The effect of collaboration on recovery after extreme weather in the logistics industry

The effect of collaboration on recovery after

extreme weather in the logistics industry

Master’s Thesis, MSc, Supply Chain Management

University of Groningen, Faculty of Economics and Business

22 June, 2018

Yanran Du

Student Number: S3188078

E-mail: y.du.6@student.rug.nl

Supervisor

Dirk Pieter van Donk

Co-assessor

Cheng-Yong Xiao

Contents

ABSTRACT ... 3

1.INTRODUCTION ... 4

2.THEORETICAL BACKGROUND ... 8

2.1 Supply chain resilience and collaboration ... 8

2.2 Performance of recovery ... 10

2.3 The impact of extreme weather ... 11

2.4 Logistics industry ... 12

2.5 The effect of different collaborative activities ... 13

3. METHODOLOGY ... 20 3.1 Research design ... 20 3.2 Development questionnaire ... 20 3.3 Data collection ... 21 3.4 Measurement ... 22 3.5 Data analysis ... 23 4.FINDINGS ... 27 4.1 Cold waves ... 28 4.2 Heavy precipitation ... 31 4.3 Storm ... 34 5.DISCUSSION ... 37

5.1 The effect of collaborative activities ... 37

5.2 The influence of different type of extreme weather ... 37

6. CONCLUSION ... 42

7.LIMITATION AND FUTURE RESEARCH... 43

REFERENCE ... 44

APPENDIX A: Questionnaire ... 49

APPENDIX B: Figure 4.2 Two-way interaction plots of decision synchronisation ... 53

APPENDIX C: Figure 4.3 Two-way interaction plots of incentive alignment ... 54

APPENDIX D: Figure 4.4 Two-way interaction plots of goal congruence ... 55

APPENDIX E: Figure 4.5 Two-way interaction plots of resource sharing ... 56

APPENDIX F: Figure 4.6 Two-way interaction plots of decision synchronisation ... 57

APPENDIX G: Figure 4.7 Two-way interaction plots of incentive alignment ... 58

APPENDIX H: Figure 4.8 Two-way interaction plots of goal congruence ... 59

APPENDIX I: Figure 4.9 Two-way interaction plots of resource sharing ... 60

ABSTRACT

Purpose: The purpose of the research is to explore the effect of different

collaborative activities on performance of recovery after extreme weather in the logistics industry. The performance of logistics companies directly influence other companies' benefits, especially with the condition that more and more organizations choose to outsource their logistics parts. To achieve better performance and common goals, logistics companies usually try to build intensive relationships with their customers. An appropriate way to construct the structure of collaboration between logistics companies and their customer efficiently is important.

Design/Methodology/Approach: A survey is utilized as method to collect the data

from 100 respondents who are working in the logistics industry.

Findings: The paper shows that there are significant and positive impacts of

information sharing, collaborative communication, and joint knowledge creation on all aspects of performance of recovery after three types (i.e. cold waves, heavy precipitation, and storm) of extreme weather respectively. While, the results about the moderating roles of decision synchronisation, incentive alignment, goal congruence, and resource sharing vary in different types of extreme weather.

Value: The paper contributes to the theory of supply chain resilience, providing

1.INTRODUCTION

Based on the record from World Meteorological Organization, the frequency of extreme weather has increased continuously over the past 10 years, such as storms, have doubled since 2010 (World Meteorological Organization, 2018). According to the reports from National Geographic, the United States experienced the most costly hurricane season ever recorded in 2017 (Drye, 2017). Meanwhile, in the same year, the torrential rain in early-July caused considerable economic losses and transport disruption in China. More than 600 flights were cancelled at Beijing airport on 6th July due to the rainfall (World Meteorological Organization, 2017). In addition, according to the data from Munich Re Group, extreme weather caused $129 billion of economic losses around the world last year, which is significantly higher than $94 billion in 2014 (Munich Re Group, 2017).

The negative effect of extreme weather on logistics industry is obvious due to the process of transportation. These events render networks of all transport modes vulnerable, hinder their operation, and lead to partial or full disruptions of the whole system (Stamos et al.,2015). Such facts promote logistics companies to explore how to recover efficiently after extreme weather and minimize the lost.

A common way in reality for logistics companies to mitigate the negative effect of extreme weather is insurance. However, the coverage of insurance policy is hard to determine due to the high level of uncertainty of extreme weather impacts. Meanwhile, insurance cannot effectively alleviate the negative effect on customer satisfaction result from the disruption of supply chain. The literature in this filed mostly focus on exploring and measuring the impacts of extreme weather on logistics (Ludvigsen & Klæboe, 2014; Stamos et al., 2015; Postance et al.,2017), but few provide clear and reliable approaches for logistics companies to deal with disruptions caused by extreme weather.

risk category, so there is a lack of historical data that is needed for generating an overview of all impacts. Secondly, due to the low-frequency of extreme weather, companies cannot allocate sufficient resources to proactively mitigate the negative effect before the disruption. Based on the characteristics of extreme weather, conventional risk approaches focusing on preparation are not suitable for extreme weather anymore (Merz et al.,2010). Therefore, increasing reactive capabilities of organization to recover from a disruption caused by extreme weather efficiently has become more important (Linnenluecke & Griffiths, 2010; Linnenluecke et al., 2012) .

Therefore, it is important for logistics companies to find efficient ways to improve their reactive capabilities, achieving a better performance of recovery after extreme weather. Scholars believe that the knowledge about supply chain resilience can provide guidance to actions, improving a firm's ability of recovering from a disruptive event (Bruijn et al., 2017; Panteli & Mancarella, 2015) .

Supply chain resilience requires a firm to consider the capabilities of the whole supply network to deal with change and uncertainty (Scholten & Schilder, 2015). Accordingly, the collaboration is essential for building a resilient supply chain, being an antecedent of the construct resilience (Jüttner & Maklan, 2011). Scholten and Schilder (2015) define collaboration via the collaborative activities of information-sharing, goal congruence, joint decision-making, resources-information-sharing, incentive alignment, collaborative communication and joint knowledge creation. There is no doubt about the positive effect of collaboration on supply chain resilience. The effect of collaboration not only depends on the application of different collaborative activities (Scholten & Schilder, 2015), but also specific participants, because it is hard for a firm to build the same level cooperation with each party in the supply chain covering all collaborative activities (Cao et al., 2010). Meanwhile, the application of different collaborative activities should be driven by a clear business need (Scholten & Schilder, 2015). So, the desired performance of recovery after extreme weather are different based on distinct logistics companies' perspectives, which indicates the importance of identifying the effect of different collaborative activities.

the stress on the effect of customer-side collaboration in logistics industry, due to the fact that logistics companies are usually an important part of other firm's supply chain, working as the supporting actors or value-added entities (Wang et al., 2016), and the customer-side collaboration influence the creating of competitive advantage. Therefore, logistics organizations pay more attention on the collaboration with their own customers to achieve common goals and competitive advantages. Generally, logistics companies try to build intensive relationships with their customers. And, the customer satisfaction directly affects the judgement of performance of the recovery after extreme weather.

Although previous research have explored the effect of collaboration, treating collaboration as an antecedent formative element of supply chain resilience, the effect of different collaborative activities is little to be known (Scholten& Schilder 2015). On the basis of the previous description, it's necessary for logistics companies to link different collaborative activities to specific performance of recovery, providing insights on how to design supply chain resilience into a supply chain efficiently. Against this backdrop, the research will explore the effect of customer-side collaboration on recovery after extreme weather in the logistics industry, the research question is generated as below:

What are the distinctive effect of different collaborative activities on improving the performance of recovery after different type of extreme weather in the logistics industry?

industry, which can provide inspirations for managers when facing such case. The similarities of efficient actions among different firms in the logistics industry can be explored. Through the research, the managers can generate a better understanding about how to cope with a disruption caused by extreme weather efficiently.

2.THEORETICAL BACKGROUND

2.1 Supply chain resilience and collaboration

Supply chain disruptions can be defined as "unplanned an unanticipated events that disrupt the normal flow of goods and materials within a supply chain" ( Macdonald & Corsi, 2013; Brüning et al., 2014). A mass of research devotes to develop appropriate ways to mitigate the negative effect of disruptions on supply chain performance. Traditional supply chain risk management focuses on reducing the probability of disruption occurrences, but not all disruptions can be predicted and prevented from happening, which promotes the exploration of supply chain resilience (Jüttner & Maklan, 2011). The purpose of supply chain resilience is to develop the adaptive capabilities to prepare for unforeseeable events, and respond to disruptions (Tukamuhabwa et al., 2015). To avoid considerably higher additional costs to recover from the negative effect of disruptive events, a proper response to disruptions is necessary for a logistics company (Hishamuddin et al., 2014). Since the characteristics of extreme weather, it is difficult to prevent the disruptions caused by extreme weather from happening, and allocate resources before extreme weather (Ludvigsen & Klæboe, 2017), supply chain resilience is necessary for dealing with such disruptions. Supply chain resilience presents a firm's ability to return to its original state, or move to a more desirable state after a disruption (Christopher & Peck, 2004). This paper applies the definition of supply chain resilience proved by

Ponomarov and Holcomb in 2009:" The adaptive capability of the supply chain to prepare for unexpected events, respond to disruptions, and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structure and function".

TABLE 2.1

Different types of collaborative activities (Cao et al., 2010)

Collaborative activity Definition

Information sharing The extent to which a firm shares a variety of relevant, accurate, complete and confidential ideas, plans, and procedures with its supply chain partners in a timely manner.

Goal congruence The extent to which supply chain partners perceive their own objectives are satisfied by accomplishing the supply chain objectives.

Decision synchronisation The process where supply chain partners orchestrate decisions in supply chain planning and operations that optimise supply chain benefits

Incentive alignment The process of sharing costs, risks, and benefits among supply chain partners

Resource sharing The process of leveraging capabilities and assets and investing in capabilities and assets with supply chain partners

Collaborative communication The contact and message transmission process among supply chain partners in terms of frequency, direction, mode, and influence strategy

Joint knowledge creation The extent to which supply chain partners develop a better understanding of and response to the market and competitive environment by working together

Collaboration in supply chain resilience involves the process to work with another or others on joint projects (Jüttner & Maklan, 2011; Scholten & Schilder, 2015), which consists of several collaborative activities (Ramanathan & Gunasekaran, 2014, Kumar & Nath Banerjee, 2014). In the research, the main seven collaborative activities presented by Cao et al (2010) are used, and the corresponding definition of each type is presented in Table 2.1.

2.2 Performance of recovery

Resilience is created to mitigate threats caused by a disruption to organizational performance (BrandonJones et al., 2014). A disruption in supply chain can be divided into three phases: readiness, responsiveness, and recovery, and the adaptive capabilities are structured along the three phases (Jüttner & Maklan, 2011). This paper focuses on recovery phase. Performance of recovery refers to the extent to which the affected company recovers from a disruptive event and minimize its effects (Macdonald & Corsi, 2013). A higher level of supply chain resilience can lead to a better performance of recovery. Measuring the performance of recovery is crucial for managers to take right actions in the future. Furthermore, it can be treated as an opportunity for adaption (Travis, 2014).

Based on different goal, the desired performance of recovery for different logistics company is distinct, influencing the measurements of performance of recovery. A

FIGURE 2.1 Performance of recovery

customer demand, and satisfaction. A disruption of supply chain usually leads to service failures because companies are not able to meet customer demand in time in according to the original plan. Production impact relates to the degree of effect on scheduled operations. Sometimes, due to a disruption in supply chain, companies have to adjust scheduled production or operations since the lost time (Macdonald & Corsi, 2013). Furthermore, recover speed can influence the other two dimensions. A longer recovery time can lead to higher financial cost and a larger impact on service (Macdonald & Corsi, 2013).

2.3 The impact of extreme weather

Extreme weather includes unexpected, unpredictable severe or unseasonal weather, which is also treated as a part of climate change in some research (Bergmann, 2016).

paper aims to link the effect of collaborative activities on performance of recovery to the five types of extreme weather respectively.

For logistics industry, the effect of extreme weather includes direct effect and indirect effect. The former is visible, such as delay of freight train (Ludvigsen & Klæboe, 2014). The latter is not as visible as the former, but more serious, such as safety and life (Kovács & Pató, 2014). Due to the vulnerability of supply chain, business can be suffer without being directly influenced by extreme weather, so it is necessary to consider wider impact of extreme weather on a firm's supply chain, not only focus on direct physical impacts (Wedawatta et al., 2010). In addition to, impacts of extreme weather are also influenced by the interactions between physical impacts and socio-economic factor (Linnenluecke et al., 2012). Therefore, compared with other factors that cause a disruption, the impacts of extreme weather are harder to be assessed, and the low frequency of extreme weather makes the process of preparation and reaction more difficult.

Combined with the characteristics of extreme weather. de Bruijn et al. (2017) elaborated five resilience principles to cope with extreme weather: 1) adopt a system's approach which means that the system is treated as a whole and all process within the system are viewed as interlinked. 2) Look at beyond-design events which means to consider the entire possible spectrum of events instead of focusing on design events derived from relatively short data records. 3) Design and prepare systems according to the 'remain functioning’ principle', which makes the consequences of disruptions are not catastrophic , but manageable. 4) Increase the recovery capacity, due to the fact that the time that recovery takes depends on the recovery capacity. 5) Remain resilient into future, which shows a ability of adaptation and transformation to cope with future extreme weather. The five principles have stressed the importance of recovery capacity, and linked the collaboration within a supply chain to dealing with disruptions caused by extreme weather, providing directions for this paper.

2.4 Logistics industry

Björklund, 2015). A firm in the logistics industry is usually involved in a vast transportation system, including various ways of transport, transit service, intermodal connections, and facilities that used to manage transportation operation (Abkowitz et al., 2017). The effect of extreme weather on this field is obvious. And a mass of research has explored how different extreme weather events harm the performance of logistics sector (Leviäkangas & Michaelides, 2014; Ludvigsen & Klæboe, 2014). Transportation infrastructures suffer damages and the cost of maintenance will increase considerably (Leviäkangas & Michaelides, 2014). Meanwhile, operators suffer losses of income and damages of cargo. In other words, extreme weather harms the safety and security of operators, and disrupts logistics chains (Stamos et al., 2015). Based on previous part, performance of recovery can be measured by financial cost, indicating the importance of loss assessment. Leviakangas and Michaelides in 2014 conclude three types of cost resulted from extreme weather: accident cost, time costs, infrastructure-related cost, and point out that most extreme weather costs should be internalised. However, it is difficult to implement it due to the lack of models that can be used to account the accurate cost. Furthermore, with greater magnitude, frequency, and abruptness of extreme weather, the effect of it may exceed thresholds for adaption and lead to a disruption of supply chain (Linnenluecke et al., 2012). Therefore, how to quickly restore performance to the pre-impact state after extreme weather becomes more important.

Logistics companies provide a wide range of logistics service to their customers, and are well-recognized as key enablers of their customers' service-related competitive advantages, which means the performance of logistics firms and their customers are closely linked (Tian et al., 2010). Based on the feature of logistics industry, the research focuses on the collaboration between logistics company and its customer.

2.5 The effect of different collaborative activities

Logistics companies need to configure their collaboration structures efficiently to achieve better performance of recovery after a disruption. Therefore, the application of collaborative activities should be linked to the type of extreme weather, identifying the effect of different collaborative activities on performance of recovery after different type of extreme weather in the logistics industry. Therefore, the paper proposes seven basic hypotheses, and then tests these hypotheses in different type of extreme weather respectively.

2.5.1 Information sharing and performance of recovery. Information sharing relates

to the extent to which a firm shares relevant, accurate, complete, and confidential ideas and plans with other parties in a timely manner (Cao et al., 2010). Such activities involve intangible resources concerning the nature of information shared, and have been recognized as an efficient way to develop competitive advantages for a firm (Olorunniwo & Li, 2010). Logistics companies usually provide physical link between customers and their suppliers, involving a large scales of information exchange (Sanchez et al., 2008). Through sharing appropriate and timely information between participants in a supply chain, the visibility of supply chain can be improved, especially regarding the information about inventory and demand levels across the supply chain, leading to a more open exchange between participants (BrandonJones et al., 2014). For logistics companies, the operational efficiency can be improved, and customer demand can be fulfilled more effectively and accurately, bringing out higher level of customer satisfaction (Sandberg, 2007), being beneficial for building an intensive relationship with customer. Meanwhile, Scholten and Schilder (2015) point out that the flexibility of supply chain will be reduced due to lack of timely information, directly influencing the speed of response to a disruption. So, the more participants in a supply chain engage in information sharing, the higher level of visibility and flexibility, leading to a more resilient supply chain. A higher level of supply chain resilience leads to a better performance of recovery. The impacts of extreme weather are characterized by high level of uncertainty (Linnenluecke et al., 2012). For logistics companies, increasing the flexibility of supply chain is beneficial for dealing with uncertainties, leading to customer satisfaction (Naim et al., 2006). Therefore, the first hypothesis state that

customer satisfaction and scheduled production) of recovery after extreme weather in the logistics industry.

2.5.2 Collaborative communication and performance of recovery. Collaborative

communication involves the contact and message transmission process between participants in supply chain, which focuses on four dimensions: the frequency of communication among partners, direction of communication, mode of communication, and influence of communication (Cao et al., 2010). For logistics companies, collaborative communication provide channels to build intensive cooperation with their customers, such as having formal and informal meetings regularly. Through collaborative communication, an atmosphere of mutual support can be created, which leads to higher level of synergy between companies (Mohr et al., 1996). Meanwhile, collaborative communication provides a head start for members in a supply chain to jointly cope with disruption of supply chain (Wieland & Marcus, 2013). Logistics companies usually work as an important part of other companies' supply chain, and the effect of extreme weather is usually regional or global, not only confined in a single company or one industry (Wang et al., 2016; Wedawatta et al., 2010). Through building collaborative communication with customers, logistics companies can grasp a more comprehensive status of supply chain and understand the changing customer demand, improving customer satisfaction. Such activities improve the levels of velocity, visibility ,and flexibility of supply chain (Wieland & Marcus, 2013). With the improvement of formative elements, the supply chain resilience also be improved, leading to a better performance of recovery. Therefore, the following hypothesis is

Hypothesis 2.There is a positive linear relationship between collaborative communication and performance outcomes (i.e. a) recovery speed, b) financial cost of recovery, and c) customer satisfaction and scheduled production) of recovery after extreme weather in the logistics industry.

2.5.3 Joint knowledge creation and performance of recovery. Joint knowledge

al., 2010). In order to create knowledge, participants in a supply chain need to engage in interlinked processes, which leads to a more intensive collaboration (Wu, 2008). One important goal of logistics companies is to build intensive relationships with their customers, the performance of logistics companies are closely linked to their customers' benefits (Tian et al., 2010). During the process of joint knowledge creation, logistics companies and their customers share relevant information and capabilities. The response time is reduced. Furthermore, one important principle proposed by deBruijn et al. (2017) to cope with extreme weather is to treat the supply chain as a whole, guiding the actions to recovery of a disruption. Through such jointly activities, logistics companies can understand their customers more deeply, and the channels of communication and cooperation are broaden. With the improvement of flexibility and visibility, the supply chain resilience is improved as well, leading to a better performance of recovery. Therefore, the following hypothesis is,

Hypothesis 3.There is a positive linear relationship between joint knowledge creation and performance outcomes (i.e. a) recovery speed, b) financial cost of recovery, and

c) customer satisfaction and scheduled production) of recovery after extreme weather

in the logistics industry.

2.5.4 The moderating roles of decision synchronisation and incentive alignment.

companies and their customers is more intensive and deeper. Information sharing involves the quality of information. Sharing timely and accurate information is important to cope with extreme weather, being helpful to assessing the impact of extreme weather and taking right actions (Wedawatta et al., 2010). Decision synchronisation specifies information needs, involving relevant and accurate information (Simatupang & Sridharan, 2004). As a result, the following hypothesis is, Hypothesis 4.Decision synchronisation takes a positive moderating role in the relationship between information sharing and performance outcomes(i.e. a) recovery speed, b) financial cost of recovery, and c) customer satisfaction and scheduled production) of recovery after extreme weather in the logistics industry.

Incentive alignment relates to the process of sharing cost, risks, and benefits among partners in supply chain. The outcomes of collaboration should be beneficial for all members to ensure a successful partnership (Cao & Zhang, 2011). Logistics companies and customers in a supply chain are motivated to act in a consistent manner with overall goals through incentive alignment. And, the levels of cooperation and commitment are secured. Meanwhile, such activities relates to risk sharing among participants in a supply chain (Simatupang & Sridharan, 2004; Cao et al., 2010). Logistics companies sometimes work as value-added entity in its customer's supply chain (Tian et al.,2010). Incentive alignment between logistics company and its customer is helpful to lead to a better communication and develop the understanding of common goals. During this process, the relevant information are shared between participants, such as inventory strategy and risks. Meanwhile, incentive alignment motivates to reveal truthful information, improving the accuracy of information (Simatupang & Sridharan, 2005). Therefore, the following hypothesis is,

Hypothesis 5.Incentive alignment takes a positive moderating role in the relationship between information sharing and performance outcomes(i.e. a) recovery speed, b) financial cost of recovery, and c) customer satisfaction and scheduled production) of recovery after extreme weather in the logistics industry.

2.5.5 The moderating role of resource sharing. Resources include physical assets,

partners in supply chain (Cao et al., 2010). So, through resource sharing , the capability of a firm can be improved. The flexibility of the supply chain can be enhanced as well. Knowledge creation can be explained for two perspectives, one is "stock" view, the other is "process" view. In "stock" view, knowledge creation increase the corporate knowledge stock, similar to other assets, which means the goal of knowledge creation also relates to determine what resource sharing decision are made between participants in a supply chain (Samaddar & Kadiyala, 2006). Logistics companies play an important role in customer's supply chain, and manage their own supply chain, interacting with other companies in the network (Piecyk & Björklund, 2015). Logistics companies can grasp a more comprehensive view about the impacts of the extreme weather through collecting information from their network. Meanwhile, resource sharing enhances a more appropriate assignment of assets and capabilities between partners in a supply chain, being beneficial for searching and acquiring relevant knowledge. There is no direct empirical evidence that resource sharing can improve the supply chain resilience. But based on the relationships between resource sharing and joint knowledge creation, we make the hypotheses as below,

Hypothesis 6.Resource sharing takes a positive moderating role in the relationship between joint knowledge creation and performance outcomes(i.e. a) recovery speed, b) financial cost of recovery, and c) customer satisfaction and scheduled production) of recovery after extreme weather in the logistics industry.

2.5.6 The moderating role of goal congruence. Goal congruence shows the extent to

FIGURE 2.2 Conceptual model

boundaries between different units. Then, intensive communication allows units to coordinate interdependent tasks, avoiding misaligned activities. Extreme weather can cause widespread damages in various fields, the collective losses of a number of sectors can devastate a regional economy (Wedawatta et al., 2010). Previous literature have proposed that collaborative communication can increase visibility and velocity, improving supply chain resilience, but lack the description the effect of goal congruence on supply chain resilience. So, based on the connections between goal congruence and collaborative communication, we make the hypotheses as below,

Hypothesis 7.Goal congruence takes a positive moderating role in the relationship between collaborative communication and performance outcomes(i.e. a) recovery speed, b) financial cost of recovery, and c) customer satisfaction and scheduled production) of recovery after extreme weather in the logistics industry.

3. METHODOLOGY

3.1 Research design

The main purpose of the research is to explore the effect of different collaborative activities applied on performance of recovery after extreme weather in the logistics industry. To answer the research question an empirical study is conducted. Meanwhile, survey is selected as research method due to three reasons. Firstly, previous literature provides detailed information of each variable, including scales and measurements of each variable. Secondly, the research try to test the relationship between specific collaborative activity and performance outcomes of recovery. Through survey, a large amount of data can be collected to prove it. Thirdly, survey can link the empirical evidence to theoretical variables. In the following sections, the data collection, measurements and data analysis are introduced.

3.2 Development questionnaire

The survey is made up of two major parts, the first part involves general information about a respondent such as company name, position, and length of service. The second part connects theoretical variables with reality, which includes three sections. The first section is a disruptive event caused by extreme weather. In this section, a respondent needs to choose a type of extreme weather which leaded to the most severe disruptive event, and the following sections will be answered based on the disruptive event. The second section is collaboration, which identifies the degree of different collaborative activities applied to recover from the disruption. Thereafter, the last section is performance outcomes of recovery, which reflects performance outcomes from three dimensions.

TABLE 3.1

General information of data

COMPANY NUMBER PERCENT

Jusda 3 0.03 Panalpina 2 0.02 SINOTRANS&CSC 2 0.02 Fedex 2 0.02 Godrej 1 0.01 Evergreen 2 0.02 HJSC 3 0.03 Mol 2 0.02 XPO Logistics 3 0.03 Damco 7 0.07 SF 3 0.03 Maersk 5 0.05

Chengdu Southwest Railway Material Corp.Ltd 3 0.03

S.F.Express 4 0.04 Safmarine 2 0.02 APEX LOGISTICS 5 0.05 IAA 3 0.03 Sto 4 0.04 COSCO 5 0.05

Shenzhen Qianhai Baicang Supply Chain Co., Ltd. 3 0.03

China Cargo Airlines 3 0.03

Ups 2 0.02

APL 2 0.02

CHINA SOUTHERN CARGO 3 0.03

OOCL 2 0.02

Siroi Enterprises 1 0.01

Abdullah Fouad Holding Company 1 0.01

MSC 2 0.02 Oubi 1 0.01 Hamburg sud 1 0.01 GTT 2 0.02 CMA 2 0.02 JZ Logistics 3 0.03 DHL 4 0.04 None 7 0.07 100 1 3.3 Data collection

the target respondents who we sent survey to but did not get feedback, we sent a reminder together with the questionnaire website again. Around two months were spent on data collection. During this process, we distributed 136 questionnaires and got 100 effective responses, the response rate of 73.5 percent. The general information is presented as Table 3.1. Based on different location of branch office, one company provided several responses.

3.4 Measurement

The purpose of the research is to test the relationships between different collaborative activities and specific performance outcomes of recovery after extreme weather in the logistics industry. Although specific definition of each collaborative activity has been provided by previous research, there is difficulty in generating some questions to measure it in reality. To ensure the reliability of data, making all questions measure corresponding variable correctly, the similar scales used by previous literature to measure collaborative activities are utilized in the research.

Information sharing is measured by the scales developed by BrandonJones et al. (2014). The five items are to measure the extent of relevant, timely, accurate, and complete information sharing between focal firm and its customers. Then, goal congruence is measured by the scales developed by Cao et al. (2010). The five items are to measure the degree of goal agreement, compatibility, or fit among supply chain partners. Decision synchronisation is measure by the scales developed by Simatupang and Sridharan (2005). And nine items are used to measure the extent of joint decisions between focal firm and its customers, involving several dimensions of joint decisions.

In measuring the performance outcomes of recovery, we followed the scales used by BrandonJones et al. (2014). There are three dimensions including recovery speed, financial cost and customer and production impact. Recovery speed is measured by four items, and financial cost is measured by two items which focuses on the comparison between actual cost and predicted cost. There are four items to measure the impact on customer and production, which aims to determine the extent of customer satisfaction, and recovering scheduled production and operations.

3.5 Data analysis

To validate that each variable is distinct construct, and each items are used to measure right variable, an exploratory factor analysis (EFA) was conducted. The results are presented as Table 3.2. Most individual items loaded separately as proposed. The average variance extracted (AVE) values were used to indicate discriminant validity.

The value of KMO is 0.833, and the value of Bartlett's Test of Sphericity is smaller than 0.05, indicating adequate sample, and it is appropriate to do factor analysis.

All variables have eigenvalues greater than 1, being considered worth analyzing. The items with loading greater than 0.40 are regarded as significant, and retained. However, some items show significant values of loading in more than one construct. such as SCKC 3 and SCKC 4, it is not surprising logically. The seven variables all belong to activities of collaboration. When a company applies one type of collaborative activity, it may experience other collaborative activity types unconsciously as well. To jointly identify customer needs, there must be relevant information exchange between logistics companies and their customers. Therefore, such items were retained.

TABLE 3.2

Results of factor analysis including all measurement items.

Factor analysis

Construct Loading

Information sharing: α=0.91, CR=0.94, AVE=0.77

SCIS1 Our firm exchange relevant information with our customers. 0.921 SCIS2 Our firm exchange timely information with our customers. 0.895 SCIS3 Our firm exchange accurate information with our customers. 0.922 SCIS4 Our firm exchange complete information with our customer. 0.852 SCIS5 Our firm exchange confidential information with our customers. 0.788

Goal congruence: α=0.94, CR=0.95, AVE=0.82

SCGC1

Our firm have agreement on the goals of the supply chain with our

customers 0.854

SCGC2

Our firm have agreement on the importance of collaboration across the

supply chain with our customers 0.923

SCGC3

Our firm have agreement on the importance of improvements that benefit

the supply chain as a whole with our customers 0.91 SCGC4

Our firm agree that own goals can be achieved by working towards the

goals of the supply chain with our customers 0.921 SCGC5

Our firm jointly layout collaboration implementation plans to achieve the

goals of the supply chain with our customers 0.891

Decision synchronisation: α=0.87, CR=0.87, AVE=0.33

SCDS1 Our firm Jointly plan on product assortment with our customers. 0.896 SCDS2 Our firm Jointly plan on promotional events with our customers 0.823 SCDS3

Our firm has a joint development of demand forecasts with our

customers. 0.608

SCDS4 Our firm has a joint resolution on forecast exceptions with our customers 0.869 SCDS5 Our firm has a joint consultation on pricing policy with our customers 0.543 SCDS6 Our firm has a joint decision on availability level with our customers 0.79 SCDS7

Our firm has a joint decision on inventory requirements with our

customers 0.682

SCDS8

Our firm has a joint decision on optimal order quantity with our

customers 0.663

SCDS9 Our firm has a joint resolution on order exceptions with our customers. 0.856

Incentive alignment: α=0.90, CR=0.92, AVE=0.71

SCIA1

Our firm and our customers co-develop systems to evaluate and publicize each other’s performance (e.g. key performance index,

scorecard, and the resulting incentive) 0.796

SCIA2 Our firm and our customers share costs (e.g. loss on order changes) 0.745 SCIA3

Our firm and our customers share benefits (e.g. saving on reduced

inventory costs) 0.876

SCIA4

Our firm and our customers share any risks that can occur in the supply

chain 0.921

SCIA5 The incentive for our firm commensurate with our investment and risk 0.853

Resource sharing: α=0.90, CR=0.93, AVE=0.72

SCRS1

Our firm and our customers use cross-organizational teams frequently

for process design and improvement. 0.807

SCRS2

Our firm and our customers dedicate personnel to manage the

collaborative Processes. 0.834

SCRS3 Our firm and our customers share technical supports. 0.895 SCRS4

Our firm and our customers share equipment (e.g. computers, networks,

machines). 0.855

SCRS5

Our firm and our customers pool financial and non-financial resources

(e.g. time, 0.833

Collaborative communication: α=0.92, CR=0.95, AVE=0.78

SCCM1 Our firm and our customers have frequent contacts on a regular basis. 0.864 SCCM2 Our firm and our customers have open and two-way communication. 0.876 SCCM3 Our firm and our customers have informal communication. 0.907 SCCM4

Our firm and our customers have many different channels to

communicate. 0.925

SCCM5

Our firm and our customers influence each other’s decisions through

discussion 0.836

rather than request.

Joint knowledge creation: α=0.89, CR=0.93, AVE=0.75

SCKC1

Our firm and our customers jointly search and acquire new and relevant

knowledge 0.936

SCKC2

Our firm and our customers jointly assimilate and apply relevant

knowledge 0.91

SCKC3 Our firm and our customers jointly identify customer needs 0.868 SCKC4 Our firm and our customers jointly discover new or emerging markets 0.765 SCKC5

Our firm and our customers jointly learn the intentions and capabilities

of our 0.838

Recovery speed: α=0.82, CR=0.87, AVE=0.65

RPRS1 It would not take long to recover normal operating performance 0.897

RPRS2 Material flow would be quickly restored 0.928

RPRS3 Disruptions would be dealt with quickly 0.866

RPRS4 The supply chain would easily recover to its original state 0.433

Financial cost: α=0.84, CR=0.92, AVE=0.87

RPFC1

The cost of recovery is lower than other firms' which also affected by

extreme weather 0.931

RPFC2 The cost of recovery process is lower than prediction 0.931

Customer and production impact: α=0.89, CR=0.93, AVE=0.78

RPI1 Performance would not deviate significantly from targets 0.847 RPI2 The supply chain would still be able to carry out its regular functions 0.907

RPI3 We would still be able to meet customer demand 0.831

RPI4 Operations would be able to continue 0.931

Eigenvalue 3.844, 4.052, 3.526, 3.533, 3.572, 3.890, 2.619, 2.604, 1.733, 3.097 Percentage of variance explained (%) 76, 81.04, 35.178, 70.6, 71.4, 77.7, 52.4, 65.0, 86.6, 77.4.

discriminant validity as well. If the AVE for each factors is greater than the squared correlation of this factor and another, there is evidence of discriminant validity (Cao et al., 2010). None of squared correlation is higher than AVE for each individual construct. Therefore, discriminant validity can be ensured.

4.FINDINGS

TABLE 4.1

Correlations of all variables

Variable Mean SD SCIS SCGC SCDS SCIA SCRS SCCM SCKC RPRS RPFC RPI SCIS: Information sharing

4.276 ,87653 _

SCGC: Goal congruence

4.384 ,90238 ,814** _{_}

SCDS: Decision synchronisation

3.5922 ,61166 ,620** _,673** _{_}

SCIA: Incentive alignment

4.034 ,81998 ,824** _,848** _,657** _{_}

SCRS: Resource sharing

3.86 ,84924 ,703** _,779** _,653** _,835** _{_}

SCCM: Collaborative communication

4.412 ,84892 ,817** _,891** _,650** _,824** _,814** _{_}

SCKC: Joint knowledge creation

3.678 ,61555 ,707** _,677** _,614** _,714** _,715** _,740** _{_}

RPRS: Recovery speed

3.3725 ,52644 ,655** _,766** _,584** _,802** _,700** _,744** _,569** _{_}

RPFC: Financial cost

3.495 ,71596 ,722** _,800** _,573** _,792** _,758** _,790** _,627** _,806** _{_}

RPI: Impacts on customer and production

3.575 ,58441 ,759** _,832** _,598** _,834** _,748** _,823** _,650** _,802** _,879** _{_}

Note:**. Correlation is significant at the 0.01 level (2-tailed).

Table 4.1 shows the means, standard deviations, and correlations of all variables. Meanwhile, Variance inflated factors scores(VIF) and tolerance are calculated for the variables in each regression model. All VIF range from 2 to 4, and all values of tolerance are higher than 0.2 and less than 1, indicating that multicollinearity is not a serious problem for the regression analysis.

FIGURE 4.1

Types of extreme weather

4.1 Cold waves

Table 4.2 presents the regression results when the type of extreme weather is cold waves. Table 4.2 (a) shows the regression results of direct relationships, indicating the impacts of information sharing (H1), collaborative collaboration (H2), and joint knowledge creation (H3) on performance of recovery separately. As can be seen in Table (a), information sharing shows a significant impact on recovery speed (β= 0.454, p < 0.05), financial cost (β= 0.800, p < 0.001), and customer satisfaction and scheduled production(β= 0.671, p < 0.01) respectively. There is also a significant effect of collaborative communication on recovery speed (β= 0.787, p < 0.001), financial cost ( β = 0.937, p < 0.001), and customer satisfaction and scheduled production (β= 0.868, p < 0.001) separately. And joint knowledge creation has a significant direct effect on recovery speed (β= 0.574, p < 0.01), financial cost(β= 0.889, p < 0.001), and customer satisfaction and scheduled production(β= 0.800, p < 0.001) as well. Thus, H1,H2,and H3 are accepted when the type of extreme weather is cold waves. Number Storm 38 Droughts 3 Heavy precipitation 35 Cold waves 21 Heat waves 3 0 20 40 60 80 100 120 Nu mb er o f re sp on de nt s

TABLE 4.2 (a) Direct relationships

Cold waves Performance of recovery

Direct relationship Variable

Recover y speed 1 Recovery speed 2 Recover y speed 3 Financial cost 1 Financial cost 2 Financial cost 3 Impacts on customer and production 1 Impacts on customer and production 2 Impacts on customer and production 3 1 Information sharing ,454* ,800*** ,671** 2 Collaborative communication ,787*** ,937*** ,868*** 3 Joint knowledge creation ,574** ,889*** ,800*** R2 ,206 ,620 ,329 ,640 ,878 ,790 ,450 ,754 ,639 R2(adjusted) ,164 ,600 ,294 ,621 ,872 ,779 ,421 ,741 ,620 F 4.934** 30.972*** 9.332*** 33.785** 13.667*** 71,553*** 15,560** 58,279*** 33,699*** Change in R2 ,206 ,620 ,329 ,640 ,878 ,790 ,450 ,754 ,639 Change in F 4.934** 30.972*** 9.332*** 33.785** 1.3667*** 71,553*** 15,560** 58,279*** 33,699*** Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

TABLE 4.2 (b)

Moderating roles of decision synchronisation and incentive alignment

Performance of recovery

Steps Variable Recovery speed Financial cost Impacts on customer and production Moderating role of decision synchronisation in the relationship between information sharing and performance of recovery

1 Zscore(SCIS):Information sharing ,573* ,604** ,570* 2 Zscore(SCDS): Decision synchronisation ,525* ,508*** ,569 3 ZSCIS_X_SCDS ,551* ,106 ,282** R2 ,604 ,839 ,750 R2(adjusted) ,534 ,811 ,706 F 8.644 29.574 1.6981*** Change in R2 ,137 ,005 ,036 Change in F 7.877 ,540 2.433*** Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

Performance of recovery Steps Variable Recovery speed Financial cost Impacts on customer and production Moderating role of Incentive alignment in the relationship between information sharing and performance of recovery

1 Zscore(SCIS)：Information sharing ,209 ,776*** ,276 2 Zscore(SCIA): Incentive alignment ,854*** ,430** ,760***

TABLE 4.2 (c)

Moderating role of goal congruence

Performance of recovery

Steps Variable Recovery speed Financial cost Impacts on customer and production

Zscore(SCCM)：Collaborative communication 1** ,769** ,781** 2 Zscore(SCGC): Goal congruence ,306 ,269 ,417 3 ZSCCM_X_SCGC ,668** ,107 ,399* R2 ,797 ,889 ,821 R2(adjusted) ,761 ,869 ,789 F 22.204*** 45.255*** 25.915*** Change in R2 ,175 ,004 ,062 Change in F ,001** ,684 5.908* Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

TABLE 4.2 (d)

moderating role of resource sharing

Performance of recovery Steps Variable Recovery speed Financial cost

Impacts on customer and production Moderating role of resource sharing in the relationship between joint knowledge creation and performance of recovery

1 Zscore(SCKC)：Joint knowledge creation ,611** ,927*** ,558*** 2 Zscore(SCRS): Resource sharing ,931*** ,403** ,844*** 3 ZSCKC_X_SCRS ,958*** ,442** ,524*** R2 ,866 ,898 ,949 R2(adjusted) ,842 ,880 ,940 F 36,518*** 49,707*** 106,261*** Change in R2 ,316 ,067 ,095 Change in F 40,003*** 11.197** 31,772*** Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

C, indicating simple slopes with low and high level of moderating variables. As can be seen, the slopes are positive and significant with high level of moderating variables. Therefore, when decision synchronisation is high, information sharing shows a more significant positive effect on recovery speed (Figure 4.2a in Appendix B), and customer satisfaction and scheduled production( Figure 4.2b in Appendix B ). Similarly, information sharing enhances recovery speed (Figure 4.3a in Appendix C) and financial cost significantly (Figure 4.3b in Appendix C) with high level of incentive alignment.

Then, Table 4.2 (c) shows the results of the interaction effect of goal congruence and collaborative communication. The interaction effect of goal congruence on recovery speed (β= 0.668, p < 0.01), and customer satisfaction and scheduled production (β= 0.399, p < 0.05) is significant. While, we failed to find a significant effect on financial cost(β= 0.107, ns). The interaction effect of resource sharing and joint knowledge creation is shown as Table 4.2 (d). There is a significant interaction effect on recovery speed (β= 0.958, p < 0.001), financial cost (β= 0.442, p < 0.01), and customer satisfaction and scheduled production (β= 0.524, p < 0.001) respectively. Therefore, we accept H6, H7(a), and H7(c). The two-way interaction plots are shown as Figure 4.4-4.5 in Appendix D and E. With the high level of moderating variables, the slopes all changed positively and significantly.

4.2 Heavy precipitation

TABLE 4.3(a) Direct relationships Performance of recovery Direct relationship Variable Recovery speed 1 Recovery speed 2 Recovery speed 3 Financial cost 1 Financial cost 2 Financial cost 3 Impacts on customer and production 1 Impacts on customer and production 2 Impacts on customer and production 3 1 Information sharing ,592*** ,588*** ,725*** 2 Collaborative communication ,733*** ,723*** ,856*** 3 Joint knowledge creation ,437** ,407** ,486** R2 ,351 ,538 ,191 ,346 ,522 ,165 ,526 ,733 ,236 R2(adjusted) ,331 ,524 ,166 ,326 ,508 ,140 ,512 ,725 ,213 F 17.834*** 38.388*** 7.779** 17.479*** 36.067*** 6.534** 36.635*** 90.742*** 10.215** Change in R2 ,351 ,538 ,191 ,346 ,522 ,165 ,526 ,733 ,236 Change in F 17.834*** 38.388*** 7.779** 17.479*** 36.067*** 6.534** 36.635*** 90.742*** 10.215** Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

TABLE 4.3(b)

Moderating roles of decision synchronisation and incentive alignment

Performance of recovery

Steps Variable Recovery speed Financial cost Impacts on customer and production Moderating role of decision synchronisation in the relationship between information sharing and performance of recovery

1 Zscore(SCIS):Information sharing ,962*** ,979*** 1,138*** 2 Zscore(SCDS): Decision synchronisation ,436** ,245 ,377** 3 ZSCIS_X_SCDS ,868*** ,722** ,869*** R2 ,673 ,549 ,834 R2(adjusted) ,642 ,505 ,818 F 21.283*** 12.562*** 51,979*** Change in R2 ,283 ,196 ,283 Change in F 26.804*** 13.435** 52,975*** Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

Performance of recovery

Steps Variable Recovery speed Financial cost Impacts on customer and production Moderating role of Incentive alignment in the relationship between information sharing and performance of recovery

1 Zscore(SCIS)：Information sharing ,507 ,182 ,371 2 Zscore(SCIA): Incentive alignment ,737** ,826** ,940*** 3 ZSCIS_X_SCIA ,743*** ,438* ,634*** R2 ,665 ,544 ,844 R2(adjusted) ,633 ,500 ,829 F 20,557*** 12,349*** 55,850*** Change in R2 ,209 ,072 ,152 Change in F 19,363*** 4,931* 30.248*** Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

TABLE 4.3(c)

Moderating role of goal congruence

Performance of recovery

Steps Variable Recovery speed Financial cost Impacts on customer and production Moderating role of Goal congruence in the relationship between collaborative communication and performance of recovery

1 Zscore(SCCM)：Collaborative communication ,587** ,289 ,641*** 2 Zscore(SCGC): Goal congruence ,763** ,748* ,731*** 3 ZSCCM_X_SCGC ,758*** ,353* ,624*** R2 ,840 ,638 ,953 R2(adjusted) ,824 ,603 ,949 F 54,113*** 18,228*** 211,273*** Change in R2 ,254 ,055 ,172 Change in F 49,051** 4.712* 114,520*** Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

TABLE 4.3(d)

Moderating role of resource sharing

Performance of recovery

Steps Variable Recovery speed Financial cost Impacts on customer and production Moderating role of resource sharing in the relationship between joint knowledge creation and performance of recovery

1 Zscore(SCKC)：Joint knowledge creation ,333** ,260* ,402*** 2 Zscore(SCRS): Resource sharing ,726*** ,787*** ,796*** 3 ZSCKC_X_SCRS ,436** ,399** ,540*** R2 ,629 ,656 ,790 R2(adjusted) ,593 ,623 ,770 F 17,506*** 19,716*** 38,881*** Change in R2 ,136 ,114 ,209 Change in F 11,381** 10,296** 30,925*** Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

Incentive alignment shows the similar moderating roles in the relationship between information sharing and performance of recovery (recovery speed: β= 0.743, p < 0.001;financial cost: β= 0.438, p < 0.05; impacts on customer and production: β= 0.634, p < 0.001). The interaction effect can be observed more intuitively in two-way interaction plots (Figure 4.6-4.7 in Appendix F and G). With high level of decision synchronisation, information sharing shows a more significant and positive effect on performance of recovery. There are similar status for incentive alignment. The slopes are higher with the high level of incentive alignment. So, we accept H4 and H5.

0.001), financial cost (β= 0.353, p < 0.05), and customer and production (β= 0.624, p < 0.001) are significant. The interaction effect between resource sharing and joint knowledge creation is significant as well(recovery speed: β = 0.436, p < 0.01;financial cost: β= 0.399, p < 0.01; impacts on customer and production: β= 0.540, p < 0.001). Thus, H6 and H7 are accepted. And, the two-way interaction plots are shown as Figure 4.8-4.9 in Appendix H and I. In a situation with high level of moderating variables, the slop is positive and more significant, indicating independent variables are significantly and positively related to dependent variables.

4.3 Storm

Table 4.4(a) presents the results about the direct effect of information sharing, collaborative communication, and joint knowledge creation on performance of recovery after storm. There are significant impacts of information sharing on recovery speed (β= 0.839, p < 0.001), financial cost (β= 0.750, p < 0.001), and customer and production (β= 0.820, p < 0.001). Similarly, collaborative communication shows a significant effect on recovery speed (β= 0.706, p < 0.001), financial cost (β= 0.718, p < 0.001), and customer and production ( β = 0.743, p < 0.001) separately. Meanwhile, there is linear relationship between joint knowledge creation and performance of recovery (recovery speed: β= 0.588, p < 0.001;financial cost: β= 0.592, p < 0.001; impacts on customer and production: β= 0.624, p < 0.001). Then, H1, H2 and H3 are accepted.

TABLE 4.4(a) Direct relationships

Performance of recovery

Direct relationship Variable Recovery _{speed 1} Recovery _{speed 2} Recovery _{speed 3} Financial _{cost 1} Financial _{cost 2} Financial _{cost 3}

Impacts on customer and production1 Impacts on customer and production 2 Impacts on customer and production 3 1 Information sharing ,839*** ,750*** ,820*** 2 Collaborative communication ,706*** ,718*** ,743*** 3 Joint knowledge creation ,588*** ,592*** ,624*** R2 0,703 0,498 0,346 0,562 0,515 0,35 0,672 0,551 0,389 R2(adjusted) 0,695 0,484 0,328 0,55 0,502 0,332 0,663 0,539 0,372 F 85,395*** 35,742*** 19,039*** 46,147*** 38,251*** 19,378*** 73,870*** 44,237*** 22,952*** Change in R2 0,703 0,498 0,346 0,562 0,515 0,35 0,672 0,551 0,389

Change in F 85,395*** 35,742*** 19,039*** 46,147*** 38,251*** 19,378*** 73,870*** 44,237*** 22,952***

Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

TABLE 4.4(b)

Moderating roles of decision synchronisation and incentive alignment

Performance of recovery

Steps Variable Recovery speed Financial cost Impacts on customer and production Moderating role of decision synchronisation in the relationship between information sharing and performance of recovery

1 Zscore(SCIS):Information sharing ,782*** 654*** ,813*** 2 Zscore(SCDS): Decision synchronisation ,157 ,152 ,050 3 ZSCIS_X_SCDS ,036 _,061 ,036 R2 ,724 ,583 ,675 R2(adjusted) ,700 ,546 ,647 F 29,735*** 15,845*** 23,574*** Change in R2 ,001 ,003 ,001 Change in F ,128 ,244 ,111 Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

Performance of recovery

Steps Variable Recovery speed Financial cost Impacts on customer and production Moderating role of Incentive alignment in the relationship between information sharing and performance of recovery

1 Zscore(SCIS)：Information sharing ,345 ,115 ,421 2 Zscore(SCIA): Incentive alignment ,570** ,753** ,580** 3 ZSCIS_X_SCIA ,005 ,028 ,140 R2 ,782 ,700 ,769 R2(adjusted) ,763 ,674 ,749 F 40,632*** 26,504*** 37,802*** Change in R2 ,000 ,000 ,008 Change in F ,001 ,037 1,184 Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

TABLE 4.4(c)

Moderating roles of goal congruence

Performance of recovery

Steps Variable Recovery speed Financial cost Impacts on customer and production Moderating role of Goal congruence in the relationship between collaborative communication and performance of recovery

1 Zscore(SCCM)：Collaborative communication ,163 ,215 ,341 2 Zscore(SCGC): Goal congruence ,997*** ,824** ,815*** 3 ZSCCM_X_SCGC ,464** ,306* ,440** R2 ,731 ,663 ,719 R2(adjusted) ,707 ,634 ,695 F 30,775*** 22,333*** 29,054*** Change in R2 ,096 ,042 ,086 Change in F 12,091** 4,210* 10,435** Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

TABLE 4.4(d)

Moderating roles of resource sharing

Performance of recovery

Steps Variable Recovery speed Financial cost Impacts on customer and production Moderating role of resource sharing in the relationship between joint knowledge creation and performance of recovery

1 Zscore(SCKC)：Joint knowledge creation ,254 _,043 ,435 2 Zscore(SCRS): Resource sharing ,507* ,666* ,433* 3 ZSCKC_X_SCRS ,096 _,141 ,213 R2 ,436 ,535 ,464 R2(adjusted) ,386 ,494 ,417 F 8,757*** 13,042*** 9,809*** Change in R2 ,004 ,009 ,020 Change in F ,245 ,641 1,275 Note: Standardized regression coefficient*p < 0.05, **p < 0.01, ***p < 0.001

5.DISCUSSION

The research aims to explore the effect of different collaborative activity on performance of recovery after extreme weather in the logistics industry, focusing on three dimensions of performance of recovery: recovery speed, financial cost, and impacts on customer and production. Furthermore, the differences among the roles of collaborative activities in recovery are tested and identified.

5.1 The effect of collaborative activities

The findings concentrate on three types of extreme weather due to the limitation of data: cold waves, heavy precipitation, and storm. For each type of extreme weather, seven hypotheses are tested respectively, indicating the impacts of different collaborative activity based on the type of extreme weather. The direct effect of information sharing, collaborative communication, and joint knowledge creation on recovery are proved in all types of extreme weather. In order to accomplish information sharing and collaborative communication, organizations have to create the linkages across the supply chain to enhance the visibility of supply chain (Jüttner & Maklan, 2011), which improves operational efficiency obviously. Customers and organizations can understand each other's status more accurately and timely, such as the visibility of demand information that helps to reduce distorted demand signals(Yan & Dooley, 2013). Therefore, the research provides empirical evidence about the positive effect of collaboration on supply chain resilience (BrandonJones et al., 2014; Scholten & Schilder, 2015).

5.2 The influence of different type of extreme weather

observed, and people can make some preparations based on prediction. In addition, decision synchronisation involves the structure of coordination, the joint decisions guide operations and processes (Cao & Zhang, 2011). To making joint decision, logistics companies and customers need to build linkages and discussions, which may involves more human resources and cost. The moderating effect of incentive alignment on the relationship between information sharing and impacts on customer and satisfaction is rejected as well. Previous literature points out that appropriate incentives motivate chain members to take decisions according to the achievement of supply chain profitability, making chain members to commit to the collaborative efforts (Simatupang, & Sridharan, 2004). In addition, we also failed to find significant interaction effect of goal congruence and collaborative communication on financial cost of recovery. Based on previous literature, goal alignment created shared interests across organizational boundaries (Yan & Dooley, 2013). When a organization builds collaborative communication with its customers to dealing with a disruption, the agreement of goals with customers can smooth the process of communication, and contribute to integrated action. The results may also relate to logistics companies' own goals. For heavy precipitation, all hypotheses about interaction effect are accepted. While, for storm, only the interaction effect between goal congruence and collaborative communication on performance of recovery is proved. It may be related to the severity of extreme weather. The storm also can bring heavy precipitation, indicating a larger scale of negative impacts (Coumou & Rahmstorf, 2012). So, the damages caused by storm are hard to predict and calculate accurately. For example, tropical storms can develop very quickly, beginning in an ocean and moving towards lands. People take efforts to predict the route of a tropical storm, and evaluate the effect on lands. But, things are not ideal, the route sometimes changes (Takagi et al., 2017). Such characteristics of extreme weather directly weaken the effect of actions taken by organizations.

TABLE 5.1 Summary Type of extreme weather Hypoth eses Independent variable Moderating variable

Dependent variable Result

Cold waves H1(a) Information sharing Recovery speed Significant and

positive effect H1(b) Information sharing Financial cost Significant and

positive effect H1( c) Information sharing Customer and

production impact

Significant and positive effect H2(a) Collaborative communication Recovery speed Significant and

positive effect H2(b) Collaborative communication Financial cost Significant and

positive effect H2( c) Collaborative communication Customer and

production impact

Significant and positive effect H3(a) Joint knowledge creation Recovery speed Significant and

positive effect H3(b) Joint knowledge creation Financial cost Significant and

positive effect H3( c) Joint knowledge creation Customer and

production impact Significant and positive effect H4(a) Information sharing Decision synchronisation

Recovery speed Significant and positive effect H4(b) Information

sharing

Decision synchronisation

Financial cost Non-significant H4( c) Information sharing Decision synchronisation Customer and production impact Significant and positive effect H5(a) Information sharing Incentive alignment

Recovery speed Significant and positive effect H5(b) Information

sharing

Incentive alignment

Financial cost Significant and positive effect H5( c) Information sharing Incentive alignment Customer and production impact Non-significant H7(a) Collaborative communication Goal congruence

Recovery speed Significant and positive effect H7(b) Collaborative

communication

Goal congruence

Financial cost Non-significant H7( c) Collaborative communication Goal congruence Customer and production impact Significant and positive effect H6(a) Joint knowledge

creation

Resource sharing

Recovery speed Significant and positive effect H6(b) Joint knowledge

creation

Resource sharing

Financial cost Significant and positive effect H6( c) Joint knowledge creation Resource sharing Customer and production impact Significant and positive effect Heavy precipitation

H1(a) Information sharing Recovery speed Significant and positive effect H1(b) Information sharing Financial cost Significant and

positive effect H1( c) Information sharing Customer and

production impact

Significant and positive effect H2(a) Collaborative communication Recovery speed Significant and

positive effect H2(b) Collaborative communication Financial cost Significant and

positive effect H2( c) Collaborative communication Customer and

production impact

Significant and positive effect H3(a) Joint knowledge creation Recovery speed Significant and

positive effect H3(b) Joint knowledge creation Financial cost Significant and

positive effect H3( c) Joint knowledge creation Customer and

production impact