s2fieldsofstudy: Business Economics

text:
Impact of Green Intellectual Capital on Sustainable Green Banking: Moderating Role of Competitive Pressure

Purpose: Adoption of an environmentalist perspective in banking operational base through social, economic, and environmentally sustainable activities becomes debatable for firm performance. The study aims to examine the impact of green intellectual capital on sustainable green banking and the moderating role of competitive pressure between them. Design/Methodology/Approach: The study surveyed by using self-administrative questionnaires from the bank employees of Rawalpindi and Islamabad. The data was collected from a sample size of 351 by using a random sampling technique. The measures of Green Intellectual Capital (GIC) are encompassed three elements as Green human capital (GHC), Green Structural Capital (GSC), and Green Relational Capital (GRC) comprising 18 items (Yusof, Omar, Zaman, & Samad, 2019) and moderator competitive pressure comprising of 7 items was adopted (Sophonthummapharn, 2009) . Findings: The result of the study shows that green intellectual capital positively contributes toward achieving sustainable green banking. Competitive pressure moderates the said relationship in the context of the Pakistani banking sector. The finding of the study implies that banks in Pakistan implement green activities because of the competitive force of the external environment and achieve both sustainability in terms of environmental protection and less amount of carbon print. Therefore, banks have more focus on the use of green intellectual capital containing green human capital, green structural capital, and green relational capital than the banks achieve sustainable green banking. The study provides a practical implementation for banks to move towards sustainable green banking.

Introduction
Green banking is gaining tremendous importance across the world and banks are taking initiatives toward the protection of environmental degradation through green policies and sustainable activities (Bukhari, Hashim, & Amran, 2019). Green banking is also prospering in developing nations such as Pakistan. In this context, the State Bank of Pakistan has given guiding principles for Green Banking in 2017 as per the recommendation of PEPA (Act-1997). The purpose behind this is the formation of Green Banking administrations to achieve sustainability (Siddiqui, Aisha, & Rasheed, 2019). Moreover, the banks achieve this sustainability by showing concern for environmental protection. The green management system enables the banks to provide their customers with green products i.e. the products that fulfill their needs as well as satisfy their concerns toward environmental protection to accomplish success in both ecological and financial ways (Chang & Chen, 2012).
Green intellectual capital may prove as a great element to help firms in boosting their market share by viably using human, basic, and social capital. Literature supports that the accomplishment of sustainability is largely dependent on intangible assets than tangible assets i.e. human assets, knowledge dissemination, and effective utilization of structural resources (Allameh, 2018). Therefore, organizations need to give more attention to human resource management as banks can achieve sustainability when employees show concern for environmental protection (Yong et al., 2019a).
Numerous researchers have examined the relationship of green intellectual capital with business sustainability, knowledge sharing, social capital, and productivity in various contexts other than banks (Yusof, Omar, Zaman, & Samad, 2019;Cavicchi & Vagnoni, 2017), and supports that green basic and green social capital has an optimistic bond with the sustainability of different businesses. On the other hand, different studies show that green intellectual capital and more concern about the environment help to increase corporate image but affect the profits to some extent. Others examined that corporate sustainability performance is a multidimensional idea affected by employees' behavior, individual managerial cognition, knowledge distribution, and relation with stakeholders ( Yusliza et al., 2020;López-Gamero et al., 2011). Moreover, the human capital interactions and communication within banks with an effective knowledge management process help the employees to work together to increase performance which is one of the intangible assets and a most important determinant of the knowledge economy. Hence, green intellectual capital with classification as; green human capital, green structural capital, and green relational capital has an incredible relationship with organization performance and can decrease or increase the performance of organizations.
Various researchers have investigated the relationship between green intellectual capital, organizational performance, and sustainability in manufacturing firms, or hotel industry, etc. however, limited studies have investigated the influence of green intellectual capital on green banking or sustainable practices in the banking sector (Yusliza et al., 2020;Chen, 2008;Allameh, 2018;Elberdin, 2017). Furthermore, very few studies have considered the moderating role of variables between intellectual capital and sustainability. Therefore, this paper is designed to add to the existing gap of knowledge by investigating the impact of green intellectual capital on sustainable green banking and by considering the moderating effects of competitive pressure.
The objectives of this study are; to examine the impact of green intellectual capital comprised of green human capital, green structural capital, and green relational capital on sustainable green banking. Furthermore, to determine whether the competitive pressure increases the relationship between green intellectual capital and sustainable green banking. The ever-increasing globalization builds pressure on banks to make policies and strategies to contribute to environmental protection. Moreover, people are also concerned about consumption and prefer environmentally friendly servers. The present study is designed to analyze the effects of various forms of Green intellectual capital on the sustainability of the conventional and Islamic banking sectors in Rawalpindi and Islamabad. Besides, this study aims to investigate whether the effects of green intellectual capital on sustainable green banking are moderated by the competitive pressure in the conventional and Islamic banking sectors in Rawalpindi and Islamabad or not.

Literature Review
The propagates of green banking adopt environmentalism as their operational base in banking activities. The adoption of green banking is not just a change in the operations of the bank, but also a cultural change in the banking sector. Green banking involves guidelines and policy-making for the restructuring of operations of the banks. Although all the operations of the banks remain the same there is an improvement in the operational activities attributed to the sustainable environment. Green banks will use less paper and concentrate on electronic transactions. Moreover, green banks are the organizations having environmental protective strategies that convert their structural capital into green structural capital with help of green human capital and made relationships with investors, customers, and suppliers based on green relational policies (Dočekalová & Kocmanová, 2016). Social sustainability refers to behaving right to employees and attracting them towards green activities to achieve environmental sustainability. Yusliza et al. (2020) highlighted the key roles of the organization to behave socially responsible and environmentally responsible to achieve sustainable economic performance, and also encourage human capital in organizations to behave green and become competitive in this highly competitive business world.
Intellectual capital and its importance have drawn the attention of many researchers. Intellectual capital along with environmental concerns is becoming a topic of interest (Yong et al., 2019b). There are two types of studies related to intellectual capital. One is intellectual capital management and the other is intellectual capital measurement. The former includes the management of assets like knowledge of intangible assets and capabilities of the banks to create a competitive advantage. Later one consists of gathering, compiling, and analyzing the non-financial assets (Elberdin, 2017).
However, Intellectual capital is a concept that is consist of different dimensions i.e. experience, capabilities, and practical knowledge for extracting value for organizations by providing nonphysical and non-monetary resources (Allameh, 2018). With an increase in environmental consciousness, more organizations are behaving responsibly towards green practices which is an effective step toward green intellectual capital development (López-Gamero et al., 2011). Green Intellectual capital is characterized as "a wide range of elusive resources containing abilities, data, and information, and so on for the insurance of nature at both individual and organization level within the company" (Chen, 2008b). Moreover, Liu, (2010) characterized Green Intellectual capital as the "aggregate of all green and natural information that helps the management of any organization to gain a competitive advantage". López-Gamero et al., (2011) argued that in most firms like manufacturing, banks, etc. sustainability depends on the future outcomes as compared to the current outcomes, and the recommended outcomes are achieved through knowledge, the main source of knowledge is intellectual capital in any organization (Yong et al., 2019b). Therefore, based on the above discussion following hypothesis has been proposed; H1: Green intellectual capital significantly impacts sustainable green banking practices of commercial banks in Pakistan.
The literature presents three dimensions under green intellectual capital among them Green human capital is important for any organization to achieve competitive advantages, as environmental degradation is increasing day by day and need a quick response from banks and other organizations to develop protective measures for the environment (Fernando et al., 2019). Resource-Based View Theory depicts that banks as an organization consist of physical and non-physical resources i.e. humans as a resource for good performance (Malik et al., 2020), so by better utilization of human capital resources, the organization gets a competitive position among competitors (Barney, 2001).
However, in any organization, green human resources embraced green practices such as working on a paperless approach, carbon footprint reduction, making surrounding green by foresting awareness, initiating friendly environment activities, and waste management (Yong & Fawehinmi, 2019). Chen (2008) describes green human capital as the employee's abilities, aptitudes, experience, duty, inventiveness, and endeavors for the protection of the environment. Human capital is linked with the procedures such as enhancing their capabilities through training and educating the employees that enhance the abilities, expertise, and knowledge of the employees ultimately leading to satisfaction of employees and the improvement in the operations of the bank (Scarpellini et al., 2017). Banks that efficiently utilize their intellectual capital can perform effectively (Wang et al., 2011). According to the Resource-Based View, theory resources must be rare, valuable, and non-substitutable to achieve competitive advantage and to exploit different opportunities (Barney, 2001). Based on the above discussion following hypothesis has been proposed.
H2: Green human capital significantly impacts sustainable green banking practices of commercial banks in Pakistan.
Previous research pointed out structural capital as the knowledge that is institutionalized in the entire organization's routines, structure, and culture in the form of databases, technology, etc. (Carlos & Martos, 2012). These intangible assets are valuable for the banks. It is also known as organizational capital that is rooted in the organization and this capital cannot be taken away if the employees leave the bank (Cavicchi & Vagnoni, 2017). Chen (2008) characterized structural capital as " the set of commitments, capabilities, information, databases, technologies, management philosophies and institutions, processes to operate, culture, and image which comes under intangible assets". If this capital dimension comes and works for environmental protection in banks, it becomes the green structural capital.
Moreover, green structural capital and environmental protection are associated with each other (Jardon & Dasilva, 2017). One of the elements of structural capital that are IT molds into green hardware, software, networks, and IT system based on environmental protection, making the structural capital the green IT capital (Chuang & Huang, 2018). Based on the above discussion following hypothesis has been proposed.
H3: Green structural capital significantly impacts sustainable green banking practices of commercial banks in Pakistan.
Literature supports relational capital as "banks or organizations made a relationship with their suppliers, customers, partners, and network members to achieve competitive advantages" (Chen, 2008a). Barr (1988) gave the new "stakeholder view" for the organizations as different managers and management scholars found distinct interdependencies and interconnectedness between employees, investors, customers, and infirm communities because of network efforts and relationships that lead toward wealth maximization. According to the stakeholder theory, the connection among an association's workers with speculators, clients, and suppliers leads to competitive advantages (Donaldson & Preston, 1995).
Moreover, stakeholder associations are significant for financial benefits and are crucial for distinct issues at distinct times (Kianto et al., 2013). Green relation capital as associated with the suppliers in supply chain management asserts that environmental perspectives of the supply chain play a significant role in sustainability (Jabbour et al., 2019). Therefore, as the social responsibilities of different organization are increasing with time because of the issue of globalization in the world, relationship capital that is intangible asset help to achieve sustainability (Eweje, 2014). Association with customers and investors is also taking concern in fields of different organization and banks, customers behavior about environment protection now matters a lot as compared to the concern towards price, product, and service that is crucial for sustainable green banking achievement in the banks and sustainability achievement in the different organization (Luthra et al., 2016). Based on the above discussion following hypothesis has been proposed.
H4: Green relational capital significantly impacts sustainable green banking practices of Pakistani commercial banks.
Due to intense competition in the banking industry, banks are focusing on building a competitive image by creating customers' awareness about the efforts of banks in environmental protection, by participating in activities related to showing themselves more responsible towards the environment, customers, investors, and other stakeholders. Based on the above discussion following hypothesis has been proposed.

H5:
Competitive pressure positively moderates the relationship of Green intellectual capital with sustainable green banking.

Methodology
The study population encompasses 3600 employees of all banks in the area of Rawalpindi and Islamabad. The sample of the study is comprised of 350 bankers. Data from primary sources for this research is gathered by utilizing a 5-point scale that's a Likert scale survey. The measures of GIC encompassed three elements as GHC, GSC, and GRC were adopted. (Yusof, Omar, Zaman, & Samad, 2019). Comprising the 18 items and moderator that is competitive pressure comprising 7 items was also adopted (Sophonthummapharn, 2009). The measurement constructs are attached at the end of the annexure. The data is analyzed using SPSS (V.23.0).  Correlation measure the quality of direct linkage among factors. The above table mentions a diagonal value of 1 shows, a perfect correlation between variables. The value of correlation among SGB and GHC is 0.748 at the significant level of 0.01, and the significance of these variables is 0.000 which means the relation between these two variables is significant as (r=.748**, p<.01). Correlation value between SGB and GSC is 0.773 and significance value is 0.000 as (r=.773**, p<.01). So, these two are significantly positively correlated. The value of the correlation between SGB and GRC is 0.783 and the significance value is again 0.000 as (r=.783**, p<.01), so they also are positively correlated. Moreover, SGB and CP are also correlated at 0.822 with a 0.000 significance value or (r=.822**, p<.01). Even though the variables are correlated with one another, the collinearity statistics mentioned below clearly indicate that there is no multicollinearity issue in data. To test the first four hypotheses, regression analysis has been conducted. In the second column the R (.825 a ), demonstrates an association among predictors and dependent variables while R 2 (.681), is the explanation of variance. So, by the illustration of this statistic, both variables IVs and DV are mutually correlated. Moreover, independent variables i.e. GRC, GHC, and GSC explains a 24.75% variation in the dependent variable which is SGB (sustainable green banking). The results are given F value (246.6), which demonstrates that independent variables with a significance value of 0.000 significantly explain good variation in the dependent variable i.e. sustainable green banking and both have a significant linear relationship between them. From the demonstration of results given in the above table, the t-value for GHC is 4.476, for GSC is 4.344, and for GRC is 6.621 with a significance 0.000 illustrating the positive impact of GHC, GRC, and GSC on sustainable green banking. Furthermore, these variables are the strongest predictor of sustainable green banking. So, on basis of statistical results, the hypothesis is accepted.

Results and Analysis
Moreover, the last column in the above table shows multicollinearity. It checks the similarity between the independent variables and is examined by a variance inflation factor (VIF) and tolerance (Hinton, MCMurray, & Brownlow, 2014). A small tolerance value indicates that the variable under consideration is almost the perfect linear combination of independent variables. The values of tolerance in this study results are between 0-1 showing the accepted linear combination of variables. Furthermore, VIF measures the impact of multicollinearity among the variables in the regression model and its value should lie between 1 and 10. VIF values in the results of the study are between 1 and 10 depicting that there is no multicollinearity among the variables. Hence, concluding that the regression model is good with no multicollinearity and a perfect linear combination of the independent variable.
Next, it is measured whether competitive pressure moderates among green intellectual capital & sustainable green banking, an analysis of moderation (Hayes, Model 1) is utilized, and entered variables in the model are; sustainable green banking, green intellectual capital, and competitive pressure. The results for multiple regression through process by Andrew F. Hayes by using model 1 are given in the above table, in which the R (.8443) value shows the correlation between variables, R 2 (.7128) value shows 71.27% of the variation is caused by predictor variable. Moreovthe er, F value is (65.0532) at a significance value of 0.000.  Table 4.8 show that competitive pressure moderates the said relationship in the context of the Pakistani banking sector (t=-2.4901). The finding of the study implies that banks in Pakistan implement green activities because of the competitive force of the external environment and achieve both sustainability in terms of environmental protection and less amount of carbon print. The research investigates the influence of green intellectual capital on sustainable green banking by averaging the score of its three extents: GHC (green human capital), GSC (green structural capital), GRC (green relational capital), and found a noteworthy positive relationship of these with sustainable green banking. Consequences also indicate that GIC has a significant positive impact on sustainable green banking and demonstrates the significant variation in the outcome variable. The research results are congruent with former research (Yusof, Omar, Zaman, & Samad, 2019). Yusof et al., (2019) investigated the influence of green intellectual capital on performance and found a positive impact between them likewise in this study green intellectual capitals have an optimistic impact on sustainable green banking.
The finding of this research also demonstrates that more human capital in banks with green behavior may lead to sustainable green banking. This finding is supported by literature showing the positive impact of the independent variable on the outcome variable (Chang, 2013;Malik et al., 2020;Yusof, Omar, Zaman, & Samad, 2019). The findings also support the significant impact of (GSC) green structural capital on sustainable green banking which is in line with the work of Yusof, Omar, Zaman, & Samad, (2019) who conclude the positive impact of green structural capital as the dimension of the capital of green intellectual on performance in the manufacturing industry. The significant positive result of green relational capital as the dimension of green intellectual capital toward sustainable green banking is supported by the work of Yusof, Omar, Zaman, & Samad, (2019).
The findings of the study also support that, to achieve sustainable green banking, banks got pressurized by other banks to do green activities to become sustainable because according to the State Bank of Pakistan guidelines, green banking activities are not mandatory (Park & Kim, 2020), these are voluntary, banks follow these guidelines because of competitive pressure. This finding is in line with the work of Agrawal & Sharma (2015) who argued that to build a competitive image in the industry, most organizations or Banks are moving towards or showing more concern about environmental protection, participating in activities related to showing themselves more responsible towards the environment, customers, investors, and other stakeholders.

Conclusion and Recommendations
The study revealed that green intellectual capital has a significant relationship with sustainable green banking. The green intellectual capital variable depicted the 24.75% variation in sustainable green banking and the model is significant at 0.0000 and perfectly fit.
From the findings, we can conclude that green human capital is the strongest predictor of sustainable green banking hence suggesting that more humans with a green mindset would enhance the possibility of achieving sustainable green banking. Green structural capital is the second strongest predictor of sustainable green banking as more the structure of banks is flexible to adopt technology and parallel to do green activities in banks will more helpful to achieve sustainable green banking and achieve efficiency and effectiveness with environmental protection. The results demonstrate of green relational capital stand in the relation of banks with their all stakeholders especially employees in banks enhances knowledge sharing and creates an environment to transfer ideas freely.
The research confirmed the moderating impact of competitive pressure on the relation between green intellectual capitals towards sustainable green banking through multiple regression models. In other words, banks got pressurized by other competitor banks to enhance green activities. The findings of the study suggest that banks of Rawalpindi and Islamabad should focus on building green human capital with structure modification towards green activities and built relations with all employees especially to harness more sustainable green banking activities.
Green human capital plays a positive role in green sustainability in banks. So, all policies of banks about sustainability should be clear, objective, simple, understandable, and communicated to all staff. Green structural capital is also a great predictor of sustainable green banking. So, banks should be made structure flexible to adopt or pursue more green activities by reducing a large amount of bank waste in the environment i.e. paper litter. Strategies and policies should be designed as they take into justification the following imperative factors: variations in the corporate atmosphere for green banking, fluctuations in tools (IT) for sustainability, steadiness with all objects containing facts for sustainable green banking, training needs, and solutions for human capital for building relations between them, regularity and levels of efficiency and effectiveness in the overall structure of banks, cost and benefits, time and so on.
of green intellectual capital.


s2fieldsofstudy:
Business
Engineering
Computer Science

text:
Performance analysis of cost effective multi-hop Time Sensitive Network for IEEE 802.1Qbv and IEEE 802.1Qbu standards

Time-Sensitive Networking (TSN) is an emerging technology, which enables advancements in applications like industrial automation, automatic vehicle-to-vehicle communication, etc. which hosts various time-critical applications, ensuring bounded latency. The novel idea of this paper is to present OMNET++ simulation-based complex multi-hop TSN network using the native VLAN concept to bring out a cost-effective model for inter-TSN and Intra-TSN domains. This paper investigates the performance of hybrid IEEE standards, ie.IEEE 802.1Qbu and IEEE 802.1Qbv standards. The simulation results show that the combination of these standards, when effectively scheduled in switches will reduce the latency by 3.3 µseconds in time-critical applications. Further, it is observed that in Best effort traffic, frame loss is also very less in the range of 2-5 frames out of 1385 frames. These results certainly will be of great value in more complex TSN deployments.

Introduction
Various Ethernet-based industrial 4.0 use cases [1], emphasize the need for efficient communication methods. The Time-Sensitive Networking(TSN) standards assert the protocols and mechanisms to scale and control the Ethernet network[2] [25]. TSN finds applications in Industrial automation, Electrical power generation and distribution, Automatic vehicleto-vehicle communication, Professional audio-video studios, Cellular radio, Multimedia communication [3]. The standard Ethernet network fails to provide the bounded latency and may result in data loss due to legacy scheduling mechanisms when both Operation Technology (OT) and Information Technology(IT) data are involved. OT data is high-priority data that involves hardware and software to monitor and control events with in an industry. IT data is noncritical enterprise-level data within industry. Hence to handle both IT and OT data IEEE has proposed various standards such as IEEE 802.1AS (gPTP, an extension of PTP protocol), IEEE 802.1Qbu (Frame pre-emption), IEEE P802.1Qci(traffic filtering and policing), IEEE 802.1CB redundant frame transmission, IEEE 802.1Qcc (configuration aspects), etc. [4] [24] [26]. In TSN, based on the type, data is classified into various classes and handled by algorithms specific to traffic classes. Data is classified into 8 classes, which correspond to the 8 priorities set by the 2 PCP value of the VLAN tag. The details of traffic classes and the priorities assigned to the class are presented in [5] [6] [7]. To simulate TSN topology and check the performance of the network simulation tools like OMNET's CoRE4INET [8] [9], Riverbed [10], NeSTiN g [11][5] [27], NS-3 [12], TSNS [7],DeNS [13]are available. Few papers show the validation of simulation work with hardware setup [28]. We choose to use the NeSTiNg framework of OMNET++ 5.5.1 and INET 4.1.2. We have extended the work carried in the NeSTiNg simulator by considering a complex network with multiple switches and implementing Virtual Local Area Networks( VLANs) to create TSN domains.

Details on IEEE 802.1Qbu
This standard is also known as frame pre-emption mechanism and it specifies the port utilization and low latency data transmission of real-time and time-sensitive data. The pre-emption mechanism enables the switch to pre-empt the non-time critical data and transmit the high priority time-critical data when they are to be transmitted through a common port. This may introduce delay in low priority data but guarantees the bounded latency of time critical data. Each port in a TSN switch supports 8 queues and hence 8 traffic classes with different priority levels. The traffic classes are Scheduled traffic(ST), Audio Video Bridging (AVB) traffic with bounded latency and Best-Effort(BE) data with default priority. In our work,scheduled traffic with PCP value 7 is considered for which the schedule is generated offline in Gate Control List(GCL) [7] [14][15] [ 16], sensor data with the priority of 6, video data of priority 5 and the BE traffic with default priority or PCP value 0 is considered Which has the lowest priority. Each Ethernet traffic class has a priority and is mapped to either express or preemptible interface. Traffic class mapped to express Queue cannot be pre-empted by other traffic classes. Whereas non-express or preemptible class of traffic can be preempted by high priority frames. For a frame to be pre-emptible the minimum frame size should be 102 Bytes excluding header [7]. Recent studies show that a non-pre-emptive switch introduces the delay of 2336 µsecond by a less priority frame and with pre-emption, the same network experiences the delay of 1016 µseconds per 1000 Mbps switch [7].

Details on IEEE 802.1Qbv
This standard is also called time aware shaper. It mentions the GCL which enables the highest priority control data in the industrial applications to be delivered to with bounded latency. When the arrival of periodic time-critical data is known the GCL pattern can be efficiently designed. As the nodes and bridges in the TSN network can be time-synchronized using IEEE 802.1AS [5][17] [18] [19][20] [21], the 802.1Qbv can be effectively used for critical data delivery. We have assumed that each port of a TSN switch consists of 8 queues. Each port can be scheduled based on the traffic class. In a single port if GCL is 10000000 then the gate corresponding to Queue 7(when Queue numbers are 0 -7) is open and gates corresponding to 0-6 are closed. Hence for the entire duration of gating where GCL has this pattern the frame of queue 7 can only be sent through the switch and all the remaining data frames are halted in the switch. GCL changes periodically and this period is called the gating cycle. The same cycle repeats throughout the simulation duration. The gating mechanism ensures that only one queue gets access to the egress(exit) port. However, the transmission of non-ST data will stop before the time slot of ST data. This time slot or window is known as a protected window. The switch internals for GCL:10000000 is as shown in figure 1.
The time slot where non-time-critical data frames are allowed through the egress(exit) port is called the unprotected window and also an additional time slot or window during which no frame transmission is allowed and the egress port is in idle state is known as a guard window. The details on all these time window slots and switch gating cycle is shown in figure 2.

Literature review
Bello [4], this paper provides a detailed introduction of TSN standards which are highly relevant to industrial communication, and also provides the future direction of research. According to the author future scope for research are as follows: While looking at the configuration TSN network, scaling the network schedules with fault tolerance is to be addressed, there is a need for improvement in deploying the applications. Concerning the security of TSN applications, there is a need of detecting the threats early and proposing mitigation strategies. Simulations, Prototypes, Performance studies, use case demonstrations are highly required for the TSN solutions maturity.
Haris Suljic [5], does the performance analysis of TSN by considering IEEE 802.1Qbv, IEEE 802.1Qu, and Credit-based shaper. The simulation was carried out in OMNET++ 5.6.1 and INET 4.2.1 with the NeSTiNg framework. The author has considered a simulation duration of 3 minutes and a simulation cycle of 500 µseconds. Protected window of 17 µseconds and unprotected window of 360 µseconds and guard window of 123 µseconds. Parameters considered for simulation are end-to-end delay and link efficiency. 5 scenarios are considered for performance analysis. The first one is IEEE 802.1Qbv, here the BE traffic experience the packet delay and time-critical data has deterministic data delivery. The Second one is IEEE 802.1Qbu and it is used along with credit-based shapers. The conclusion of this simulation is Audio-video traffic is delayed by credit-based shaper, so frame pre-emption does not reduce the latency of frames with higher priority. The third scenario is using both frame preemption and time aware shaper. The Combination of these 2 reduces the delay of BE traffic whereas delay of deterministic data transmission is unchanged because of using proper gating mechanism. In scenario 4, Gating, Pre-emption, and Credit-based shapers are used. The combined result is frame pre-emption provides reduced end-to-end latency for event-triggered data and BE data experiences more delay compared to the previous scenarios. Scenario 5 is about an industrial in-car network with 14 end nodes and 2 switches. In this scenario frame pre-emption and gating mechanisms are used. Gating results in an unaltered delay in the data delivery and pre-emption will result in increased delay for BE traffic. The conclusion of this experiment is to look at efficiently and dynamically designing the GCL, also look at various combinations of these standards with various configurations to check the performance of the network in a more efficient way.
In [7], the aspects of Pre-emption are discussed in detail. The performance of the TSN network when there are multiple express data (The data that pre-empts less-priority data) is explained. The author develops a new simulation tool known as TSNS. This tool is used for simulation of the TSN switch behavior according to the IEEE 802.1Q amendments. The environment used is Microsoft's visual studio and code is developed using C language.
In [10], the author develops a tool to check how TSN configuration can be automated. The tool was developed to make use of OMNET's NeSTiNg platform. Researchers can use this extension to automate the configurations, extract the flows and check the performance of the network by using the GUI which is provided by the author. This helps us save time in configuring the parameters through XML files.
Time-aware shaper schedules the frames according to the predefined GCL list.In [15] Ramon Serna Oliver, et al, proposes a method of synthesizing the GCL based on an SMT solver. Aslo discussion on Time aware shaper, Pre-emption, and Credit-based shaper is done [15]. But, still, there is a need to do more analysis by taking various case studies into account. We need to look at trade-offs between various performance parameters and efficiently design the system looking at particular applications. Another motivation behind this paper is to explore various options that are part of the OMNET++ simulation tool for Time-Sensitive Network simulation and look at the options available, appreciate and also look for various improvements in the existing NeSTiNg simulation framework.
Escola [22], The objective of this work is to implement and validate the designed system that allows automatically configuring the TAS's scheduling parameters of the switches of a TSN network. For configuring TAS, IEEE 802.1Qcc proposes NETCONF, RESTCONF, and SNMP from which the author has chosen NETCONF. NETCONF enables us to install, delete and manipulate the configuration of network devices. NETCONF is a network management protocol, but it does not specify how to model the configuration. To tackle this issue, the TSN standards suggest using YANG. YANG is a data modeling language which is used by the NETCONF protocol. YANG is described in the RFC 6020.
Anna arestova [29], In this paper the analysis of Frame preemption(FP) and Time aware shaper(TAS) is carried out and it is compared with Strict priority scheduling(ST) by NeSTiNg framework of OMNET++ simulator. For simulation, a multi-hop, tree topology with multiple star configurations are considered which is comparable to an industrial automation network. The result shows that the TAS produces a large configuration overhead but performs the best and it is suitable for ultra-low latency requirements. FP can be combined with TAS for the efficient band usage. ST shows the least configuration overhead and is suitable for real-time applications with fewer millisecond cycles. The transmission offsets can be effectively scheduled to reduce the interference in the egress queue of a switch when ST and FP standards are used.  iii. Second floor: Contains switch "C" and 4 nodes. iv. Third floor : Contains switch "D" and 4 nodes. If on each floor there is a device that belongs to a specific TSN domain then we should go for topology mentioned in figure 5 for intra-TSN and figure 6 inter -TSN communication.

Proposed Work
The proposed inter-TSN and intra-TSN communication network are as depicted in figure  3 and figure 4 respectively. In the block diagram the color codes are used for identifying the domains. The devices with same color belong to one domain. We see such '4' domains in figure  3 and '5' domains in figure 4.
As mentioned in the literature survey, according to [5], there is a need to have further look into the configuration aspects of TSN protocols for more efficient data transmission. According to [4] lot of simulations, prototypes verification are required in TSN because many applications require improved versions of existing standards. Hence, we have checked how the combination of both these protocols will increase the efficiency of the communication instead of using them separately.  Figure 3 shows the block diagram of the intra-TSN domain network which has 4 TSN-enabled switches and each switch has 4 devices physically connected to them. Each device generates a specific type of traffic and we have scheduled the switch in such a way that each traffic class is handled effectively in the presence of TSN protocols such as Pre-emption and Gating. The configuration of switches, their ports, type of schedule, types of the algorithms used is mentioned in tables 2 and 3. The details on the TSN domains and corresponding nodes are mentioned in table 1. These TSN domains are the broadcasting domains or VLANs. 4 VLANs have been created and programmed the network such that the broadcast from a node in a VLAN is limited to only the devices present in that particular VLAN.

Details of TSN domains.
i. TSN Domain 1 is implemented by VLAN 20 which has a robotController that generates time-critical periodic data and broadcast the data to only robot1, robot2 and robot3.
ii. TSN domain 2 is implemented by VLAN 30 and includes a camera which generates a non-periodic data with priority 5 and broadcasts the data to CPD1(Camera Processing Device 1), CPD2, and CPD3.
iii. TSN domain 3 is implemented by VLAN 40 which includes the sensor as the source node that produces nonperiodic data with priority 6. The sensor data is broadcast to only the nodes in that specific VLAN, i.e. to actuator1,actuator2, and actuator3.
iv. TSN domain 4 is implemented by VLAN 60 and BETG as the BE Traffic Generator and broadcasts the data only to BETR1(BE Traffic Receiver), BETR2 and BETR3.

Details on traffic generated by all the nodes and frame sizes.
The robotController transmits minimum sized, network control and periodic traffic hence a frame size of 102 Bytes is chosen. This is the minimum size requirement for frame which is preemptable [7]. Similarly a maximum frame size of 1500 bytes is chosen for BETG and Camera which are similar to Best effort traffic. As the sensors used in real time applications generate the traffic lesser than BE traffic and greater than control traffic hence the sensor traffic chosen is 500 Bytes. Number of robotController frames considered during the simulation period is 138 frames. The number of frames can be increased by increasing the simulation duration. The number of frames sent and received by all the nodes are depicted in Figure 9 and Figure 10.
In the simulation process, the scheduling configuration for switches is written in an XML file and all the VLAN configurations are done in the .ini file of the NeSTiNg framework. The types of data generated by the nodes in the network, size of each data, periodicity, and their priorities are mentioned in table 2.

Inter-TSN domain/Inter VLAN communication
A new node named as newdevice is now added to Switch D. This belongs to a new VLAN named as VLAN 50.
In the inter VLAN communication we have assumed that there is a communication from robotController that belongs to VLAN20 to newnode that belongs to VLAN 50. Generally when communication between 2 VLANs is implemented by using an L3 router. But, here we  We have leveraged the native VLAN concept for inter-TSN domain communication. [23] We have explicitly tagged the native VLAN with a specific ID in which our desired TSN node is present. Example: Inter TSN domain traffic L2 routing from VLAN 20 to VLAN 50 Assume that a device is connected to switchD.eth [6] and it is mapped to VLAN 50. The egress traffic of switchC.eth [6] port with VLAN tag 20 will be mapped to VLAN tag 50. This is a native VLAN for this switch-D. Hence the Node connected to switchD.eth [6] gets the TSN traffic which demonstrates inter-TSN domain communication.

Results
According to figure 3 where the communication is within the VLAN. i.e. robotController to robot1,robot2,robot3. Or Sensor to Actuator1, Actuator2, Actuator3, and so on pre-emption along with Gating is applied. 17 µseconds protected window, 360 µseconds unprotected window and, 123 µseconds guard window is considered in one switch cycle. The latency in transmitting time-critical data is constant throughout the simulation time as shown in figure 11. This is possible because, in an entire switching cycle, the robotController transmits the data at a fixed time, and also it is periodic. By knowing this factor GCL vector can be efficiently designed to maintain constant delay in the case of robotController. Looking at the latency charts the deterministic data delivery is guaranteed for the destinations present after multiple hops also. The latency in transmitting sensor data to all its nodes varying with simulation time as shown in figure 12. This variation in the latency is due to the buffer capacity of queues in switch as well as other high-priority data transmission. Latency for transmitting camera data and BETG data to all the corresponding nodes is periodic as shown in figures 13 and 14 respectively.
The graph of frames sent Vs received by the source nodes to destination nodes of single-hop is shown in figures 9 and 10 for both cases. The robot1, camera1, and sensor1 nodes have 0 frame loss whereas for BETR1 loss is only 2/1385 frames.
In the Inter-TSN network, the high priority robotController data has the constant latency of 13.42 µseconds for the first hop(to robot1), 19.56 µseconds for the second hop(to robot2) and 25.7 µseconds for the third hop(to robot3). This is similar to the latency of the Intra-TSN network. The latency chart for all the remaining source nodes to their corresponding destination nodes in the Inter-TSN network follows the same periodic pattern as intra-TSN latency charts with small variations in the values.

Conclusion and future work
In this work, the hybrid IEEE standards 802.1Qbv and 802.1Qbu are used for scheduling the traffic in the switch. By this configuration, we can guarantee deterministic data delivery of time-critical applications and make sure that non-periodic BE traffic will have very little frame loss of 2-5 frames out of 1385 frames. This paper implements a new configuration of TSN domains and also a concept of native VLAN whenever Inter-TSN domain communication is required. This avoids the usage of a dedicated router interconnecting multiple TSN domains. Hence the approach we followed here is a cost-effective solution. The advantage of making multiple broadcast domains in a single network significantly reduces CPU cycles of switches. This avoids unnecessary broadcast of the traffic which is not destined to the nodes. As future work, the hardware validation of complex networks is necessary along with IEEE 802.1AS(Time synchronization) standard. Also proposing new scheduling algorithms to have efficient communication between time-sensitive nodes is required.


Exploring the Innovation Path of the Digital Construction Industry Using Mixed Methods

: To provide a theoretical basis for deepening digital reform in the construction industry and explore the innovation path of the digital construction industry, this research uses patent data for Guangdong Province to carry out explanatory sequence research. First, this research analyzes the dynamic evolution process of networks in the digital construction industry by using social network analysis. Then, grounded theory is used to conduct qualitative research to explore the innovation path of the digital construction industry. The results show that the scale of the innovation network of the digital construction industry is continually expanding and the digital carriers are becoming increasingly diverse. Inﬂuenced by the diversiﬁcation of digital carriers, the research theme of the digital construction industry is beginning to develop in the direction of intelligence. The ﬁndings indicate that the use of mixed methods improves the robustness of the results and that the quantitative research results are explained by the qualitative research results. This research not only contributes to information development in the construction industry but also provides a theoretical basis for deepening digital reform in the construction industry. explanatory the innovation pathway for the digital construction industry using social network analysis and grounded theory based on patent data for Guangdong Province. Several important conclusions can be drawn from the

Introduction
The application of digital technology has provided a new innovation platform for the development of the traditional construction industry, and the digital construction industry is now part of the digital economy. In the "Statistical Classification of the Digital Economy and Its Core Industries (2021)" promulgated by the National Bureau of Statistics of China, the digital construction industry is defined as the integration of digital technology and the traditional construction industry. As the digital construction industry is new, there are few studies on the topic, and there is also a lack of relevant studies on its development. Considering the digital transformation requirements of the construction industry, it is necessary to study the innovation path of the industry.
In recent years, many researchers have attached great importance to the application of digital technology in the construction industry. Benjamin Teisserenc and Samad Sepasgozar studied the adoption of blockchain technology through digital twins in the construction industry [1]. Diego Calvetti studied the future of sensored construction sites [2]. Li studied the advanced-technological UAV-based enhanced reconstruction of edges for building models [3]. The global demand for digitalization in the construction industry continues to increase. With the emergence of Industry 4.0, the rapid development of digital technology has pushed the construction industry into the digital age. In 2019, the global construction digitalization market was valued at approximately 9.8 billion US dollars, and this value is expected to exceed 29.1 billion dollars by 2027 With the increasing demand for infrastructure construction in emerging economies, such as China, Vietnam, and India, the demand for the digital construction industry in the Asia-Pacific region will soon bring the industry into the lead in the world, and infrastructure construction will bring additional opportunities for the development of the digital construction industry [4].
In the new era of digital transformation, the role of intellectual property has become increasingly prominent. Intellectual property, especially patent ownership, has become an important measure of the comprehensive strength of a region. To explore regional innovation capability, Zhuang and Wu measured the degree of association among Chinese universities, enterprises and the government in collaborative innovation with patent data [5]. Li took the number of patent applications and the number of granted patents for service invention as the Index of Innovation Output and analyzed the efficiency factors that influence differences in regional innovation ability across China [6]. These studies provide references for research on innovation in the digital construction industry with the aid of intellectual property patent data.
In this paper, patent applications in the digital construction industry are taken as the breakthrough point. First, this research explores the development of the digital construction industry using the social network method and constructs an innovation network for the industry. Based on this, we study changes in the network topology structure of the digital construction industry and determine the innovation themes. Furthermore, to explain the results obtained by the social network science method, grounded theory is used to carry out explanatory sequence research and generate an innovation theory path for the digital construction industry. This study reveals the development of digital technology innovation in the construction industry, explores the innovation path for construction industry information and provides a theoretical basis and practical experience for deepening digital reform in the construction industry.
The rest of this paper is organized as follows. Section 2 provides a literature review of the relevant research. Section 3 presents the research design and the research methodology. Section 4 presents the results and a discussion of future research directions. Section 5 concludes the paper by summarizing the main findings.

The Concept of the Digital Economy
Tapscott first proposed the concept of the digital economy in 1996 and focused on three aspects: the digital economy and its elements, the relationship between the internet and business and government, and the changing role of leaders [7].
The "G20 Digital Economy Development and Cooperation Initiative" clearly defined the digital economy as a series of economic activities. The digital economy takes digital knowledge and information as key factors of production, the modern information internet as an important carrier, and the use of information and communication technology as important driving forces [8]. The China Academy of Information and Communications Technology defined the digital economy as a new economic form in which digital knowledge and information are key factors of production, digital technology innovation is the core driver, and the modern information network is an important carrier. Through the deep integration of digital technology and the real economy, the digitalization and intelligence level of traditional industries are continuously improved and the reconstruction of economic development and government governance models is accelerated [9].
Unlike the G20 and China Academy of Information and Communications Technology, the National Bureau of Statistics of China regards data resources as a key factor of production in the digital economy. The National Bureau of Statistics of China defines the digital economy as a series of economic activities with data resources as a key production factor, modern information networks as an important carrier, and the effective use of information and communication technology as an important driving force of efficiency improvement and economic structural optimization.

Scope of the Digital Economy
Although Tapscott determined the nature of the digital economy, he did not provide a clear definition of its scope. E-commerce is at the forefront of the digital economy [10]. The U.S. Department of Commerce focused on the impact of IT and e-commerce on economic growth in its "Emerging Digital Economy" report, and IT and e-commerce have begun to be used as a classification basis for the digital economy [11]. The U.S. Census Bureau divided the digital economy into three parts: supporting infrastructure, e-commerce processes, and e-commerce transactions [12]. The Bureau of Economic Analysis divided the digital economy into three categories: digital infrastructure, e-commerce and digital media [13].
With the widespread application of information and communications technology, the boundaries of the digital economy have changed. Ahmad and Ribarsky concluded that information and communications technology belongs to the digital economy as a type of digital empowerment [14].
The National Bureau of Statistics of China determined the scope of the digital economy industry to be digital product manufacturing, digital product service industry, digital technology application industry, digital factor-driven industry, and digital efficiency improvement industry. The digital construction industry belongs to the digital efficiency improvement industry.

The Concept of Innovation
Innovation is the driving force of economic growth and the primary driving force of development [15]. It plays an important role in the progress of society and industry, especially in the era of the digital economy. The term "innovation" was first proposed in 1912 by Schumpeter, who argued that "innovation is the 'new combination' of new production factors and production conditions in the production system" [16]. Drucker defined innovation as the act that endows resources with a new capacity to create wealth [17]. Freeman and Soete defined innovation as a management system for building new engineering, new products and new technologies [18]. Innovation is a multistage process, the measurement of which can be divided into three parts: (1) measurement of innovation inputs, such as R&D; (2) measurement of intermediate outputs, such as the number of patented inventions; and (3) direct measurement of innovation outputs [19].

Digital Economy Innovation
A high level of innovation is an important feature of the development of the digital economy. In recent years, studies on digital economy innovation have been increasing and many research results have been published, including studies on digital transformation [20][21][22][23], innovation-driven development [24,25], high-quality economic development [26][27][28], and new generation information technology [29][30][31][32]. However, these studies have mostly been multiangle studies on digital economy innovation and have primarily been qualitative; there has been no explanatory sequence research combining quantitative and qualitative methods. Nor has there been a clear proposal or project with the digital construction industry as the research subject.

Digitalization in the Construction Industry
Digitalization is an important change driver in the construction industry. Puolitaival and Kähkönen discussed the nature and type of digital technologies used in construction management and explored digital technology-related competences; their research as a whole took a multimethod qualitative approach relying on the constructivist paradigm, and the research design had some exploratory features but was mostly descriptive [33]. Wang et al. adopted a sequential mixed qualitative and quantitative data collection and analysis approach to systematically identify, assess, and categorize barriers to digital transformation in the engineering and construction sectors. Their findings contributed to the body of knowledge on digital transformation in the construction industry and helped construction firms and government bodies improve their understanding of these barriers to digital transformation and put forward relevant policies and incentives, thus taking advantage of digital transformation benefits as a way to enhance construction project management [34]. Khahro et al. analyzed the possible benefits of and barriers to digital transformation and e-commerce in the construction industry and found that the maintenance of existing frameworks, stability and lack of software expertise are among the obstacles in the construction industry's use of e-business methods [35].

Patent Data Research
Patent data contain a variety of innovation information, including applicant information, patent technology classification, and citation information, which creates the possibility for multidimensional innovation research [36]. Based on patent data, international cooperation analysis [37], regional innovation capability analysis and comparative analysis [38] can be carried out. Patent data are often used as an important indicator when studying the impact of industrial policy on firms' innovation behavior. Some scholars have studied the impact of industrial policy on firms' patent output [39,40]. In addition, the citation information of patents can be used to study knowledge spillovers [41].
Patent data are widely used in the core areas of technology management [42]. From the perspective of technological innovation, patent data are an important resource for analyzing technological change and have become a measure of R&D output. Therefore, patent data are regarded as the end point of innovation. In the late 1970s, the OECD began to pay attention to patent data and developed the "Patent Handbook", which explained the relationship between patent indicators and other technological indicators, the methodological issues of using patent indicators, and the advantages of using patent indicators. It also discussed the link between patent indicators and innovation indicators.

Research Subject
According to "The Report on the Development and Employment of the Digital Economy in the Guangdong-Hong Kong-Macao Bay Area" (hereafter referred to as the Bay Area), which was released by the China Academy of Information and Communications Technology [43], the digital economy has been developing rapidly in Guangdong, totaling 4.9 trillion yuan in 2019 and accounting for 45.3 percent of Guangdong's GDP. The scale of the digital economy in Guangdong Province and the GDP of Guangdong Province are shown in Figure 1.  At the same time, the proportion of the digital economy to the total provincial GDP in Guangdong increased from 37% in 2016 to 45% in 2019. According to the "White Paper on the Development of China's Digital Economy (2021)" by the CAICT, the scale of the digital economy in Guangdong Province in 2020 was approximately 5.2 trillion yuan, accounting for 46.8% of Guangdong's GDP and ranking first in China. According to the "China Digital Economy Development Report (2022)" issued by the CAICT, the scale of Guangdong's digital economy in 2021 was approximately 5.9 trillion yuan, ranking first in China and accounting for 47.5% of Guangdong's GDP. Guangdong has become the high ground of digital economy development in China, while the development of the digital construction industry and its innovation network there are both highly representative and worthy of further study.

Research Step
Step 1: Data Collection For this research, we collected patent data for 9 cities, Guangzhou, Shenzhen, Zhuhai, Foshan, Huizhou, Dongguan, Zhongshan, Jiangmen, and Zhaoqing, in the Greater Bay Area of Guangdong Province from 2016 to 2020 and obtained an original database.
Step 2: Data filtering This research referred to the "Statistical Classification of Digital Economy and Its Core Industries (2021)", "International Patent Classification and National Economic Industry Classification Reference Table (2018)", "National Economic Industry Classification Notes (2017)", "National Economic Industry Classification" (GB/T 4754-2017) and other relevant classification standards to filter the original database. First, we retrieved all 2-digit codes of the construction industry from the "National Economic Classification"; then, we searched for keywords related to the digital construction industry and finally obtained the digital construction industry patent research sample.
Step 3: Identifying the innovative development path of the digital construction industry Based on the patent research sample of the digital construction industry, we applied mixed methods to identify the innovative development path of the digital construction industry. We adopted an explanatory sequence design, and the specific implementation steps were as follows. First, based on the Louvain algorithm, we took the International Patent Classification (IPC) as the main basis for constructing a two-dimensional matrix of digital construction industry innovation and then created the digital construction industry innovation networks. The research themes were determined based on the identified innovative core technologies, and the breakthrough points of the technological route were determined based on the identified key technologies. Then, based on grounded theory, we used open coding for the digital construction industry patent information. Through axis coding, we explored the meaning of the initial concepts and categories, identified their logical relationships, and integrated the main categories. Through selective coding, we determined the core categories and then refined the theoretical framework to identify the innovative development path of the digital construction industry.
The research steps are shown in Figure 2.

Mixed Methods Research
In 2004, American education researchers R. Burke Johnson and Anthony J. Onwuegbuzie presented mixed methods research as a natural complement to qualitative and quantitative research, calling mixed methods research "the third research paradigm" [44].
The first international academic journal dedicated to mixed methods research defined "mixed methods research" as "a researcher's use of both qualitative and quantitative research methods in a single study or a research program to collect, analyze data, integrate research findings and inferences" [45].
Quantitative and qualitative research methods each have their own advantages. Quantitative research achieves generalization and precision of conclusions, and qualitative research realizes in-depth excavation of experience from a personal perspective. However, mixed methods use both approaches in academic research to maximize the realization of the research objectives. determined based on the identified key technologies. Then, based on grounded theory, we used open coding for the digital construction industry patent information. Through axis coding, we explored the meaning of the initial concepts and categories, identified their logical relationships, and integrated the main categories. Through selective coding, we determined the core categories and then refined the theoretical framework to identify the innovative development path of the digital construction industry.
The research steps are shown in Figure 2.

Mixed Methods Research
In 2004, American education researchers R. Burke Johnson and Anthony J. Onwuegbuzie presented mixed methods research as a natural complement to qualitative and quantitative research, calling mixed methods research "the third research paradigm" [44].
The first international academic journal dedicated to mixed methods research defined "mixed methods research" as "a researcher's use of both qualitative and quantitative research methods in a single study or a research program to collect, analyze data, integrate research findings and inferences" [45].
Quantitative and qualitative research methods each have their own advantages. Quantitative research achieves generalization and precision of conclusions, and qualitative research realizes in-depth excavation of experience from a personal perspective. However, mixed methods use both approaches in academic research to maximize the realization of the research objectives.

Explanatory Sequence Design
One of the three core foundational approaches in mixed methods research is explanatory sequence design, which aims to first use quantitative research to collect and analyze data and then to use qualitative research to interpret the results of the quantitative method. Statistical significance and effect sizes generated by quantitative research methods provide generalized results for the research. However, we often do not know the context in which the results were obtained. In the next stage, qualitative research can help us interpret the results of quantitative research.

Explanatory Sequence Design
One of the three core foundational approaches in mixed methods research is explanatory sequence design, which aims to first use quantitative research to collect and analyze data and then to use qualitative research to interpret the results of the quantitative method. Statistical significance and effect sizes generated by quantitative research methods provide generalized results for the research. However, we often do not know the context in which the results were obtained. In the next stage, qualitative research can help us interpret the results of quantitative research.
This research design is called explanatory sequence design. The advantage of this design is that quantitative research is used first to analyze the research problem, and qualitative research is then used to explain the results. The two research methods support each other. Figure 3 shows a flow chart for the explanatory sequence design of this paper. This research design is called explanatory sequence design. The advantage of this design is that quantitative research is used first to analyze the research problem, and qualitative research is then used to explain the results. The two research methods support each other. Figure 3 shows a flow chart for the explanatory sequence design of this paper.

Social Network Analysis
The Louvain algorithm is a module-based community discovery algorithm used to discover the hierarchical community structure with the goal of maximizing the modularity Q of the whole graph attribute structure. The advantage of the Louvain algorithm is that it can identify the core node, research subject and key node of each module. By virtue of the community discovery algorithm, this research analyzes the current situation of the digital construction industry, explores the relationship and boundaries among patent subjects, constructs the knowledge atlas system for the industry, and determines the evolution of modularization themes in the industry.
(1) Modularity The physical meaning of modularity is the difference between the number of edges in a community and the number of edges in a random situation. The range of modularity is [−1/2, 1) and is defined as follows: k k * 1

Social Network Analysis
The Louvain algorithm is a module-based community discovery algorithm used to discover the hierarchical community structure with the goal of maximizing the modularity Q of the whole graph attribute structure. The advantage of the Louvain algorithm is that it can identify the core node, research subject and key node of each module. By virtue of the community discovery algorithm, this research analyzes the current situation of the digital construction industry, explores the relationship and boundaries among patent subjects, constructs the knowledge atlas system for the industry, and determines the evolution of modularization themes in the industry. (1) Modularity The physical meaning of modularity is the difference between the number of edges in a community and the number of edges in a random situation. The range of modularity is [−1/2, 1) and is defined as follows: where A ij represents the weight of the edges between node I and node j. When the network is not a weighted graph, the weight of all edges can be regarded as 1; k i = ∑ j A ij represents the sum of the weights of all edges connected to node i; c i represents the community to which node i belongs; m = 1 2 ∑ ij A ij is the sum of the weights of all the edges; the probability that node j is connected to any node is k j 2m ; and the edge of node i and j is k i k j 2m in random cases.
(2) Modularity gain When node i is assigned to its neighbor j, the change in modularity is related only to the community of nodes i and j. The modularity gain when assigning node i to community c where neighbor node j resides is where k i,in is the sum of the edge weights of the nodes in communities c and i and k i,in is the sum of the corresponding edge weights multiplied by 2. ∑ tot represents the total weight of the nodes in community c.

Grounded Theory
Grounded theory is a well-known approach in the field of qualitative research put forward by Glaser and Strauss [46]. This qualitative research method has the main purpose of establishing a theory based on empirical data [47]. Before our research began, there was no theoretical hypothesis. Therefore, this research started from actual observations, generalized the experience with original data, and then advanced to systematic theory. Grounded theory is a way of building a theory from the bottom up.
The operational process of grounded theory is as follows: (1) Generate concepts from the data and log the data step by step; (2) Continually compare data and concepts and systematically ask generative theoretical questions about the concepts; (3) Develop theoretical concepts and establish connections between them; (4) Encode the data level by level (encoding data is the most important part of grounded theory, which includes three levels of coding); and (5) Construct theories that strive to obtain the density, variability and high degree of integration of the theoretical concepts.
Overall, the coding of information level by level, including open coding, axial coding and selective coding, is the most important part of grounded theory. First, the researcher requires an open mind to log all materials as they emerge. Second, the main task of axial coding is to discover and establish various connections between conceptual categories. Finally, selective coding is the systematic analysis of the discovered conceptual categories and the selection of a "core category".

Results and Discussions
To explore the theme and evolutionary trend of digital innovation in the traditional construction industry, we constructed innovation networks for the digital construction industry with IPC as the main body. Then, we divided the cluster into three levels: largescale clusters (with 10 or more nodes), medium-scale clusters (with 5-10 nodes) and small-scale clusters (with less than 5 nodes). The modularization distribution of the digital construction industry from 2016 to 2020 is shown in Figure 4. The evolution of the scale of the largest cluster in the digital construction industry is shown in Figure 5.
(5) Construct theories that strive to obtain the density, variability and high degree of integration of the theoretical concepts.
Overall, the coding of information level by level, including open coding, axial coding and selective coding, is the most important part of grounded theory. First, the researcher requires an open mind to log all materials as they emerge. Second, the main task of axial coding is to discover and establish various connections between conceptual categories. Finally, selective coding is the systematic analysis of the discovered conceptual categories and the selection of a "core category".

Results and Discussions
To explore the theme and evolutionary trend of digital innovation in the traditional construction industry, we constructed innovation networks for the digital construction industry with IPC as the main body. Then, we divided the cluster into three levels: largescale clusters (with 10 or more nodes), medium-scale clusters (with 5-10 nodes) and smallscale clusters (with less than 5 nodes). The modularization distribution of the digital construction industry from 2016 to 2020 is shown in Figure 4. The evolution of the scale of the largest cluster in the digital construction industry is shown in Figure 5.

Characteristics of Innovation Networks in the Digital Construction Industry
The number of nodes and edges both increased over the years, but the number of clusters decreased after peaking in 2018. This indicates that more nodes were integrated into the cluster, and the number of large-scale clusters increased year by year. According

Characteristics of Innovation Networks in the Digital Construction Industry
The number of nodes and edges both increased over the years, but the number of clusters decreased after peaking in 2018. This indicates that more nodes were integrated into the cluster, and the number of large-scale clusters increased year by year. According to statistics, from 2016 to 2020, the number of large-scale clusters increased and the numbers of medium-scale and small-scale clusters decreased, indicating an increase in network connectivity.
As shown in Figure 5, the scale of the largest cluster in the digital construction industry expanded between 2016 and 2020, and an increasing number of IPC elements were included in the cluster, indicating that technological innovation in the digital construction industry was developing. The cluster index statistics for the digital construction industry are shown in Table 1.  Table 1 shows the following.
(1) The network scale had a gradual expansion tendency. The number of nodes and edges in the network both increased annually, which means that cooperation among subjects in various fields was gradually expanding. (2) The network diameter increased every year, reaching 16 in 2020. Research has shown that the diameter of sci-tech innovation networks increases rapidly at the beginning of their growth [48]. The growth trend of the innovation network of the 9 cities in the Bay Area shows that these cities had entered their growth period. (3) Compared with the number of nodes, the average path length of all clusters was very small, and the average clustering coefficient was always greater than 0.8, which are the characteristics of a small world network. (4) The average degree and average weighted degree of the network nodes showed an increasing annual trend, indicating that the connectivity of the network also increased every year. However, the network density was much lower than 1, indicating that although the innovation subjects increased each year, they were all in small independent and modularized networks, and the overall cohesion of the network was not strong, representing unstable and weak cooperation among various fields.

Subject Identification in Innovation Network Research on the Digital Construction Industry
The digital construction innovation networks during the period 2016-2020 are shown in Figures 6-10.
In 2016, there were 4 large-scale clusters, 6 medium-scale clusters and 17 small-scale clusters. In 2017, the numbers of large-and medium-scale clusters increased, but the proportion of medium-scale independent clusters also increased, indicating a lack of connectivity among the new clusters.
In 2018, the numbers of large and small clusters both increased, but large clusters lacked connectivity to the other clusters. In 2019, the numbers of small-scale and mediumscale clusters both decreased, but the number of large-scale clusters increased significantly and no independent large-scale clusters appeared, indicating that the sizes of both smalland medium-scale clusters were expanding and that their connectivity was increasing.  In 2016, there were 4 large-scale clusters, 6 medium-scale clusters and 17 small-scal clusters. In 2017, the numbers of large-and medium-scale clusters increased, but the proportion of medium-scale independent clusters also increased, indicating a lack of connectivity among the new clusters.  In 2016, there were 4 large-scale clusters, 6 medium-scale clusters and 17 small-sca clusters. In 2017, the numbers of large-and medium-scale clusters increased, but the proportion of medium-scale independent clusters also increased, indicating a lack of connectivity among the new clusters.  In 2018, the numbers of large and small clusters both increased, but large clusters lacked connectivity to the other clusters. In 2019, the numbers of small-scale and mediumscale clusters both decreased, but the number of large-scale clusters increased significantly  In 2018, the numbers of large and small clusters both increased, but large clusters lacked connectivity to the other clusters. In 2019, the numbers of small-scale and mediumscale clusters both decreased, but the number of large-scale clusters increased significantly Buildings 2022, 12, x FOR PEER REVIEW 13 of and no independent large-scale clusters appeared, indicating that the sizes of both sma and medium-scale clusters were expanding and that their connectivity was increasing. In 2020, the number of small-scale clusters continued to decrease, but the numbers large-and medium-scale clusters remained stable and no large-scale clusters emerge indicating that the connectivity of both small-and medium-scale clusters did not diminis as their size continued to grow.

Evolution of the Innovation Network in the Digital Construction Industry
The annual research themes were determined through the identified cluster core and the evolutionary path of the innovation themes in the digital construction indust was then depicted. The key technologies of each year were determined by identifying th key connections of the clusters, and the evolutionary path of the key technologies of inn vation in the digital construction industry was then described. The evolutions of the i novation theme and key technologies in the digital construction industry are shown Figures 11 and 12. (1) Evolution of innovation themes in the digital construction industry In 2020, the number of small-scale clusters continued to decrease, but the numbers of large-and medium-scale clusters remained stable and no large-scale clusters emerged, indicating that the connectivity of both small-and medium-scale clusters did not diminish as their size continued to grow.

Evolution of the Innovation Network in the Digital Construction Industry
The annual research themes were determined through the identified cluster cores, and the evolutionary path of the innovation themes in the digital construction industry was then depicted. The key technologies of each year were determined by identifying the key connections of the clusters, and the evolutionary path of the key technologies of innovation in the digital construction industry was then described. The evolutions of the innovation theme and key technologies in the digital construction industry are shown in Figures 11 and 12. (1) Evolution of innovation themes in the digital construction industry From 2016 to 2020, the scope of digital objects continued to expand from buildings to road and bridge engineering and water supply and drainage engineering, but buildings were always digital objects.
In terms of digital carriers, the types of digital carriers increased, and lighting devices were always an important digital carrier. In addition, power generation, power substations, power distribution devices, air conditioning devices, manipulator devices, and waterrelated devices were important carriers of digitization. After 2019, electrical communication technology, wireless communication technology, computing and calculation algorithm equipment, and basic electrical components became new digital carriers, indicating that the construction industry had begun to intelligently transform. From 2016 to 2020, the scope of digital objects continued to expand from buildings to road and bridge engineering and water supply and drainage engineering, but buildings were always digital objects.
In terms of digital carriers, the types of digital carriers increased, and lighting devices were always an important digital carrier. In addition, power generation, power substations, power distribution devices, air conditioning devices, manipulator devices, and water-related devices were important carriers of digitization. After 2019, electrical communication technology, wireless communication technology, computing and calculation algorithm equipment, and basic electrical components became new digital carriers, indicating that the construction industry had begun to intelligently transform.
In terms of digital results, this research found that fixed buildings were first integrated with lighting engineering, electrical engineering, and then mechanical engineering. With the addition of digital carriers, the digital construction industry began to integrate with human necessity and transportation equipment fields. With the addition of new carriers, such as electrical communication technology, wireless communication technology, computing and calculation algorithm equipment, and basic electrical components, the In terms of digital results, this research found that fixed buildings were first integrated with lighting engineering, electrical engineering, and then mechanical engineering. With the addition of digital carriers, the digital construction industry began to integrate with human necessity and transportation equipment fields. With the addition of new carriers, such as electrical communication technology, wireless communication technology, computing and calculation algorithm equipment, and basic electrical components, the digital construction industry began to integrate with the physics, textile and papermaking fields. To date, the digital construction industry has not integrated with the chemical and metallurgical fields.
(2) Evolution of key technologies for innovation in the digital construction industry In 2016-2017, lighting devices played a key role in the innovation of the digital construction industry. Buildings first combined the two fields of lighting engineering and electricity through combinations of lighting devices and other items and exposure recharging devices, and the key connection position of the exposure recharging devices was then replaced by light emitting diodes. In 2017, light emitting diodes played a fundamental connectivity role in digital construction industry innovation.
x FOR PEER REVIEW 15 of 20 (2) Evolution of key technologies for innovation in the digital construction industry In 2016-2017, lighting devices played a key role in the innovation of the digital construction industry. Buildings first combined the two fields of lighting engineering and electricity through combinations of lighting devices and other items and exposure recharging devices, and the key connection position of the exposure recharging devices was then replaced by light emitting diodes. In 2017, light emitting diodes played a fundamental connectivity role in digital construction industry innovation.
In 2018-2019, special-purpose buildings or similar structures played a key role in digital construction industry innovation. Digital construction industry innovations began to combine mechanical engineering, human necessity, and transportation fields through special-purpose buildings or similar structures, which had a wider connection range than that of lighting devices.
In 2020, the key connections of the innovation network began to diversify. Photosensitive cells, radio lines, and transmission control procedures became new key connections, which led to new development directions and new technologies for digital construction industry innovation.

Qualitative Research on the Digital Construction Industry
This research used qualitative analysis software for open coding, axial coding and selective coding of data. In 2018-2019, special-purpose buildings or similar structures played a key role in digital construction industry innovation. Digital construction industry innovations began to combine mechanical engineering, human necessity, and transportation fields through special-purpose buildings or similar structures, which had a wider connection range than that of lighting devices.
In 2020, the key connections of the innovation network began to diversify. Photosensitive cells, radio lines, and transmission control procedures became new key connections, which led to new development directions and new technologies for digital construction industry innovation.

Qualitative Research on the Digital Construction Industry
This research used qualitative analysis software for open coding, axial coding and selective coding of data.
(1) Open coding Open coding is the process of decomposing, abstracting, conceptualizing and categorizing data. Examples of open coding for this study are shown in Table 2. (2) Axial coding Axial coding is a process that discovers and determines the logical relationship between categories. This research reorganized the original category into 40 subcategories and groups and then into 10 principal categories. The axial encoding results are shown in Table 3. Selective coding is a process that selects the core category, links it systematically with other categories and conceptualizes and theorizes it. In this step, this research first analyzed the connotations and properties of the 10 principal categories obtained by axial coding and then compared them with the original data records. The final results are shown in Figure 13.
ildings, intelligent transportation, and construction of intelligent build-Digital construction industry ilding equipment, intelligent building device, intelligent building magent parking equipment, and intelligent transportation equipment Digital construction equipment

(3) Selective coding
Selective coding is a process that selects the core category, links it systematically with other categories and conceptualizes and theorizes it. In this step, this research first analyzed the connotations and properties of the 10 principal categories obtained by axial coding and then compared them with the original data records. The final results are shown in Figure 13.

Conclusions
This paper presents an explanatory sequence research on the innovation pathway for the digital construction industry using social network analysis and grounded theory based on patent data for Guangdong Province. Several important conclusions can be drawn from the results.
First, the findings indicate that the use of mixed methods improves the robustness of the results and that the quantitative research results are explained by the qualitative research results.
Second, the scale of the innovation network in the digital construction industry has expanded in recent years, the number of large-scale clusters has increased, and network connectivity has also gradually increased. The range of digital objects has broadened, and digital carriers have become diverse and transformed by intellectualization.
Third, the research themes of the digital construction industry are influenced by digital carriers, which started with the field of lighting engineering and then expanded to mechanical engineering and operational transport facilities and finally to the field of human necessity. With the addition of electric communication technology, wireless communication technology, basic electric components and other carriers, the research themes of the digital construction industry began to move in the direction of intelligence.
Finally, the grounded theory results further explain the digital construction industry innovation path, which is based on a digital carrier, then a clear digital object, and finally the digital transformation of the construction industry.
The main contribution of this paper is a methodology based on social network analysis techniques to model how different innovation pathways and clusters interact in a dynamic innovation network that evolves depending on how interconnected and diversified it is. This paper can help practitioners understand the development of digital technology innovation in the construction industry. Furthermore, it can provide academics with a theoretical basis and practical experience for deepening digital reform in the construction industry.