monthly composite images. Finally, we removed images still contaminated by clouds manually, due to errors in the cloud-mask flag. Specifically, each image for each city was checked manually. When the image is blurred as in Figure 4b, it indicates that the image is still covered by clouds and is deleted. The remaining images are all high-quality and cloud-free together without moonlight contamination.
The monthly composite images were compared with images of adjacent phases (for example, comparing images from February 2020 with images from January and March 2020) to eliminate the noise generated by transient light sources.
Remove residential pixels of night time lighting images. In the calculation of the work resumption index, the night time light changes in industrial areas, business areas, education areas and other areas, can directly reflect the degree of resumption of work, while the light change in residential areas cannot. Liu et al. [40] found that the lights in residential areas increased after the beginning of the lockdown. As the residential areas occupy a large area of city, it is necessary to eliminate the NTLs in the residential areas. EULUC data were used as the base map to extract the NTLs image pixels of the residential land-use category. When the area of residential land-use category in a pixel exceeds 50%, the pixel was excluded to reduce the influence of night time lighting in a residential area as much as possible. The NTLs data of non-residential areas were obtained after removing.
Fill the gaps in night time lighting images. After preprocessing, a lot of gaps are left in the image that should be filled to calculate the work resumption index more accurately. The least-squares linear regression model for the per-pixel time series of the daily data was used to obtain the trend of NTLs data. Although scholars mostly chose the seasonal and trend decomposition using loess (STL) decomposition [29] method to obtain the NTLs trend, Eleanor C. Stokes et al. [53] found that the trend slope has a little difference using between the weighted least square linear regression methods and the STL decomposition approaches and the linear regression is much simpler. Next, the pixels' values of the gap were predicted and filled according to the trend of each pixel time series. Specifically, the daily brightness of lights (except for vacancies) for each pixel during the study period (from 10 February 2020 to 29 March 2020) was counted. A regression fit was made to every time-series data to obtain a linear equation. Then, the values at the vacancies were then estimated and filled. It is worth noting that the gap filling is not carried out when the image of a city has many gaps, which means that the area is seriously polluted by clouds in the period. In this study, the threshold was set to 20%. No filling is done when the percentage of vacancies is higher than this number. Likewise, the work resumption index is also not calculated.
Calculate the work resumption index. Although many studies have been done on the resumption of work after public emergencies [7,9,43], the rate of work resumption after public emergencies has not been defined clearly [7]. Combined with existing research [9,10,43,44], the resumption of work level can be reflected by the extent to which the brightness of NTLs (or urban travel intensity) from the Spring Festival holiday back to the normal working day. We proposed the work resumption index that approximates the degree of offline work resumption.
The calculation formula of the work resumption index WRI is shown as follows Equation (2): where NTL t is the average DNB radiance of week t. The work resumption index was calculated each week starting from 10 February 2020 (The 17th day of the first lunar month) to 29 March 2020. NTL holiday is the average DNB radiance during the Spring Festival.
As shown in Figure 5b, taking Beijing as an example, the urban travel intensity is low during the Spring Festival (from 25 January 2020 (the first day of the first lunar month) to 8 February 2020 (the 15th of the first lunar month, Lantern Festival)), whose level of work resumption generally assumes as zero. NTL work is the predicted NTLs radiation at the time of full resumption of work in 2020. Elvidge et al. [38] found that night time lights in China have been growing for several decades. We assume the same annual growth in night time The calculation method of the work resumption index, based on urban travel intensity, is consistent with the NTLs method. It is shown as follows (4): where UTI t is the average urban travel intensity on weekdays of week t. As shown in Figure 5, urban travel intensity is significantly different on weekdays and weekends. For example, urban travel intensity is significantly lower on Saturday, 15 February and Sunday,16 February 2020, than on 10 to 14 February 2020 (weekdays). So, only the weekday data were selected. UTI holiday is the average urban travel intensity during the Spring Festival. UTI work is the average urban travel intensity of normal working days before the Chinese New Year in 2020, because of the lack of data to predict the intensity of urban travel when work fully resumes in 2020. VIIRS DNB data can reflect economic activities. Generally, the brighter the area is, the more economic activity there is [54]. Most of the travel of residents on working days is related to commuting. NTLs and urban travel intensity data reflect the work resumption level from different angles, so the average work resumption index obtained by NTLs and UTI is used to improve the accuracy. The final work resumption index is shown in Equation (5): where WRI NTLs is work resumption index obtained by night time lights. WRI UTI is work resumption index obtained by urban travel intensity. Verify the results. The studies of work resumption lack continuous and authoritative data during the epidemic. Therefore, how to evaluate the effectiveness of the measurement method becomes a major difficulty. Two methods were adopted to solve this problem: (1) Compare with official statistics. The work resumption rate of the enterprises above designated size is the only official data of the resumption rate. The validity of the method can be demonstrated if the work resumption index has the same trend as the official data and is lower than the official data. This is because the rate of return to work is lower for small and medium-sized enterprises than for large firms [55].
(2) Give more information about whether it is consistent with the facts. During the resumption of work and production, the government issued a series of policy guidance and promotion. If our results are consistent with the time required by relevant policies, the accuracy of the method can also be verified.

Experimental Results
First, the urban travel intensity around the Chinese New Year in 2020 and 2019 is shown in Figure 5b. In Beijing, the intensity of urban travel increased significantly on the seventh day of the Chinese New Year in 2019 (11 February 2019, corresponding to 31 January 2020 according to the lunar calendar), indicating that some people returned to work, while in 2020 it is not until the 17th day of the New Year (10 February 2020) that work begins to resume. The intensity of urban travel had resumed to the pre-Spring Festival in the fourth week of Chinese New Year 2019, while it remained low in 2020. So COVID-19 has seriously affected the resumption of work after the Spring Festival holiday. In addition, the intensity of urban travel is higher in 2020 than in 2019 before the Chinese New Year. Because China is constantly developing. Beijing as the capital is changing day by day, attracting a large number of workers every year. The activity of city residents is rising.
The top 21 cities were selected to calculate the work resumption index, according to the ranking of the emigration rate before the Spring Festival holiday. These cities have many large factories and these areas are economically developed, which means the demand for work resumption is more urgent. However, the epidemic situation makes it more difficult and thus, the estimation of the level of work resumption in these cities is more significant. In this study, the 21 cities were divided into three parts: provincial capital cities, Yangtze River Delta cities and Pearl River Delta cities. Figure 6a-c shows the folding line chart of the weekly work resumption indexes for 21 cities based on NTLs. Since VIIRS DNB images have a lot of gaps after preprocessing, some cities have vacant values for a period. Overall, the level of work resumption is roughly increasing gradually. However, the growth in some cities is not quite smooth, but constantly fluctuating. The fluctuation amplitude of the provincial capital cities is smaller than that of the Yangtze River Delta cities. Due to the different angles of the NPP-VIIRS satellites at different times, the DNB radiation has anisotropy [56]. The existence of oscillation is reasonable. However, there are some anomalies in Figure 6b. It is obvious that the work resumption index in the Yangtze River Delta cities suddenly increases in the second week. Because most of these cities are coastal cities with many ports. Large backlogs of orders make trade busy, which can lead to a sharp increase in NTLs luminous intensity. Next, the specific work resumption indexes of these 21 cities were analyzed. As shown in Table 1, from the first week after 9 February 2020, some cities began to resume work with different intensities, but the overall trend was low, even some of them were negative. Because the work resumption rate was assumed to be zero during the Chinese New Year, whereas night time lighting changes during holidays [29]. By the end of February, the third week, resumption of work indexes in different cities had a wide range. Among the provincial capital cities except Wuhan, the work resumption index of Beijing, Zhengzhou and Xi'an were relatively low, only about 17%, while Changsha reached 45.31%. For the cities in the Yangtze River Delta, the work resumption indexes were higher than that of the capital cities ahead. The lowest was Nanjing, 37.30% and the highest was Ningbo of 51.18%. In addition, the work resumption indexes in cities of the Pearl River Delta were slightly higher than that of the Yangtze River Delta. In Shenzhen, Foshan, Dongguan and Zhongshan, the work resumption indexes reached 55% or more. Among the 20 cities except for Wuhan, the highest work resumption index is Zhongshan 65.72% and the lowest is Xi'an 15.93%.
By the end of March, the work resumption indexes in most cities were over 70%. The indexes in the Pearl River Delta cities were about 80%, while the Yangtze River Delta cities have reached 90%, or even fully resume work. However, the work resumption index in some provincial capitals is still relatively low. Beijing, Zhengzhou, Kunming and Xi'an only recovered to about 58%.
Compared with the results of 21 cities, the speed of work resumption in economically developed coastal cities, such as the Pearl River Delta and the Yangtze River Delta, is faster than that of the mainland provincial capitals, especially at the end of February. Because the coastal cities have many large factories, enterprises, etc. and undertake a lot of import and export trades, they are more urgent to return to work. By the end of March, the gap between the inland city (such as Tianjin, Changsha, Chengdu, Chongqing, etc.) and the coastal city has been significantly reduced. However, the work resumption indexes were relatively low in Beijing and Zhengzhou, due to the severe epidemic situation and strict control policy. Beijing, as the political and cultural center of the country, took more cautious measures to resume work and production. The Beijing government required no more than 50% of employees of staff-intensive enterprises to be present on 24 February [57]. Henan Province is adjacent to Hubei Province and has a large population movement. As of 12 March 2020, Henan has the 3rd highest cumulative confirmed cases nationwide, after Hubei and Guangdong [58]. It had taken much more time until the full resumption of work.
As the city with the most serious epidemic situation in China, Wuhan is the last one fully recovered and the work resumption index has been basically zero due to the implementation of the lockdown policy. Until 11 March, the prevention and control headquarters of Hubei Province issued a notice to the society that enterprises should resume work and resume production no earlier than 24:00 on 20 March. Therefore, in the last week of March, Wuhan's work resumption index had increased to 12.72%.
Since some areas of China, especially the southern and eastern coastal areas, are often covered by clouds, there are many gaping holes after preprocessing, which makes it impossible to calculate the work resumption index of the whole province precisely. Therefore, only seven provinces were selected for analysis (Table 2 and Figure 7).  From Figure 7, it can be also observed that the level of work resumption has been steadily improved. In the week after 9 February, most areas have begun to return to work.
In late February, the degree of resumption of work varies between different provinces. Jiangsu and Shandong had a higher work resumption index, while the index of Hebei was the lowest, i.e., 14.24% and other provinces were about 25%. In Early March, the gap of work resumption in each province had decreased. The highest work resumption index was Jiangsu, 61.79%, while the lowest still was Hebei, 35.83%. By the end of March, the work resumption in Jiangsu and Shandong has recovered to about 90%, while other provinces only were about 60%, such as Hunan, the lowest rate of 57.07%.
At the provincial level, we still get the same conclusion as before: the rate of returning to work in coastal such as Jiangsu and Shandong are higher than that in inland areas.

Verifing Results
Compare with official statistics. Compared the resumption of work index in 8 provinces and municipalities with the level of enterprises above designated size resumption issued by the national development and Reform Commission, it can be seen the trend of work resumption in all regions is the same as that in the official statistics, shown as Figure 8. Small and medium-sized enterprises are struggling to survive [59] and they are recovering more slowly than larger firms. The work resumption index obtained by our method estimates the overall level of resumption of work. It can be seen in Figure 8 that the index is also lower than the official data, which proves the validity of the proposed method. Furthermore, the gap between the large and medium-sized enterprises is constantly narrowing. This indicates that small and medium-sized enterprises may have returned to work and production and the economy is recovering step by step during this period. Verify with facts. In addition to Hubei Province, other regions also have successively issued notices that all kinds of enterprises to resume work no earlier than 24:00 on 9 February, except for essential enterprises [60]. Therefore, the work resumption indexes from 10 to 16 February should be the minimum. The analysis results agree with the real situation. In Hubei, the epidemic situation had been effectively controlled in February after unremitting efforts. The number of newly diagnosed cases in Wuhan by March had also been greatly reduced and people's lives were gradually restored. As previously mentioned, the government required Hubei to resume work no earlier than 20 March. In the results, the work resumption index in Wuhan had been at a low level and increased significantly in the last week of March, which is also in line with the actual situation.

Discussion
We calculated the work resumption index based on night time lights and urban travel intensity. The 21cities and seven provinces were selected as examples for discussion, as the southern areas and eastern coastal areas are mostly covered by clouds and the northeast area is covered by ice and snow in February and March. These regions have many abnormal pixels, which cannot reflect the NTLs changes in the whole region. The method we propose may not be suitable for areas covered by clouds or snow for long periods. In addition, it can be seen an anomaly in the week 2 work resumption index based on luminosity in Yangtze River Delta cities and Wuhan. Developed light industry and foreign trade in the Yangtze River Delta cities may cause a sharp increase in the luminosity of NTLs. The epidemic in Wuhan is most severe in February. The control of the epidemic and the delivery of medical supplies and living materials make the night time lights change.
The NPP-VIIRS passes by at around 1:30 a.m. local time. The VIIRS data may do not fully present the activity after working hours as the entertainment venue is still closed. However, the night time lighting in the commercial area can reflect the degree of resumption of work in the commercial area. As long as the lighting changes are related to the work resumption, whether, in commercial or industrial areas, or other areas, we think it is helpful. In the study, only the night time light images of non-residential areas were extracted to improve the estimation accuracy. However, the spatial resolution of VIIRS DNB data is 500 m, which is relatively high. There may be mixed (i.e., residential and non-residential) areas in a pixel. Although it is impossible to avoid this situation, our method may be more effective than directly using the night time lighting data of the whole city to estimate the reworking level [43]. In the future, we expect to use higher resolution night time lighting data to delineate a more accurate NTLs image in non-residential areas.
Due to technical limitations, the VIIRS DNB data lack sensitivity to the blue light spectrum [61]. The VIIRS DNB radiation sensitivity is insufficient for the identification of all outdoor light sources, as the spectrum below 500 nm is emitted with high ratios from modern, efficient lighting such as LED-road lighting or Xenon vehicle lighting. However, during the resumption of work, apparent changes in night time lighting were observed. The VIIRS DNB data are still feasible as a proxy to monitor the resumption of economic activity levels [41]. In addition, the VIIRS DNB data can be obtained free of charge for daily worldwide night time light images, which are widely used in night time light research [61]. We also expect to use multispectral sensors covering the blue to the NIR range to study urban lighting more effectively in the future. Artificial night time skyglow as a supplement to night time light remote sensing data is affected by air quality [62,63]. Air pollution decreases due to COVID-19 city lockdown [64,65]. However, to our knowledge, the effect of air quality on artificial skyglow has not been quantified. Similarly, no studies have determined the specific contribution of artificial skyglow to night time light remote sensing data. Only studies suggest that the diffuse light observed around cities is a direct observation of the composition of urban skyglow that scatters upward [66].
Baidu Maps has only released urban travel intensity data for China from January to May 2020 and during the National Day holiday. So, our method can only get the resumption index for each city in China during that period. When extending to other countries and regions, night time light data can be considered combined with local population travel data.
The results were compared with the relevant data to verify the effectiveness of the calculation of the work resumption index. However, the methods only verify the effectiveness of the changing pattern. The effectiveness of a single independent value is not verified; for example, it is impossible to judge whether the work resumption index 55.63% of Beijing is accurate.
Unfortunately, the work resumption index only represents the level of offline work resumption. Some companies worked from home and the actual level of return to work may be higher than our results. In the future, more datasets will be used to estimate the trend by quantitative analysis. Furthermore, the relationship between the work resumption index and other data such as the regional economy, the number of migrant workers and the severity of the epidemic situation will be considered in the future to analyze the influencing factors of the resumption of work. NTLs has been demonstrated to have a close relationship with the economy [54,67]. It can be applied to the investigation of socioeconomic conditions after a pandemic.

Conclusions
To address the lack of continuous resumption rate data under the epidemic, we proposed to analyze the work resumption level by combining night time lights data and urban travel intensity data. Considering the gap between the recovery of night time light intensity and the resumption rate, the images of residential areas were excluded. Meanwhile, NTLs data were pre-processed, such as lunar radiation correction, to obtain as much valid data as possible. To improve the estimation accuracy, only weekday urban travel intensity data were used for the study. We found that the COVID-19 epidemic severely impacted the resumption of work in China after the Spring Festival. Work started to resume everywhere from 10 February and the index of resumption gradually increased. By the end of March, the index of resumption of work in most areas exceeded 70% and some cities have achieved complete resumption of work.
In addition, the work resumption index varies greatly from region to region. Generally, the process of work resumption in coastal areas is faster, such as cities in the Yangtze River Delta and the Pearl River Delta, while the process is relatively slow in inland cities such as Beijing and Zhengzhou. Wuhan began to resume work until the last week of March. Local governments should formulate different policies according to the actual situation. Each company must go through prevention of epidemics before resuming work to avoid the gathering of infections among employees.
The results were verified by official statistics and facts, which demonstrate that remote sensing images of night time lights and urban travel intensity data can be effectively used for resumption studies under the impact of the epidemic. This not only enriches the applied research of night time lighting data but also provides new perspectives on the socio-economic impact of the epidemic. The process of recovering social production and life can provide a reference for the local government to evaluate the implementation effect of the policy and the extent of the loss of the national economy during the epidemic. Government statistics have a lag. Some media reports lack credibility. Our results can provide a reference for the government to make timely changes to the resumption of work and prevention policies. In addition, the combination of remote sensing data (night time lights) and ground-based statistics (urban travel intensity) greatly improves the accuracy of the results.
Since the night light data can be affected by other factors easily, such as cloud and moonlight and snow, a preprocessing step should be done for the daily NTLs image VNP46A1. The lack of data due to the cloud also limits the application of daily night time light data. In the future, we look forward to using the new product VNP46A2 [45], which provides BRDF-Corrected NTLs, to do research. In addition, we will compare with the resumption level of China in 2021.
In conclusion, this study has important implications for the estimation of the work resumption rate, which provided a new method to dynamically estimate the level of work resumption using night time lighting data and urban travel intensity. In addition, this study may be extended to other countries and regions to estimate the economic impact of the epidemic.