Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Logistics innovation capability and its impacts on the supply chain risks in the Industry 4.0 era

Logistics innovation capability and its impacts on the supply chain risks in the Industry 4.0 era Purpose – The purpose of the paper is to present an empirical study on the logistics innovation capability and its impacts on the supply chain risk in the Australian courier firms. Based on the resource-based review, logistics innovation capability provides valuable insight into mitigating supply chain risks in the Industry 4.0 era. Design/methodology/approach – The research model focuses on the relationships between logistics innovation capability and supply chain risk. Partial least squares approach for structural equation modelling is used to validate the research model by empirically analysing survey data. Findings – The empirical result shows negative relationships between logistics innovation capability and supply chain risks. These relationships may imply that firms can mitigate the negative impacts of supply chain risks by developing logistics innovation capabilities. The findings demonstrate the applicability of logistics innovation capability for mitigating supply chain risks in the Australian courier firms. Originality/value – There are very few empirical studies on the mitigating supply chain risk through logistics innovation capability. The empirical results provide an insight into innovation management and risk management in logistics and supply chain. This insight offers practical guidance for developing and deploying logistics innovation capability to support and enable supply chain risk management strategies in the Industry 4.0 era. Keywords Innovation, Capability, Supply chain risk, Logistics management, Transport, Industry 4.0 Paper type Research paper 1. Introduction As market competition for logistics and transport increases, a growing number of logistics and transport firms try to pursue the operational excellence so that the company can provide the excellent logistics operations to gain a firm’s market share for sustainable development in © Michael Wang, Sean Asian, Lincoln C. Wood and Bill Wang. Published in Modern Supply Chain Research and Applications. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http:// Modern Supply Chain Research and Applications creativecommons.org/licences/by/4.0/legalcode This work was supported, in part, by the New Zealand Forest Research Institute Ltd (Scion). The pp. 83-98 Emerald Publishing Limited authors would like to thank the anonymous reviewers and editors for their valuable and constructive 2631-3871 comments. DOI 10.1108/MSCRA-07-2019-0015 the long term (Christopher, 1996). This is one of the important objectives of the fourth MSCRA industrial revolution (Industry 4.0), which accelerates the development of innovation 2,2 capability in various industries (Frank et al., 2019; Lasi et al., 2014). Supply chain risk is one of the obstacles to achieving operational excellence. Many researchers urge that supply chain risk is a problem in supply chain and logistics (Davis, 1993; Lee, 2002; Miller, 1992; Prater, 2005; Vasco et al., 2010; Wang, 2018). In addition, supply chain risk management plays a vital role in building resilience and achieving sustainability of organisations (Christopher and Peck, 2004). Many previous supply chain risk studies focus on supply chain risk identification and classification (Manuj and Mentzer, 2008; Simangunsong et al., 2012; Sodhi and Lee, 2007; Tang and Nurmaya Musa, 2011; Wang and Jie, 2019). However, lacking resolutions of supply chain risk is a problem, which has also been raised in previous studies (Borut et al., 2012; Guido et al., 2008; Sanchez-Rodrigues et al., 2010; Simangunsong et al., 2012; Wang, 2018). Moreover, the world-leading logistics and transport firm DHL recognises that the supply chain risk may hamper firms towards operational excellence, which is achieved through a focus on systems, cost-effectiveness and speed (Christopher, 1996). Therefore, it is significant to investigate a contingency way to manage supply chain risks. Innovation capability has a long history of research; it was developed from the resource-based view (RBV) theory. Firms can gain and sustain competitive advantages by developing and deploying valuable resources and capabilities (Olavarrieta and Ellinger, 1997; Wernerfelt, 1984). The RBV theory states that in a firm, the resources comprise skills, technologies, capabilities and infrastructure. The resources and capabilities have to be coordinated and deployed in order to generate competitive advantages (Mohamed et al., 2014). Furthermore, capabilities are complex bundles of skills and accumulated knowledge, exercised through organisational processes, which enable firms to coordinate activities and make use of their assets (Day, 1994). Hafeez et al. (2002) regard capability as the ability to make use of resource to perform some task or activity and define a resource as anything tangible or intangible owned or acquired by a firm. RBV of the firm provides valuable insights for understanding how competitive advantage within firms is created and how the advantage is sustained over time. The companies obtain a competitive advantage by accumulating internal resources and capabilities that are rare, valuable and difficult to imitate (Grant, 1991; Olavarrieta and Ellinger, 1997; Russo and Fouts, 1997). Implementing logistics innovation to solve the problems in logistics and transport is not new (Daniel and Fredrik, 2011; Flint et al., 2005; Scott, 2009). Industry 4.0 is rooted in the smart manufacturing concept, which required new technologies and innovation capabilities (Frank et al., 2019). The logistics service providers compete based on their capabilities (Lai, 2004; Wang, 2016b). Logistics capability improves logistics operations, and an effective logistics operation generates competitive advantage and gains a firm’s market share (Mentzer and Flint, 1999; Scott, 2009; Wang et al., 2018). Seeking perfection is one of the key principles of operational excellence in the Industry 4.0. In this study, we argue that logistics innovation capability may help firms to manage supply chain risks through excellent logistics operations. According to a contingency theory, this study attempts to understand the associates between the logistics innovation capabilities and supply chain risks. The findings may imply the supply chain risk management strategies and applicability of logistics innovation for mitigating the supply chain risk in the Australian courier firms. Supply chain risks mainly reflect the negative impacts on logistics operations, such as delays, damage and loss (Sanchez-Rodrigues et al., 2010). Excellent logistics operations need to reduce and mitigate negative impacts. The findings reveal that logistics innovation capability mitigates the impacts of the supply chain risks. This also supports the Industry 4.0 research. The rest of the paper is structured as follows. Section 2 provides a literature review on logistics innovation capability and supply chain risk. Section 3 shows the research model and hypotheses development. Section 4 and 5 present the research methodology and empirical Logistics data analysis. Section 6 discusses the findings, implications and conclusion. innovation in the industry 2. Literature review 4.0 era Logistics innovation capability Innovation is an important logistics capability (Fawcett and Stanley, 1997; Hayes et al., 1988; Lu and Yang, 2010; Morash 1996, 2001). Innovation capability is considered as a dynamic capability (Lawson and Samson, 2001). Dynamic capabilities theory was developed from the RBV theory. Teece et al. (1997) developed the RBV approach one step further by formulating the dynamic capability perspective. Innovation capability is defined as the firm’s ability to continuously transform knowledge and ideas into new products, processes and systems for the benefit of the firm (Lawson and Samson, 2001; Yang, 2012). A dynamic capability is distinct from operational capabilities, which is involved in the current operations of an organisation. Dynamic capabilities, by contrast, refer to “thecapacityof anorganisationto purposefully create, extend, or modify its resource base”(Helfat, 2007). Winter (2003) describes two different types of capabilities: ordinary or ‘zero-level’ capabilities as those that allow a firm to ‘make a living’ in the short term; the other type of capability is dynamic capability. The latter one is higher level capabilities, which can be deployed to extend, modify or create the ordinary or ‘zero-level’ capabilities (Winter, 2003). Logistics innovation capability is considered as a higher level capability to reconfigure operation capabilities to achieve excellent logistics operations and mitigate supply chain risk. Innovation can reconfigure and transform both external and internal resources in order to adapt the company’s strategy (Teece et al.,1997). Logistics innovation refers to the new technology, new services, new processes and new ideas, which are used for improving logistics operations (Scott, 2009). In this paper, the logistics innovation capability is an ability to incorporate the logistics innovation to solve the problem and adapt to a fluctuating environment in a supply chain. The rapidly changing and uncertain business environment raises challenges to enterprises, and innovation is an important tool for keeping their competitive advantage (Lin, 2006). Notably, some researchers found innovation can be used for reducing supply chain risk (Daniel and Fredrik, 2011; Lin, 2013). Dani (2010) emphasises that building innovative culture, innovative processes and innovation capability are key to managing and mitigating supply chain risks. Also, innovation has positive effects on logistics service and is critical for strengthening the Logistics Service Provider (LSP)–customer relationship, generating customer loyalty, achieving competitive advantage and improving the performance of logistics service firms (Asian, 2019; Flint et al., 2005; Wagner and Sutter, 2012). Lin (2013) suggests that logistics service providers pay more attention to innovation capability, to provide better services for their customers. For example, FedEx is one of the most successful and innovative 3PL providers in the world. Its overnight delivery service changed how business-to-business and business-to- customer transactions operated, offering businesses the opportunity of using just-in-time techniques which saved warehousing space and reduced overall costs. The introduction of efficient consumer response techniques led to smaller and more efficient shipment sizes, which in turn further reduced costs (Black and Hunter, 2003; Wang, 2016a; Wei et al., 2018). Supply chain risk There are multiple ways to define the concept of risk. Everyone has his/her own perspectives to understand and justify the risk. Thus, it is significant to consider the risk factors in a specific context. This study concentrates supply chain risks in logistics and transport operations. For managers, the risk is a threat that something might happen to disrupt normal activities or stop things happening as planned (Flynn et al., 2016; Waters, 2011). Risks occur because people never know what will happen in the future. People can use the MSCRA best forecasts and do every possible analysis, but there is always uncertainty about future 2,2 events (Waters, 2011). Supply chain risk is a complex notion that may come in different forms and may comprise supply chain risk sources, risk consequences and risk drivers (Christopher and Lee, 2004; Juttner € et al., 2003; Manuj and Mentzer, 2008; Rodrigues et al., 2008). In the supply chain risk management literature, the risk is due to unreliable and uncertain resources creating supply chain interruption (Tang and Nurmaya Musa, 2011). In this study, supply chain risk is considered as the impacts, consequences and/or errors (e.g., delays, damages and loss) that may harm the logistics operations. According to an extensive review of the literature and previous studies, supply chain risk is categorised into three major clusters in this study, (1) company-side risk, (2) customer-side risk and (3) environment-side risk (Murugesan et al., 2013; Sanchez-Rodrigues et al., 2010; Simangunsong et al., 2012; Wang, 2018; Wang et al., 2014). Courier companies deliver a parcel from a point of pickup to a point of destination. Normally, three parties are directly involved in a typical courier delivery: senders, receivers and delivery companies (Wang, 2016a). The three categories balance and simplify the way to assess internal, external and environmental risks in the courier industry (Christopher and Peck, 2004; Miller, 1992). Company-side risk. In this paper, supply chain risks are concentrated on logistics and transport service providers. The company-side risk can broadly be categorised as the potential disturbances to the flow of goods and information (Ellegaard, 2008). It consists of logistics risk and information risk. Logistics risk is defined as weakness, fault, error, loss and/or unexpected outcome that may influence normal logistics-related activities/processes in transport service providers. Some factors of logistics risk in the literature relating to courier companies include delays in delivery time (Rodrigues et al., 2008; Simangunsong et al., 2012), transport network management (Sanchez-Rodrigues et al., 2008), storage issues (Hauser, 2003), carrier strength (Hauser, 2003) and freight transport operations (Sanchez-Rodrigues et al.,2010). They may disrupt normal operations and cause problems (Hauser, 2003; Rodrigues et al., 2008; Simangunsong et al., 2012). Information risk is defined as information-related unexpected incidents; outcome and/or problems may influence the information in time, accuracy and availability in logistics and transport service providers. Information is the aider in the smooth functioning of the supply chain. The information risk addressed in the literature includes unavailability of information (Guo et al., 2006), information delays (Cucchiella and Gastaldi, 2006), breakdowns of information infrastructure (Blackhurst et al., 2008) and other information and communication issues (Sanchez-Rodrigues et al.,2010). Customer-side risk. Customer-side risk mainly occurs from customer-related processes, e.g., enquiry and quotation, order receipt, order processing and order amendment. In this paper, customer-related risk, a type of supply chain internal risk which mainly originates from the customer side rather than the company side, may cause disputes and/or influence the normal logistics operations in logistics service providers. Due to the nature of courier delivery, both the consignee (receiver) and the consignor (sender) involved in a transaction play an equally important role in the delivery process (Wang, 2011). It is important to consider both receivers and senders together. In supply chain literature, there are various customer- related risks, e.g., unanticipated customer, reputation, forecast error, delays to customer and receivable risks (Chopra and Sodhi, 2004; Manuj and Mentzer, 2008; Sodhi and Lee, 2007; Sodhi and Tang, 2012). Environment-side risk. Supply chain risk can arise due to the interactions between the supply chain network and its external environment/events. The environmental risk is an important type of supply chain risk; it has been discussed widely in previous studies (Manuj and Mentzer, 2008; Simangunsong et al., 2012; Sodhi and Lee, 2007; Zsidisin, 2003). In supply chain literature, most environment risks are unavoidable; e.g., driver shortage, road congestions/closures, regulations and natural disaster. Recently, the terrorist attacks, piracy and unstable politics result in more supply chain risks. As a logistics service provider, courier Logistics companies need to consider the external environmental factor beforehand. It is significant to innovation in study the environment-side risk in the supply chain. Therefore, in this study, the the industry environment-side risk includes labour, road congestion, natural disasters, fuel price and 4.0 era regulations. (Blackhurst et al., 2008; McKinnon and Ge, 2004; Sanchez-Rodrigues et al., 2010; Simangunsong et al., 2012). Supply chain risk mitigation Mitigation is one of the important strategies to manage supply chain risks. Manuj and Mentzer (2008) summarise the existing literature from supply chain and related disciplines to suggest a five-step process for supply chain risk management. The steps consist of risk identification, risk assessment and evaluation, selection of appropriate risk management strategies, strategy implementation and supply chain risk mitigation. Mitigation of supply chain risks is an important part of supply chain risk management. Juttner € et al. (2003) suggest the basic constructs of supply chain risk management, including mitigating risks in a supply chain. From a single company view in a supply chain, Miller (1992) suggests risk mitigation strategies including avoidance, control, cooperation and flexibility. The major categories of techniques of managing the risk include avoidance (i.e., eliminating, withdrawing from or not being involved in the risk), reduction (optimising and mitigating the risk), sharing (transferring, outsourcing or insuring the risk) and retention (accepting and budgeting) (Dorfman, 2008). Furthermore, mitigating supply chain risks does not try to influence or alter the source of risk. Instead, it tries to find ways to adapt and hence minimise the impact of risk (Simangunsong et al., 2012; Wang, 2016a). The supply chain is a complex system consisting of various enterprises processes and relationships (Pettit et al., 2013), and each party connects to the different business partners via separate channels (Lambert and Cooper, 2000). Any change in one party could cause new issues, unexpected consequences and chain reactions in an entire supply chain. Chopra and Sodhi (2004) summarise that capability development is a risk mitigation approach in the supply chain. It minimises the negative supply chain risk impacts. In this paper, the logistics innovation capability is considered as a supply chain risk mitigation tool to manage the risks and improve supply chain resilience. In the Industry 4.0 era, companies rely on new technologies to achieve better business performance (Frank et al., 2019; Gilchrist, 2016). Logistics innovation capability includes the ability to manage technology and process innovation. Furthermore, the study investigates whether logistics innovation capability affects supply chain risk. 3. Research model and hypothesis development With the development of Industry 4.0, new technologies and ideas have been widely implemented in the industries. Logistics and supply chain plays a vital role in delivering and connecting the sectors. This may imply the importance of the innovation capability in the logistics and supply chain to support the Industry 4.0 research (Frank et al.,2019; Lasi et al., 2014). Logistics innovation capability is heavily involved in logistics and transport operations; it provides opportunities for improving the business performance, as in packaging innovations that decrease the risk of damaging goods in transit (Daniel and Fredrik, 2011). Containerisation is a typical example of innovating to improve logistics operations and reduce the risks and uncertainty in transit (Scott, 2009). The logistics and transport industry has seen many examples of logistics innovation, for example, smart packaging has become an important research topic in the smart supply chain (Frank et al., 2019; Schaefer and Cheung, 2018). This creative idea can minimise company-side supply chain risks, e.g., reducing waste and improving information sharing (Lasi et al.,2014; Schaefer and Cheung, 2018; Wollschlaeger et al.,2017). Logistics innovation is capable of managing the logistics risks, which is considered MSCRA as an important enterprise risk (Scott, 2009; Simangunsong et al.,2012). In this study, the 2,2 logistics innovation capability comprises new technologies and services, such as real-time online tracking and tracing service; this has been widely used in logistics companies to provide accurate and timely information. The information technologies reduce the supply chain risks, which are caused by delay or unavailability of information (Christopher and Lee, 2004). Material requirement planning (MRP) systems are used to improve the information manipulation, resulting in reducing supply chain risk in firms (Chrwan-jyh and Phillip, 1994). Thus, the following hypothesis is proposed: H1. Logistics innovation capability is negatively related to company-side risk. Logistics innovation capability is critical for strengthening the LSP–customer relationship, generating customer loyalty (Asian, 2019; Flint et al., 2005; Wagner and Sutter, 2012). Today’s customers have much higher expectations than ever before; many new problems and unexpected issues required creative problem-solving processes. The logistics innovation capability provides an opportunity to resolve the relevant problems and issues, and develop customer relationships. Furthermore, a good relationship between logistics firms and customers may reduce the customer-related uncertainty and risk. For example, FedEx’s overnight delivery service changed how business-to-business and business-to-customer transactions operated. This offered businesses the opportunity of using just-in-time techniques, which saved warehousing space and reduced overall costs. The introduction of efficient consumer response (ECR) techniques led to smaller and more efficient shipment sizes, which in turn further reduced the risks (Black and Hunter, 2003). Thus, the following hypothesis is proposed: H2. Logistics innovation capability is negatively related to customer-related risk. Environmental risk is an important supply chain risk (Ho et al., 2015). The environmental fluctuations caused unexpected changes, risks and uncertainties, which have impacts on the company performance (Lu et al., 2018; Szu-Yuan et al., 2009), and logistics innovation capability can influence the company performance (Wang, 2018; Wang et al., 2018). One example of logistics innovation capability reducing environment-side risk is logistics companies introduced a flexible fuel factor or fuel surcharge against unstable fuel price (Hoffman, 2006). Today, most transport and logistics companies introduced the fuel surcharge into the delivery cost. Moreover, using new technologies and creative ideas to mitigate environmental uncertainty and risk have been promoted in previous studies (Hayes et al., 1988; Kim, 2006). Thus, the following hypothesis is proposed: H3. Logistics innovation capability is negatively related to environmental risk. Overall, the research framework comprises logistics innovation capability and supply chain risk. The measurements of logistics innovation capability in this paper are drawn from previous studies (Braunscheidel and Suresh, 2009; Hayes et al., 1988; Wang et al., 2015). The three categories of supply chain risk, including company-side risk, customer-side risk, and environment-side risk (Sanchez-Rodrigues et al., 2010; Simangunsong et al., 2012), are used to assess the supply chain risks in the Australian courier firms. The conceptual research framework is indicated in Figure 1. 4. Research methods The study aims to investigate the relationships between logistics innovation capability and the different types of supply chain risks, including company-side risk, customer-side risk, and environment-side risk (Wang, 2018). The partial least squares approach for structural Logistics Supply Chain Risk innovation in the industry Company- 4.0 era side H1 H2 Logistics Customer- Innovation side H3 Environment Figure 1. Research framework equation modelling (PLS-SEM) is applied to validate the research model by empirically analysing survey data. The SmartPLS version 2.0.M3 statistical software package is used for data analysis. The questionnaire was reviewed and discussed with supply chain and logistics academicians, researchers and managers from world-leading logistics and transport firms in Australia and New Zealand. Pilot testing was conducted before using the instrument for actual data collection. The purpose of a pilot test is to ensure that the preliminary questionnaire is appropriate, valid and well developed. The author designed it with a specific format to collect information from the respondents to develop estimable variables for the individual incentives in a straightforward fashion. Data collection The questionnaire survey in this research is a major instrument for collecting primary data. At the beginning of the data collection, the pilot testing of instruments with a small number of participants was conducted. The questionnaire was designed for people currently working in the Australian courier industry, with the knowledge and experience to answer the questions. The online method was used to circulate questionnaires, and direct electronic data entry was used for recording answers. We used a Web-based survey so that the online questionnaire could be accessed via a PC, Tablet and smartphone. The participants who might not have been able to access computers, e.g., drivers and warehouse staff, could respond to the survey on smartphones. A survey population was selected randomly from the Australian business register online and Australian yellow page. We deleted the incomplete responses. A total of 160 valid responses have been used for data analysis. The respondents are from five states and three territories in Australia. Over 70 per cent of respondents indicate that they are working at a management level including general/ branch/operations manager, sales/customer services manager or other managers. Of all participants, 60 per cent had been working in their current firms for over five years; 66 per cent had more than five years of experience in the transport and logistics industry. This level of experience would enhance the accuracy of the data, and the quality of responses was MSCRA satisfactory. 2,2 Furthermore, we asked participants to indicate whether or not their companies are implementing supply chain risk management and 90 per cent of respondents indicated that they were aware of the problems/negative impacts of supply chain risks. However, only 50 per cent of respondents recognised risk management/mitigation activities undertaken in their companies. This may imply that the Australian courier companies need to pay more attention to the supply chain risk management strategies. Measures Measurement is an important part of business research that facilitates correspondence between the world of concepts and the world of observations (Hammersley, 1992). In the study, a multiple-indicator measure was employed to measure the concepts (Bryman and Bell, 2011), mainly because there are potential problems relying on a single indicator. Also, SEM requires a minimum number of indicators for each latent variable (Loehlin, 2004). Multiple- indicator measures may minimise measurement errors and improve the reliability and validity of measures of concepts (Grinnell and Unrau, 2011). The questionnaire is structured and presented on a seven-point Likert scale. This is a common format for assessing participants’ opinions of usability. In the Likert-type scale used in this study, we asked the participants to indicate the degree to which they agree or disagree with each of the logistics innovation capability’s statement presented in the firm compared to the major competitors; the value ‘1’ represents ‘strongly disagree’, and the value ‘7’ represents ‘strongly agree’. Due to the nature of risk, we measured the severity of the impact of each risk variable in the firms (Wang et al., 2014). If a risk variable has a very high impact in the firm, the rating should be maximum (i.e. Seven represents ‘very severe problem’); if it has no impact, then the rating should be least (i.e. One represents ‘no problem’) and if it is in between these two ranges, the rating should be between two and six based on their severity. 5. Results This section presents confirmatory factor analysis results for the measurement model and structure model by using structural equation modelling software SmartPLS. Measurement models were used to assess the reliability and validity of the scale items, and the proposed hypotheses were tested in a structural model. Based on the factor analysis, we confirm that the logistics innovation capability has one underlying factor, and the supply chain risk consists of company-side risk, customer-side risk and environment-side risk. Measurement model Reliability is an assessment of the degree of consistency between multiple measurements of a variable (Hair, 2010). This study applied the reliability coefficient with Cronbach’s alpha. Validity is another important dimension to indicate the degree of accuracy of measurements. Convergent validity assesses the degree to which two measures of the same concept are correlated (Hair, 2010). Strong correlations are required to ensure convergent validity, and anything greater than 0.7 is considered satisfactory. In contrast, discriminant validity is the degree to which two conceptually similar concepts are distinct (Hair, 2010) and so indicates that the scale is sufficiently different from other similar concepts. Normally discriminate validity can be determined from the factor correlation matrix; less than 0.7 is considered satisfactory (Hair, 2010). Convergent validity is demonstrated by loadings greater than 0.700, average variance extracted (AVE) greater than 0.500, composite reliability (CR) greater than 0.700 and communalities greater than 0.500. Discriminant validity is demonstrated by the square root of the AVE being greater than any Logistics of the interconstruct correlations (Hair et al., 2012). The factor analysis is used to examine the innovation in t-values of the measurement model loading in the study. Table I summarizes the results. the industry 4.0 era Structural model The structural model is validated in this section. A path coefficient is used for hypotheses testing. The estimation of the structural relationships in the model was conducted by using a bootstrap routine with 2,000 iterations. The requirement for β to be significant follows the standard suggested by Hair et al. (2010) that the relative t-value should be greater than 1.96, which is equivalent to the 95 per cent confidence interval. The significance of β indicates that if a relationship exists in PLS, it can be measured by bootstrapping. A negative β indicates the direction of the relationship as hypothesised, while a positive β indicates the opposite. The standardised path estimates (β) are considered to be large, medium and small for values of greater than 0.37, 0.24 and 0.1, respectively. The absolute value of a path coefficient should be not greater than 1. The Smart PLS is used to estimate the coefficients; results of hypotheses testing in this study are supported as indicated in Table II. 6. Discussion and conclusion This paper presents an empirical study for investigating the relationships between logistics innovation capability and supply chain risks in the Industry 4.0 era. There are very few empirical studies on mitigating supply chain risk through logistics innovation capability. The results indicate that logistics innovation capability is negatively related Items Factor loading t-value Logistics innovation capability α 5 0.87, CR 5 0.91, AVE 5 0.62 My firm applies creative techniques in freight movement and distribution 0.72 15.99 My firm regularly improves the company’s operational systems 0.75 17.56 My firm adopts technologies and innovative solutions for problem-solving 0.81 13.96 My firm applies simplification of operations 0.83 14.84 My firm applies standardisation of operations 0.82 13.61 My firm applies protection for freight safety and risk 0.76 09.92 Company-side risk α 5 0.90, CR 5 0.93, AVE 5 0.72 One inadequate operational strength (e.g., poor fleet/ delivery capacity) 0.86 23.56 Two storage issues (e.g., school/company closed, temperature control) 0.77 09.15 Three delays in pickup/delivery 0.81 17.25 Three poor communication between company and drivers 0.92 36.10 Six poor information sharing within the company 0.88 25.56 Customer-side risk α 5 0.90, CR 5 0.93, AVE 5 0.76 Delays due to customer’s mistakes (e.g., not home, incorrect paperwork) 0.85 18.54 Customers changing the preference 0.92 29.71 Inaccurate forecast of customers’ freight volume 0.88 25.27 Higher customer expectation (e.g., misunderstanding transit time) 0.85 15.18 Environment-side risk α 5 0.88, CR 5 0.91, AVE 5 0.67 Labour/driver shortage 0.79 22.16 Road congestion/closures 0.83 20.61 Weather/natural disasters/industrial action (e.g., bushfire, strike) 0.82 19.15 Unstable fuel prices 0.83 20.70 Table I. Government laws/regulation 0.84 18.40 Item reliability and Note: (1) α 5 Cronbach’s alpha; (2) CR 5 composite reliability; (3) AVE 5 average variance extracted validity to supply chain risks. This implies that the Australian courier companies may mitigate MSCRA the impacts of supply chain risks by developing logistics innovation capabilities. The 2,2 new technologies and creative ideas may be adopted for the supply chain risk management strategies. This supports the Industry 4.0 base technologies to integrate the logistics network (Frank et al.,2019). According to the literature review and empirical data analysis, logistics innovation capability has been identified as an important dynamic capability for mitigating supply chain risks. Dynamic capability is a higher level capability, which can be deployed to extend, modify or create the ordinary capabilities (Winter, 2003). Logistics innovation capability is considered as a higher level capability to reconfigure operations capabilities to achieve excellent logistics operations and mitigate the supply chain risks. In addition, logistics innovation refers to the new technology, new services, new processes and new ideas, which are used for improving logistics operations (Scott, 2009). The logistics innovation capability is an ability to incorporate the logistics innovation to solve the problem and adapt to the changing environment. This would help companies to achieve an excellent logistics operation; it is a way to seek perfection and reduce the impacts of supply chain risk in the Industry 4.0 era. Furthermore, the logistics innovation capability is an appropriate way to assist firms to work out how the unexpected problems and risks can be managed. Using innovation capability to mitigate environmental uncertainty and risk has been promoted in previous studies (Hayes et al.,1988; Kim, 2006). This study revisits the logistics innovation capability and further supports the logistics innovation in the Industry 4.0 research studies (Frank et al.,2019; Lasi et al.,2014). For example, smart packaging carried out with the help of new technologies, processes and services may reduce the damage of goods caused during transportation (Schaefer and Cheung, 2018), automation may improve the logistics efficiency (Wollschlaeger et al., 2017), distributed network may reduce the response time (Frank et al., 2019) and online tracking and tracing provide accurate and timely information and reduce risks of delay or unavailability of information (Christopher and Lee, 2004). It is possible that the capability to implement logistics innovations could be used to manage various supply chain risks. In the survey, the items ‘the company applies creative techniques in freight movement’ and ‘the company applies protection for freight safety and risk’ gained the highest average mean values. The empirical results may support the implementation of Industry 4.0 base technologies (Frank et al., 2019). The item that scored second-highest average mean value is ‘the company applies standardisation of operations’. This may shed light on the digitalisation and automation technology (Wollschlaeger et al.,2017). The overall results demonstrate that being able to bring in innovations in logistics is important for mitigating supply chain risk in the Industry 4.0 era. The three types of supply chain risk analysed in the Australian courier are company-side risk, customer-side risk and environment-side risk. Based on the survey results, the major Standardised Path coefficient (β) t-value Result Logistics innovation capability→ Company-side risk 0.39 5.26* H1: Supported Logistics innovation capability→ Customer-side risk 0.28 3.67* H2: Supported Logistics innovation capability → Environment-side 0.29 3.31* H3: Table II. risk Supported Results of hypotheses using SEM Note:*p < 0.001 company-side risks are delays in pickup/delivery, lost/damaged freight, processing errors, Logistics poor information sharing and inadequate operational strength. The greatest risk, delays, is innovation in also identified in studies by Sanchez-Rodrigues et al. (2009) and Manuj and Mentzer (2008). the industry Customer-side risks have been considered a major factor in the logistics and supply chain. 4.0 era Traditionally, in supply chain risk management, customers are distinguished by their position on the supply or demand side (Murugesan et al., 2013). However, senders and receivers play equal and important roles in a delivery. The major customer-side risks were delays due to customers’ mistakes, high customer expectation and inaccurate forecasting of customers’ freight volume. The greatest environment-side risks including road congestion/ closures, unstable fuel prices, labour/driver shortage, uncertainty due to government laws/ regulation and weather/natural disasters/industrial action. Road congestion and lack of a driver were also found to be risks by McKinnon and Ge (2004), and unstable fuel prices, regulations and natural disasters were found to be risks by Simangunsong et al. (2012). The Australian courier industry has similar problems to those that have been identified in New Zealand, the United Kingdom and Europe. Every research has limitations (Zikmund, 2013). This study focuses on the Australian courier context; hence, generalisation to other contexts should be done with caution. The supply chain risk measurement tool was based on the subjective judgement and single- survey design in this study (Flynn et al., 2018). The conceptual framework can be further empirically tested in different industries and countries. This would help to improve the validity and reliability of the measurement tools. This study offers original contributions to the logistics innovation and supply chain risk management in the Industry 4.0 era, including empirically validated measurement models of logistics innovation capability and supply chain risks in the Australian courier companies. The research models and constructs may be used in further research studies and be generalised for different management purposes. The empirical results indicate the significant relationships between logistics innovation capability and supply chain risks. The findings may demonstrate the applicability of logistics innovation capability for mitigating supply chain risks in the Australian courier firms. Furthermore, the findings provide an insight into innovation management and risk management in logistics and supply chain. This offers practical guidance for developing and deploying logistics innovation capability to support and enable supply chain risk management strategies in the Industry 4.0 era. Future study may be conducted to investigate the extent of different types of innovation capability for risk management in different sectors. References Asian, S. (2019), “On the importance of service performance and customer satisfaction in third-party logistics selection”, Benchmarking: An International Journal, Vol. 26 No. 5, pp. 1550-1564. Black, J.T. and Hunter, S.L. (2003), Lean Manufacturing Systems and Cell Design, Society of Manufacturing Engineers, Dearborn, Mich. Blackhurst, J.V., Scheibe, K.P. and Johnson, D.J. (2008), “Supplier risk assessment and monitoring for the automotive industry”, International Journal of Physical Distribution and Logistics Management, Vol. 38 No. 2, pp. 143-165. Borut, J., Tina, C. and Bojan, R. (2012), “Mastering supply chain risks”, Serbian Journal of Management, Vol. 7 No. 2, pp. 271-285. Braunscheidel, M.J. and Suresh, N.C. (2009), “The organizational antecedents of a firm’s supply chain agility for risk mitigation and response”, Journal of Operations Management, Vol. 27 No. 2, pp. 119-140. Bryman, A. and Bell, E. (2011), Business Research Methods, 3rd ed., Oxford University Press, Oxford. Chopra, S. and Sodhi, M.S. (2004), “Managing risk to avoid supply chain break down”, MIT Sloan MSCRA Management Review, Vol. 46 No. 1, pp. 53-61. 2,2 Christopher, M. (1996), “From brand values to customer value”, Journal of Marketing Practice: Applied Marketing Science, Vol. 2 No. 1, pp. 55-66. Christopher, M. and Lee, H. (2004), “Mitigating supply chain risk through improved confidence”, International Journal of Physical Distribution and Logistics Management, Vol. 34 No. 5, pp. 388-396. Christopher, M. and Peck, H. (2004), “Building the resilient supply chain”, The International Journal of Logistics Management, Vol. 15 No. 2, pp. 1-14. Chrwan-jyh, H. and Phillip, L.C. (1994), “Adopting rescheduling capability in DRP to deal with operational uncertainty in logistics systems”, The International Journal of Logistics Management, Vol. 5 No. 1, pp. 33-42. Cucchiella, F. and Gastaldi, M. (2006), “Risk management in supply chain: a real option approach”, Journal of Manufacturing Technology Management, Vol. 17 No. 6, pp. 700-720. Dani, S. (2010), “Managing risk in virtual enterprise networks: a supply chain innovation perspective”, in Managing Risk in Virtual Enterprise Networks: Implementing Supply Chain Principles, National Technical University of Athens, Greece, pp. 72-91. Daniel, H. and Fredrik, N. (2011), “Logistics-driven packaging innovation: a case study at IKEA”, International Journal of Retail and Distribution Management, Vol. 39 No. 9, pp. 638-657. Davis, T. (1993), “Effective supply chain management”, Sloan Management Review, Vol. 34 No. 4, pp. 35-46. Day, G.S. (1994), “The capabilities of market driven organisations”, Journal of Marketing, Vol. 4 No. 58, pp. 37-52. Dorfman, M.S. (2008), Introduction to Risk Management and Insurance, 9th ed., Pearson/Prentice Hall, Upper Saddle River, NJ. Ellegaard, C. (2008), “Supply risk management in a small company perspective”, Supply Chain Management: An International Journal, Vol. 13 No. 6, pp. 425-434. Fawcett, S.E. and Stanley, L.L. (1997), “Developing a logistics capability to improve the performance of international operations”, Journal of Business Logistics, Vol. 18 No. 2, pp. 101-127. Flint, D.J., Larsson, E., Gammelgaard, B. and Mentzer, J.T. (2005), “Logistics innovation: a customer value-oriented social process”, Journal of Business Logistics, Vol. 26 No. 1, pp. 113-147. Flynn, B., Pagell, M. and Fugate, B. (2018), “Editorial: survey research design in supply chain management: the need for evolution in our expectations”, Journal of Supply Chain Management, Vol. 54 No. 1, pp. 1-15. Flynn, B.B., Koufteros, X. and Lu, G. (2016), “On theory in supply chain uncertainty and its implications for supply chain integration”, Journal of Supply Chain Management, Vol. 52 No. 3, pp. 3-27. Frank, A.G., Dalenogare, L.S. and Ayala, N.F. (2019), “Industry 4.0 technologies: implementation patterns in manufacturing companies”, International Journal of Production Economics, Vol. 210, pp. 15-26. Gilchrist, A. (2016), Industry 4.0: The Industrial Internet of Things, Apress, Berkeley, CA. Grant, R.M. (1991), “The resource-based theory of competitive advantage: implications for strategy formulation”, California Management Review, Vol. 33 No. 3, pp. 114-135. Grinnell, R.M. and Unrau, Y.A. (2011), Social Work Research and Evaluation: Foundations of Evidence- Based Practice, 9th ed., Oxford University Press, New York. Guido, J.L.M., Enrico, C. and Marta, Z. (2008), “Supply risk management vs supplier selection to manage the supply risk in the EPC supply chain”, Management Research News, Vol. 31 No. 11, pp. 846-866. Guo, Z., Fang, F. and Whinston, A.B. (2006), “Supply chain information sharing in a macro prediction Logistics market”, Decision Support Systems, Vol. 42 No. 3, pp. 1944-1958. innovation in Hafeez, K., Zhang, Y. and Malak, N. (2002), “Determining key capabilities of a firm using analytic the industry hierarchy process”, International Journal of Production Economics, Vol. 76 No. 1, pp. 39-51. 4.0 era Hair, J.F. (2010), Multivariate Data Analysis, 7th ed., Prentice Hall, Upper Saddle River, NJ. Hair,J.F., Sarstedt,M., Pieper,T.M.and Ringle,C.M.(2012), “The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications”, Long Range Planning, Vol. 45 No. 5-6, pp. 320-340. Hammersley, M. (1992), “Mixing methods: qualitative and quantitative research”, The International Journal of Allied Health Sciences and Practice, Vol. 8 No. 4, pp. 39-55. Hauser, L.M. (2003), “Risk-adjusted supply chain management”, Supply Chain Management Review, Vol. 7 No. 6, pp. 64-71. Hayes, R.H., Wheelwright, S.C. and Clark, K.B. (1988), Dynamic Manufacturing: Creating the Learning Organization, Free Press of Glencoe Collier-Macmillan, New York, London. Helfat, C.E. (2007), Dynamic Capabilities: Understanding Strategic Change in Organizations, Blackwell Pub., Malden, MA. Ho, W., Zheng, T., Yildiz, H. and Talluri, S. (2015), “Supply chain risk management: a literature review”, International Journal of Production Research, pp. 1-39. Hoffman, W. (2006), “Warning bells for 3PLs”, Traffic World, Vol. 270 No. 37, p. 1. Juttner, € U., Peck, H. and Christopher, M. (2003), “Supply chain risk management: outlining an agenda for future research”, International Journal of Logistics Research and Applications, Vol. 6 No. 4, pp. 197-210. Kim, S.W. (2006), “Effects of supply chain management practices, integration and competition capability on performance”, Supply Chain Management: An International Journal, Vol. 11 No. 3, pp. 241-248. Lai, K.H (2004), “Service capability and performance of logistics service providers”, Transportation Research Part E: Logistics and Transportation Review, Vol. 40, pp. 385-399. Lambert, D.M. and Cooper, M.C. (2000), “Issues in supply chain management”, Industrial Marketing Managment, Vol. 29 No. 2, pp. 65-83. Lasi, H., Fettke, P., Kemper, H.G., Feld, T. and Hoffmann, M. (2014), “Industry 4.0”, Business and Information Systems Engineering, Vol. 6 No. 4, pp. 239-242. Lawson, B. and Samson, D. (2001), “Developing innovation capability in organisations: a dynamic capabilities approach”, International Journal of Innovation Management, Vol. 5 No. 3, pp. 377-400. Lee, H.L. (2002), “Aligning supply chain strategies with product uncertainties”, California Management Review, Vol. 44 No. 3, pp. 105-119. Lin, C.Y. (2006), Influences of Individual, Organisational and Environmental Factors on Technological Innovation in Taiwan’s Logistics Industry, Chang Jung Christian University, Taiwan. Lin, C.Y. (2013), “Determinants of organisational innovation for logistics service providers in Taiwan”, Journal of Statistics and Management Systems, Vol. 9 No. 3, pp. 613-631. Loehlin, J.C. (2004), Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis, 4th ed., L. Erlbaum Associates, Mahwah, NJ. Lu, C.S. and Yang, C.C. (2010), “Logistics service capabilities and firm performance of international distribution center operators”, The Service Industries Journal, Vol. 30 No. 2, pp. 281-298. Lu, D., Ding, Y., Asian, S. and Paul, S.K. (2018), “From supply chain integration to operational performance: the moderating effect of market uncertainty”, Global Journal of Flexible Systems Management, Vol. 19 No. 1, pp. 3-20. Manuj, I. and Mentzer, J.T. (2008), “Global supply chain risk management strategies”, Journal of MSCRA Business Logistics, Vol. 29 No. 1, pp. 133-155. 2,2 McKinnon, A.C. and Ge, Y. (2004), “Use of a synchronised vehicle audit to determine opportunities for improving transport efficiency in a supply chain”, International Journal of Logistics Research and Applications, Vol. 7 No. 3, pp. 219-238. Mentzer, J.T. and Flint, D.J. (1999), “Developing a logistics service quality scale”, Journal of Business Logistics, Vol. 20 No. 1, pp. 9-32. Miller, K.D. (1992), “A framework for integrated risk management in international business”, Journal of International Business Studies, Vol. 23 No. 2, pp. 311-331. Mohamed, Z.A., Ann, H.J. and Yee, W.F. (2014), Strategic Management, Oxford University Press, Selangor Darul Ehsan, Malaysia. Morash, E.A. (2001), “Supply chain strategies, capabilities, and performance”, Transportation Journal, Vol. 41 No. 1, pp. 37-51. Morash, J.T., Dewitt, W. and Keebler, J.S. (1996), “Strategic logistics capabilities for competitive advantage and firm success”, Journal of Business Logistics, Vol. 17 No. 1, pp. 1-22. Murugesan, P., Natarajan, T. and Lakshminarayanan, M. (2013), “Assessment of supply chain risk: scale development and validation”, Benchmarking: An International Journal, Vol. 20 No. 1, pp. 79-105. Olavarrieta, S. and Ellinger, A.E. (1997), “Resource based theory and strategic logistics research”, International Journal of Physical Distribution and Logistics Management, Vol. 27 No. 9/10, pp. 559-587. Pettit, T.J., Croxton, K.L. and Fiksel, J. (2013), “Ensuring supply chain resilience: development and implementation of an assessment tool”, Journal of Business Logistics, Vol. 34 No. 1, pp. 46-76. Prater, E. (2005), “A framework for understanding the interaction of uncertainty and information systems on supply chains”, International Journal of Physical Distribution and Logistics Management, Vol. 35 No. 7, pp. 524-539. Rodrigues, V.S., Stantchev, D., Potter, A., Naim, M. and Whiteing, A. (2008), “Establishing a transport operation focused uncertainty model for the supply chain”, International Journal of Physical Distribution and Logistics Management, Vol. 38 No. 5-6, pp. 388-411. Russo, M.V. and Fouts, P.A. (1997), “A resource-based perspective on corporate environmental performance and profitability”, Academy of Management Journal, Vol. 40 No. 3, p. 534. Sanchez-Rodrigues, V., Potter, A. and Naim, M.M. (2009), “The impact of logistics uncertainty on sustainable transport operations”, International Journal of Physical Distribution and Logistics Management, Vol. 40 No. 1-2, pp. 61-83. Sanchez-Rodrigues, V., Potter, A. and Naim, M.M. (2010), “Evaluating the causes of uncertainty in logistics operations”, International Journal of Logistics Management, Vol. 21 No. 1, pp. 45-64. Sanchez-Rodrigues, V., Stantchev, D., Potter, A., Naim, M. and Whiteing, A. (2008), “Establishing a transport operation focused uncertainty model for the supply chain”, International Journal of Physical Distribution and Logistics Management, Vol. 38 No. 5, pp. 388-411. Schaefer, D. and Cheung, W.M. (2018), “Smart packaging: opportunities and challenges”, Procedia CIRP, Vol. 72 No. C, pp. 1022-1027. Scott, J.G. (2009), “Logistics innovation: a literature-based conceptual framework”, The International Journal of Logistics Management, Vol. 20 No. 3, pp. 360-377. Simangunsong, E., Hendry, L.C. and Stevenson, M. (2012), “Supply-chain uncertainty: a review and theoretical foundation for future research”, International Journal of Production Research, Vol. 50 No. 16, pp. 4493-4523. Sodhi, M.S. and Lee, S. (2007), “An analysis of sources of risk in the consumer electronics industry”, Journal of the Operational Research Society, Vol. 58 No. 11, pp. 1430-1439. Sodhi, M.S. and Tang, C.S. (2012), Managing Supply Chain Risk, Springer, US, Boston, MA. Logistics Szu-Yuan, S., Meng-Hsiang, H. and Wen-Jin, H. (2009), “The impact of alignment between supply chain innovation in strategy and environmental uncertainty on SCM performance”, Supply Chain Management: An the industry International Journal, Vol. 14 No. 3, pp. 201-212. 4.0 era Tang, O. and Nurmaya Musa, S. (2011), “Identifying risk issues and research advancements in supply chain risk management”, International Journal of Production Economics, Vol. 133 No. 1, pp. 25-34. Teece, D.J., Pisano, G. and Shuen, A. (1997), “Dynamic capabilities and strategic management”, Strategic Management Journal, Vol. 18 No. 7, pp. 509-533. Vasco, S.R., Andrew, P. and Mohamed, M.N. (2010), “The impact of logistics uncertainty on sustainable transport operations”, International Journal of Physical Distribution and Logistics Management, Vol. 40 No. 1/2, pp. 61-83. Wagner, S.M. and Sutter, R. (2012), “A qualitative investigation of innovation between third-party logistics providers and customers”, International Journal of Production Economics, Vol. 140 No. 2, pp. 944-958. Wang, M. (2011), Reverse Logistics Optimization, Massey University, Auckland, New Zealand. Wang, M. (2016a), Logistics Capability, Supply Chain Uncertainty and Risk, and Logistics Performance: An Empirical Analysis of the Australian Courier Industry, RMIT University, Melbourne, Australia. Wang, M. (2016b), “The role of innovation capability in the Australian courier industry”, International Journal of Innovation Management, Vol. 20 No. 7, pp. 1-18. Wang, M. (2018), “Impacts of supply chain uncertainty and risk on the logistics performance”, Asia Pacific Journal of Marketing and Logistics, Vol. 30 No. 3, pp. 689-704. Wang, M. and Jie, F. (2019), “Managing supply chain uncertainty and risk in the pharmaceutical industry”, Health Services Management Research, pp. 1-9. Wang, M., Jie, F. and Abareshi, A. (2014), “The measurement model of supply chain uncertainty and risk in the Australian courier industry”, Operations and Supply Chain Management: An International Journal, Vol. 7 No. 3, pp. 89-96. Wang, M., Jie, F. and Abareshi, A. (2015), “A conceptual framework for mitigating supply chain uncertainties and risks in the courier industry”, International Journal of Supply Chain and Operations Resilience, Vol. 1 No. 4, pp. 319-338. Wang, M., Jie, F. and Abareshi, A. (2018), “Improving logistics performance for one belt one road: a conceptual framework for supply chain risk management in Chinese third-party logistics providers”, International Journal of Agile Systems and Management, Vol. 11 No. 4, pp. 364-380. Waters, D. (2011), Supply Chain Risk Management Vulnerability and Resilience in Logistics, 2nd ed., Kogan Page, London. Wei, C., Asian, S., Ertek, G. and Hu, Z.H. (2018), “Location-based pricing and channel selection in a supply chain: a case study from the food retail industry”, Annals of Operations Research, pp. 1-26, doi: 10.1007/s10479-018-3040-7. Wernerfelt, B. (1984), “A resource based view of the firm”, Strategic Management Journal, Vol. 5 No. 2, pp. 171-180. Winter, S.G. (2003), “Understanding dynamic capabilities”, Strategic management journal, Vol. 24 No. 10, pp. 991-995. Wollschlaeger, M., Sauter, T. and Jasperneite, J. (2017), “The future of industrial communication: automation networks in the era of the internet of things and industry 4.0”, IEEE Industrial Electronics Magazine, Vol. 11 No. 1, pp. 17-27. Yang, C.C. (2012), “Assessing the moderating effect of innovation capability on the relationship between logistics service capability and firm performance for ocean freight forwarders”, A Leading Journal of Supply Chain Management, Vol. 15 No. 1, pp. 53-69. Zikmund, W.G. (2013), Business Research Methods, 9th ed., South-Western, Mason, OH. MSCRA Zsidisin, G.A. (2003), “Managerial perceptions of supply risk”, Journal of Supply Chain Management, 2,2 Vol. 39 No. 4, pp. 14-26. Further reading Christopher, M. (1998), Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, 2nd ed., Finacial Times Management, London. Corresponding author Michael Wang can be contacted at: michael.wang@scionresearch.com / michaelwangaus@gmail.com For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Modern Supply Chain Research and Applications Emerald Publishing

Logistics innovation capability and its impacts on the supply chain risks in the Industry 4.0 era

Download PDF
16 pages

Loading next page...
 
/lp/emerald-publishing/logistics-innovation-capability-and-its-impacts-on-the-supply-chain-ilcrr7MsBY

References (93)

Publisher
Emerald Publishing
Copyright
© Michael Wang, Sean Asian, Lincoln C. Wood and Bill Wang
ISSN
2631-3871
DOI
10.1108/mscra-07-2019-0015
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of the paper is to present an empirical study on the logistics innovation capability and its impacts on the supply chain risk in the Australian courier firms. Based on the resource-based review, logistics innovation capability provides valuable insight into mitigating supply chain risks in the Industry 4.0 era. Design/methodology/approach – The research model focuses on the relationships between logistics innovation capability and supply chain risk. Partial least squares approach for structural equation modelling is used to validate the research model by empirically analysing survey data. Findings – The empirical result shows negative relationships between logistics innovation capability and supply chain risks. These relationships may imply that firms can mitigate the negative impacts of supply chain risks by developing logistics innovation capabilities. The findings demonstrate the applicability of logistics innovation capability for mitigating supply chain risks in the Australian courier firms. Originality/value – There are very few empirical studies on the mitigating supply chain risk through logistics innovation capability. The empirical results provide an insight into innovation management and risk management in logistics and supply chain. This insight offers practical guidance for developing and deploying logistics innovation capability to support and enable supply chain risk management strategies in the Industry 4.0 era. Keywords Innovation, Capability, Supply chain risk, Logistics management, Transport, Industry 4.0 Paper type Research paper 1. Introduction As market competition for logistics and transport increases, a growing number of logistics and transport firms try to pursue the operational excellence so that the company can provide the excellent logistics operations to gain a firm’s market share for sustainable development in © Michael Wang, Sean Asian, Lincoln C. Wood and Bill Wang. Published in Modern Supply Chain Research and Applications. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http:// Modern Supply Chain Research and Applications creativecommons.org/licences/by/4.0/legalcode This work was supported, in part, by the New Zealand Forest Research Institute Ltd (Scion). The pp. 83-98 Emerald Publishing Limited authors would like to thank the anonymous reviewers and editors for their valuable and constructive 2631-3871 comments. DOI 10.1108/MSCRA-07-2019-0015 the long term (Christopher, 1996). This is one of the important objectives of the fourth MSCRA industrial revolution (Industry 4.0), which accelerates the development of innovation 2,2 capability in various industries (Frank et al., 2019; Lasi et al., 2014). Supply chain risk is one of the obstacles to achieving operational excellence. Many researchers urge that supply chain risk is a problem in supply chain and logistics (Davis, 1993; Lee, 2002; Miller, 1992; Prater, 2005; Vasco et al., 2010; Wang, 2018). In addition, supply chain risk management plays a vital role in building resilience and achieving sustainability of organisations (Christopher and Peck, 2004). Many previous supply chain risk studies focus on supply chain risk identification and classification (Manuj and Mentzer, 2008; Simangunsong et al., 2012; Sodhi and Lee, 2007; Tang and Nurmaya Musa, 2011; Wang and Jie, 2019). However, lacking resolutions of supply chain risk is a problem, which has also been raised in previous studies (Borut et al., 2012; Guido et al., 2008; Sanchez-Rodrigues et al., 2010; Simangunsong et al., 2012; Wang, 2018). Moreover, the world-leading logistics and transport firm DHL recognises that the supply chain risk may hamper firms towards operational excellence, which is achieved through a focus on systems, cost-effectiveness and speed (Christopher, 1996). Therefore, it is significant to investigate a contingency way to manage supply chain risks. Innovation capability has a long history of research; it was developed from the resource-based view (RBV) theory. Firms can gain and sustain competitive advantages by developing and deploying valuable resources and capabilities (Olavarrieta and Ellinger, 1997; Wernerfelt, 1984). The RBV theory states that in a firm, the resources comprise skills, technologies, capabilities and infrastructure. The resources and capabilities have to be coordinated and deployed in order to generate competitive advantages (Mohamed et al., 2014). Furthermore, capabilities are complex bundles of skills and accumulated knowledge, exercised through organisational processes, which enable firms to coordinate activities and make use of their assets (Day, 1994). Hafeez et al. (2002) regard capability as the ability to make use of resource to perform some task or activity and define a resource as anything tangible or intangible owned or acquired by a firm. RBV of the firm provides valuable insights for understanding how competitive advantage within firms is created and how the advantage is sustained over time. The companies obtain a competitive advantage by accumulating internal resources and capabilities that are rare, valuable and difficult to imitate (Grant, 1991; Olavarrieta and Ellinger, 1997; Russo and Fouts, 1997). Implementing logistics innovation to solve the problems in logistics and transport is not new (Daniel and Fredrik, 2011; Flint et al., 2005; Scott, 2009). Industry 4.0 is rooted in the smart manufacturing concept, which required new technologies and innovation capabilities (Frank et al., 2019). The logistics service providers compete based on their capabilities (Lai, 2004; Wang, 2016b). Logistics capability improves logistics operations, and an effective logistics operation generates competitive advantage and gains a firm’s market share (Mentzer and Flint, 1999; Scott, 2009; Wang et al., 2018). Seeking perfection is one of the key principles of operational excellence in the Industry 4.0. In this study, we argue that logistics innovation capability may help firms to manage supply chain risks through excellent logistics operations. According to a contingency theory, this study attempts to understand the associates between the logistics innovation capabilities and supply chain risks. The findings may imply the supply chain risk management strategies and applicability of logistics innovation for mitigating the supply chain risk in the Australian courier firms. Supply chain risks mainly reflect the negative impacts on logistics operations, such as delays, damage and loss (Sanchez-Rodrigues et al., 2010). Excellent logistics operations need to reduce and mitigate negative impacts. The findings reveal that logistics innovation capability mitigates the impacts of the supply chain risks. This also supports the Industry 4.0 research. The rest of the paper is structured as follows. Section 2 provides a literature review on logistics innovation capability and supply chain risk. Section 3 shows the research model and hypotheses development. Section 4 and 5 present the research methodology and empirical Logistics data analysis. Section 6 discusses the findings, implications and conclusion. innovation in the industry 2. Literature review 4.0 era Logistics innovation capability Innovation is an important logistics capability (Fawcett and Stanley, 1997; Hayes et al., 1988; Lu and Yang, 2010; Morash 1996, 2001). Innovation capability is considered as a dynamic capability (Lawson and Samson, 2001). Dynamic capabilities theory was developed from the RBV theory. Teece et al. (1997) developed the RBV approach one step further by formulating the dynamic capability perspective. Innovation capability is defined as the firm’s ability to continuously transform knowledge and ideas into new products, processes and systems for the benefit of the firm (Lawson and Samson, 2001; Yang, 2012). A dynamic capability is distinct from operational capabilities, which is involved in the current operations of an organisation. Dynamic capabilities, by contrast, refer to “thecapacityof anorganisationto purposefully create, extend, or modify its resource base”(Helfat, 2007). Winter (2003) describes two different types of capabilities: ordinary or ‘zero-level’ capabilities as those that allow a firm to ‘make a living’ in the short term; the other type of capability is dynamic capability. The latter one is higher level capabilities, which can be deployed to extend, modify or create the ordinary or ‘zero-level’ capabilities (Winter, 2003). Logistics innovation capability is considered as a higher level capability to reconfigure operation capabilities to achieve excellent logistics operations and mitigate supply chain risk. Innovation can reconfigure and transform both external and internal resources in order to adapt the company’s strategy (Teece et al.,1997). Logistics innovation refers to the new technology, new services, new processes and new ideas, which are used for improving logistics operations (Scott, 2009). In this paper, the logistics innovation capability is an ability to incorporate the logistics innovation to solve the problem and adapt to a fluctuating environment in a supply chain. The rapidly changing and uncertain business environment raises challenges to enterprises, and innovation is an important tool for keeping their competitive advantage (Lin, 2006). Notably, some researchers found innovation can be used for reducing supply chain risk (Daniel and Fredrik, 2011; Lin, 2013). Dani (2010) emphasises that building innovative culture, innovative processes and innovation capability are key to managing and mitigating supply chain risks. Also, innovation has positive effects on logistics service and is critical for strengthening the Logistics Service Provider (LSP)–customer relationship, generating customer loyalty, achieving competitive advantage and improving the performance of logistics service firms (Asian, 2019; Flint et al., 2005; Wagner and Sutter, 2012). Lin (2013) suggests that logistics service providers pay more attention to innovation capability, to provide better services for their customers. For example, FedEx is one of the most successful and innovative 3PL providers in the world. Its overnight delivery service changed how business-to-business and business-to- customer transactions operated, offering businesses the opportunity of using just-in-time techniques which saved warehousing space and reduced overall costs. The introduction of efficient consumer response techniques led to smaller and more efficient shipment sizes, which in turn further reduced costs (Black and Hunter, 2003; Wang, 2016a; Wei et al., 2018). Supply chain risk There are multiple ways to define the concept of risk. Everyone has his/her own perspectives to understand and justify the risk. Thus, it is significant to consider the risk factors in a specific context. This study concentrates supply chain risks in logistics and transport operations. For managers, the risk is a threat that something might happen to disrupt normal activities or stop things happening as planned (Flynn et al., 2016; Waters, 2011). Risks occur because people never know what will happen in the future. People can use the MSCRA best forecasts and do every possible analysis, but there is always uncertainty about future 2,2 events (Waters, 2011). Supply chain risk is a complex notion that may come in different forms and may comprise supply chain risk sources, risk consequences and risk drivers (Christopher and Lee, 2004; Juttner € et al., 2003; Manuj and Mentzer, 2008; Rodrigues et al., 2008). In the supply chain risk management literature, the risk is due to unreliable and uncertain resources creating supply chain interruption (Tang and Nurmaya Musa, 2011). In this study, supply chain risk is considered as the impacts, consequences and/or errors (e.g., delays, damages and loss) that may harm the logistics operations. According to an extensive review of the literature and previous studies, supply chain risk is categorised into three major clusters in this study, (1) company-side risk, (2) customer-side risk and (3) environment-side risk (Murugesan et al., 2013; Sanchez-Rodrigues et al., 2010; Simangunsong et al., 2012; Wang, 2018; Wang et al., 2014). Courier companies deliver a parcel from a point of pickup to a point of destination. Normally, three parties are directly involved in a typical courier delivery: senders, receivers and delivery companies (Wang, 2016a). The three categories balance and simplify the way to assess internal, external and environmental risks in the courier industry (Christopher and Peck, 2004; Miller, 1992). Company-side risk. In this paper, supply chain risks are concentrated on logistics and transport service providers. The company-side risk can broadly be categorised as the potential disturbances to the flow of goods and information (Ellegaard, 2008). It consists of logistics risk and information risk. Logistics risk is defined as weakness, fault, error, loss and/or unexpected outcome that may influence normal logistics-related activities/processes in transport service providers. Some factors of logistics risk in the literature relating to courier companies include delays in delivery time (Rodrigues et al., 2008; Simangunsong et al., 2012), transport network management (Sanchez-Rodrigues et al., 2008), storage issues (Hauser, 2003), carrier strength (Hauser, 2003) and freight transport operations (Sanchez-Rodrigues et al.,2010). They may disrupt normal operations and cause problems (Hauser, 2003; Rodrigues et al., 2008; Simangunsong et al., 2012). Information risk is defined as information-related unexpected incidents; outcome and/or problems may influence the information in time, accuracy and availability in logistics and transport service providers. Information is the aider in the smooth functioning of the supply chain. The information risk addressed in the literature includes unavailability of information (Guo et al., 2006), information delays (Cucchiella and Gastaldi, 2006), breakdowns of information infrastructure (Blackhurst et al., 2008) and other information and communication issues (Sanchez-Rodrigues et al.,2010). Customer-side risk. Customer-side risk mainly occurs from customer-related processes, e.g., enquiry and quotation, order receipt, order processing and order amendment. In this paper, customer-related risk, a type of supply chain internal risk which mainly originates from the customer side rather than the company side, may cause disputes and/or influence the normal logistics operations in logistics service providers. Due to the nature of courier delivery, both the consignee (receiver) and the consignor (sender) involved in a transaction play an equally important role in the delivery process (Wang, 2011). It is important to consider both receivers and senders together. In supply chain literature, there are various customer- related risks, e.g., unanticipated customer, reputation, forecast error, delays to customer and receivable risks (Chopra and Sodhi, 2004; Manuj and Mentzer, 2008; Sodhi and Lee, 2007; Sodhi and Tang, 2012). Environment-side risk. Supply chain risk can arise due to the interactions between the supply chain network and its external environment/events. The environmental risk is an important type of supply chain risk; it has been discussed widely in previous studies (Manuj and Mentzer, 2008; Simangunsong et al., 2012; Sodhi and Lee, 2007; Zsidisin, 2003). In supply chain literature, most environment risks are unavoidable; e.g., driver shortage, road congestions/closures, regulations and natural disaster. Recently, the terrorist attacks, piracy and unstable politics result in more supply chain risks. As a logistics service provider, courier Logistics companies need to consider the external environmental factor beforehand. It is significant to innovation in study the environment-side risk in the supply chain. Therefore, in this study, the the industry environment-side risk includes labour, road congestion, natural disasters, fuel price and 4.0 era regulations. (Blackhurst et al., 2008; McKinnon and Ge, 2004; Sanchez-Rodrigues et al., 2010; Simangunsong et al., 2012). Supply chain risk mitigation Mitigation is one of the important strategies to manage supply chain risks. Manuj and Mentzer (2008) summarise the existing literature from supply chain and related disciplines to suggest a five-step process for supply chain risk management. The steps consist of risk identification, risk assessment and evaluation, selection of appropriate risk management strategies, strategy implementation and supply chain risk mitigation. Mitigation of supply chain risks is an important part of supply chain risk management. Juttner € et al. (2003) suggest the basic constructs of supply chain risk management, including mitigating risks in a supply chain. From a single company view in a supply chain, Miller (1992) suggests risk mitigation strategies including avoidance, control, cooperation and flexibility. The major categories of techniques of managing the risk include avoidance (i.e., eliminating, withdrawing from or not being involved in the risk), reduction (optimising and mitigating the risk), sharing (transferring, outsourcing or insuring the risk) and retention (accepting and budgeting) (Dorfman, 2008). Furthermore, mitigating supply chain risks does not try to influence or alter the source of risk. Instead, it tries to find ways to adapt and hence minimise the impact of risk (Simangunsong et al., 2012; Wang, 2016a). The supply chain is a complex system consisting of various enterprises processes and relationships (Pettit et al., 2013), and each party connects to the different business partners via separate channels (Lambert and Cooper, 2000). Any change in one party could cause new issues, unexpected consequences and chain reactions in an entire supply chain. Chopra and Sodhi (2004) summarise that capability development is a risk mitigation approach in the supply chain. It minimises the negative supply chain risk impacts. In this paper, the logistics innovation capability is considered as a supply chain risk mitigation tool to manage the risks and improve supply chain resilience. In the Industry 4.0 era, companies rely on new technologies to achieve better business performance (Frank et al., 2019; Gilchrist, 2016). Logistics innovation capability includes the ability to manage technology and process innovation. Furthermore, the study investigates whether logistics innovation capability affects supply chain risk. 3. Research model and hypothesis development With the development of Industry 4.0, new technologies and ideas have been widely implemented in the industries. Logistics and supply chain plays a vital role in delivering and connecting the sectors. This may imply the importance of the innovation capability in the logistics and supply chain to support the Industry 4.0 research (Frank et al.,2019; Lasi et al., 2014). Logistics innovation capability is heavily involved in logistics and transport operations; it provides opportunities for improving the business performance, as in packaging innovations that decrease the risk of damaging goods in transit (Daniel and Fredrik, 2011). Containerisation is a typical example of innovating to improve logistics operations and reduce the risks and uncertainty in transit (Scott, 2009). The logistics and transport industry has seen many examples of logistics innovation, for example, smart packaging has become an important research topic in the smart supply chain (Frank et al., 2019; Schaefer and Cheung, 2018). This creative idea can minimise company-side supply chain risks, e.g., reducing waste and improving information sharing (Lasi et al.,2014; Schaefer and Cheung, 2018; Wollschlaeger et al.,2017). Logistics innovation is capable of managing the logistics risks, which is considered MSCRA as an important enterprise risk (Scott, 2009; Simangunsong et al.,2012). In this study, the 2,2 logistics innovation capability comprises new technologies and services, such as real-time online tracking and tracing service; this has been widely used in logistics companies to provide accurate and timely information. The information technologies reduce the supply chain risks, which are caused by delay or unavailability of information (Christopher and Lee, 2004). Material requirement planning (MRP) systems are used to improve the information manipulation, resulting in reducing supply chain risk in firms (Chrwan-jyh and Phillip, 1994). Thus, the following hypothesis is proposed: H1. Logistics innovation capability is negatively related to company-side risk. Logistics innovation capability is critical for strengthening the LSP–customer relationship, generating customer loyalty (Asian, 2019; Flint et al., 2005; Wagner and Sutter, 2012). Today’s customers have much higher expectations than ever before; many new problems and unexpected issues required creative problem-solving processes. The logistics innovation capability provides an opportunity to resolve the relevant problems and issues, and develop customer relationships. Furthermore, a good relationship between logistics firms and customers may reduce the customer-related uncertainty and risk. For example, FedEx’s overnight delivery service changed how business-to-business and business-to-customer transactions operated. This offered businesses the opportunity of using just-in-time techniques, which saved warehousing space and reduced overall costs. The introduction of efficient consumer response (ECR) techniques led to smaller and more efficient shipment sizes, which in turn further reduced the risks (Black and Hunter, 2003). Thus, the following hypothesis is proposed: H2. Logistics innovation capability is negatively related to customer-related risk. Environmental risk is an important supply chain risk (Ho et al., 2015). The environmental fluctuations caused unexpected changes, risks and uncertainties, which have impacts on the company performance (Lu et al., 2018; Szu-Yuan et al., 2009), and logistics innovation capability can influence the company performance (Wang, 2018; Wang et al., 2018). One example of logistics innovation capability reducing environment-side risk is logistics companies introduced a flexible fuel factor or fuel surcharge against unstable fuel price (Hoffman, 2006). Today, most transport and logistics companies introduced the fuel surcharge into the delivery cost. Moreover, using new technologies and creative ideas to mitigate environmental uncertainty and risk have been promoted in previous studies (Hayes et al., 1988; Kim, 2006). Thus, the following hypothesis is proposed: H3. Logistics innovation capability is negatively related to environmental risk. Overall, the research framework comprises logistics innovation capability and supply chain risk. The measurements of logistics innovation capability in this paper are drawn from previous studies (Braunscheidel and Suresh, 2009; Hayes et al., 1988; Wang et al., 2015). The three categories of supply chain risk, including company-side risk, customer-side risk, and environment-side risk (Sanchez-Rodrigues et al., 2010; Simangunsong et al., 2012), are used to assess the supply chain risks in the Australian courier firms. The conceptual research framework is indicated in Figure 1. 4. Research methods The study aims to investigate the relationships between logistics innovation capability and the different types of supply chain risks, including company-side risk, customer-side risk, and environment-side risk (Wang, 2018). The partial least squares approach for structural Logistics Supply Chain Risk innovation in the industry Company- 4.0 era side H1 H2 Logistics Customer- Innovation side H3 Environment Figure 1. Research framework equation modelling (PLS-SEM) is applied to validate the research model by empirically analysing survey data. The SmartPLS version 2.0.M3 statistical software package is used for data analysis. The questionnaire was reviewed and discussed with supply chain and logistics academicians, researchers and managers from world-leading logistics and transport firms in Australia and New Zealand. Pilot testing was conducted before using the instrument for actual data collection. The purpose of a pilot test is to ensure that the preliminary questionnaire is appropriate, valid and well developed. The author designed it with a specific format to collect information from the respondents to develop estimable variables for the individual incentives in a straightforward fashion. Data collection The questionnaire survey in this research is a major instrument for collecting primary data. At the beginning of the data collection, the pilot testing of instruments with a small number of participants was conducted. The questionnaire was designed for people currently working in the Australian courier industry, with the knowledge and experience to answer the questions. The online method was used to circulate questionnaires, and direct electronic data entry was used for recording answers. We used a Web-based survey so that the online questionnaire could be accessed via a PC, Tablet and smartphone. The participants who might not have been able to access computers, e.g., drivers and warehouse staff, could respond to the survey on smartphones. A survey population was selected randomly from the Australian business register online and Australian yellow page. We deleted the incomplete responses. A total of 160 valid responses have been used for data analysis. The respondents are from five states and three territories in Australia. Over 70 per cent of respondents indicate that they are working at a management level including general/ branch/operations manager, sales/customer services manager or other managers. Of all participants, 60 per cent had been working in their current firms for over five years; 66 per cent had more than five years of experience in the transport and logistics industry. This level of experience would enhance the accuracy of the data, and the quality of responses was MSCRA satisfactory. 2,2 Furthermore, we asked participants to indicate whether or not their companies are implementing supply chain risk management and 90 per cent of respondents indicated that they were aware of the problems/negative impacts of supply chain risks. However, only 50 per cent of respondents recognised risk management/mitigation activities undertaken in their companies. This may imply that the Australian courier companies need to pay more attention to the supply chain risk management strategies. Measures Measurement is an important part of business research that facilitates correspondence between the world of concepts and the world of observations (Hammersley, 1992). In the study, a multiple-indicator measure was employed to measure the concepts (Bryman and Bell, 2011), mainly because there are potential problems relying on a single indicator. Also, SEM requires a minimum number of indicators for each latent variable (Loehlin, 2004). Multiple- indicator measures may minimise measurement errors and improve the reliability and validity of measures of concepts (Grinnell and Unrau, 2011). The questionnaire is structured and presented on a seven-point Likert scale. This is a common format for assessing participants’ opinions of usability. In the Likert-type scale used in this study, we asked the participants to indicate the degree to which they agree or disagree with each of the logistics innovation capability’s statement presented in the firm compared to the major competitors; the value ‘1’ represents ‘strongly disagree’, and the value ‘7’ represents ‘strongly agree’. Due to the nature of risk, we measured the severity of the impact of each risk variable in the firms (Wang et al., 2014). If a risk variable has a very high impact in the firm, the rating should be maximum (i.e. Seven represents ‘very severe problem’); if it has no impact, then the rating should be least (i.e. One represents ‘no problem’) and if it is in between these two ranges, the rating should be between two and six based on their severity. 5. Results This section presents confirmatory factor analysis results for the measurement model and structure model by using structural equation modelling software SmartPLS. Measurement models were used to assess the reliability and validity of the scale items, and the proposed hypotheses were tested in a structural model. Based on the factor analysis, we confirm that the logistics innovation capability has one underlying factor, and the supply chain risk consists of company-side risk, customer-side risk and environment-side risk. Measurement model Reliability is an assessment of the degree of consistency between multiple measurements of a variable (Hair, 2010). This study applied the reliability coefficient with Cronbach’s alpha. Validity is another important dimension to indicate the degree of accuracy of measurements. Convergent validity assesses the degree to which two measures of the same concept are correlated (Hair, 2010). Strong correlations are required to ensure convergent validity, and anything greater than 0.7 is considered satisfactory. In contrast, discriminant validity is the degree to which two conceptually similar concepts are distinct (Hair, 2010) and so indicates that the scale is sufficiently different from other similar concepts. Normally discriminate validity can be determined from the factor correlation matrix; less than 0.7 is considered satisfactory (Hair, 2010). Convergent validity is demonstrated by loadings greater than 0.700, average variance extracted (AVE) greater than 0.500, composite reliability (CR) greater than 0.700 and communalities greater than 0.500. Discriminant validity is demonstrated by the square root of the AVE being greater than any Logistics of the interconstruct correlations (Hair et al., 2012). The factor analysis is used to examine the innovation in t-values of the measurement model loading in the study. Table I summarizes the results. the industry 4.0 era Structural model The structural model is validated in this section. A path coefficient is used for hypotheses testing. The estimation of the structural relationships in the model was conducted by using a bootstrap routine with 2,000 iterations. The requirement for β to be significant follows the standard suggested by Hair et al. (2010) that the relative t-value should be greater than 1.96, which is equivalent to the 95 per cent confidence interval. The significance of β indicates that if a relationship exists in PLS, it can be measured by bootstrapping. A negative β indicates the direction of the relationship as hypothesised, while a positive β indicates the opposite. The standardised path estimates (β) are considered to be large, medium and small for values of greater than 0.37, 0.24 and 0.1, respectively. The absolute value of a path coefficient should be not greater than 1. The Smart PLS is used to estimate the coefficients; results of hypotheses testing in this study are supported as indicated in Table II. 6. Discussion and conclusion This paper presents an empirical study for investigating the relationships between logistics innovation capability and supply chain risks in the Industry 4.0 era. There are very few empirical studies on mitigating supply chain risk through logistics innovation capability. The results indicate that logistics innovation capability is negatively related Items Factor loading t-value Logistics innovation capability α 5 0.87, CR 5 0.91, AVE 5 0.62 My firm applies creative techniques in freight movement and distribution 0.72 15.99 My firm regularly improves the company’s operational systems 0.75 17.56 My firm adopts technologies and innovative solutions for problem-solving 0.81 13.96 My firm applies simplification of operations 0.83 14.84 My firm applies standardisation of operations 0.82 13.61 My firm applies protection for freight safety and risk 0.76 09.92 Company-side risk α 5 0.90, CR 5 0.93, AVE 5 0.72 One inadequate operational strength (e.g., poor fleet/ delivery capacity) 0.86 23.56 Two storage issues (e.g., school/company closed, temperature control) 0.77 09.15 Three delays in pickup/delivery 0.81 17.25 Three poor communication between company and drivers 0.92 36.10 Six poor information sharing within the company 0.88 25.56 Customer-side risk α 5 0.90, CR 5 0.93, AVE 5 0.76 Delays due to customer’s mistakes (e.g., not home, incorrect paperwork) 0.85 18.54 Customers changing the preference 0.92 29.71 Inaccurate forecast of customers’ freight volume 0.88 25.27 Higher customer expectation (e.g., misunderstanding transit time) 0.85 15.18 Environment-side risk α 5 0.88, CR 5 0.91, AVE 5 0.67 Labour/driver shortage 0.79 22.16 Road congestion/closures 0.83 20.61 Weather/natural disasters/industrial action (e.g., bushfire, strike) 0.82 19.15 Unstable fuel prices 0.83 20.70 Table I. Government laws/regulation 0.84 18.40 Item reliability and Note: (1) α 5 Cronbach’s alpha; (2) CR 5 composite reliability; (3) AVE 5 average variance extracted validity to supply chain risks. This implies that the Australian courier companies may mitigate MSCRA the impacts of supply chain risks by developing logistics innovation capabilities. The 2,2 new technologies and creative ideas may be adopted for the supply chain risk management strategies. This supports the Industry 4.0 base technologies to integrate the logistics network (Frank et al.,2019). According to the literature review and empirical data analysis, logistics innovation capability has been identified as an important dynamic capability for mitigating supply chain risks. Dynamic capability is a higher level capability, which can be deployed to extend, modify or create the ordinary capabilities (Winter, 2003). Logistics innovation capability is considered as a higher level capability to reconfigure operations capabilities to achieve excellent logistics operations and mitigate the supply chain risks. In addition, logistics innovation refers to the new technology, new services, new processes and new ideas, which are used for improving logistics operations (Scott, 2009). The logistics innovation capability is an ability to incorporate the logistics innovation to solve the problem and adapt to the changing environment. This would help companies to achieve an excellent logistics operation; it is a way to seek perfection and reduce the impacts of supply chain risk in the Industry 4.0 era. Furthermore, the logistics innovation capability is an appropriate way to assist firms to work out how the unexpected problems and risks can be managed. Using innovation capability to mitigate environmental uncertainty and risk has been promoted in previous studies (Hayes et al.,1988; Kim, 2006). This study revisits the logistics innovation capability and further supports the logistics innovation in the Industry 4.0 research studies (Frank et al.,2019; Lasi et al.,2014). For example, smart packaging carried out with the help of new technologies, processes and services may reduce the damage of goods caused during transportation (Schaefer and Cheung, 2018), automation may improve the logistics efficiency (Wollschlaeger et al., 2017), distributed network may reduce the response time (Frank et al., 2019) and online tracking and tracing provide accurate and timely information and reduce risks of delay or unavailability of information (Christopher and Lee, 2004). It is possible that the capability to implement logistics innovations could be used to manage various supply chain risks. In the survey, the items ‘the company applies creative techniques in freight movement’ and ‘the company applies protection for freight safety and risk’ gained the highest average mean values. The empirical results may support the implementation of Industry 4.0 base technologies (Frank et al., 2019). The item that scored second-highest average mean value is ‘the company applies standardisation of operations’. This may shed light on the digitalisation and automation technology (Wollschlaeger et al.,2017). The overall results demonstrate that being able to bring in innovations in logistics is important for mitigating supply chain risk in the Industry 4.0 era. The three types of supply chain risk analysed in the Australian courier are company-side risk, customer-side risk and environment-side risk. Based on the survey results, the major Standardised Path coefficient (β) t-value Result Logistics innovation capability→ Company-side risk 0.39 5.26* H1: Supported Logistics innovation capability→ Customer-side risk 0.28 3.67* H2: Supported Logistics innovation capability → Environment-side 0.29 3.31* H3: Table II. risk Supported Results of hypotheses using SEM Note:*p < 0.001 company-side risks are delays in pickup/delivery, lost/damaged freight, processing errors, Logistics poor information sharing and inadequate operational strength. The greatest risk, delays, is innovation in also identified in studies by Sanchez-Rodrigues et al. (2009) and Manuj and Mentzer (2008). the industry Customer-side risks have been considered a major factor in the logistics and supply chain. 4.0 era Traditionally, in supply chain risk management, customers are distinguished by their position on the supply or demand side (Murugesan et al., 2013). However, senders and receivers play equal and important roles in a delivery. The major customer-side risks were delays due to customers’ mistakes, high customer expectation and inaccurate forecasting of customers’ freight volume. The greatest environment-side risks including road congestion/ closures, unstable fuel prices, labour/driver shortage, uncertainty due to government laws/ regulation and weather/natural disasters/industrial action. Road congestion and lack of a driver were also found to be risks by McKinnon and Ge (2004), and unstable fuel prices, regulations and natural disasters were found to be risks by Simangunsong et al. (2012). The Australian courier industry has similar problems to those that have been identified in New Zealand, the United Kingdom and Europe. Every research has limitations (Zikmund, 2013). This study focuses on the Australian courier context; hence, generalisation to other contexts should be done with caution. The supply chain risk measurement tool was based on the subjective judgement and single- survey design in this study (Flynn et al., 2018). The conceptual framework can be further empirically tested in different industries and countries. This would help to improve the validity and reliability of the measurement tools. This study offers original contributions to the logistics innovation and supply chain risk management in the Industry 4.0 era, including empirically validated measurement models of logistics innovation capability and supply chain risks in the Australian courier companies. The research models and constructs may be used in further research studies and be generalised for different management purposes. The empirical results indicate the significant relationships between logistics innovation capability and supply chain risks. The findings may demonstrate the applicability of logistics innovation capability for mitigating supply chain risks in the Australian courier firms. Furthermore, the findings provide an insight into innovation management and risk management in logistics and supply chain. This offers practical guidance for developing and deploying logistics innovation capability to support and enable supply chain risk management strategies in the Industry 4.0 era. Future study may be conducted to investigate the extent of different types of innovation capability for risk management in different sectors. References Asian, S. (2019), “On the importance of service performance and customer satisfaction in third-party logistics selection”, Benchmarking: An International Journal, Vol. 26 No. 5, pp. 1550-1564. Black, J.T. and Hunter, S.L. (2003), Lean Manufacturing Systems and Cell Design, Society of Manufacturing Engineers, Dearborn, Mich. Blackhurst, J.V., Scheibe, K.P. and Johnson, D.J. (2008), “Supplier risk assessment and monitoring for the automotive industry”, International Journal of Physical Distribution and Logistics Management, Vol. 38 No. 2, pp. 143-165. Borut, J., Tina, C. and Bojan, R. (2012), “Mastering supply chain risks”, Serbian Journal of Management, Vol. 7 No. 2, pp. 271-285. Braunscheidel, M.J. and Suresh, N.C. (2009), “The organizational antecedents of a firm’s supply chain agility for risk mitigation and response”, Journal of Operations Management, Vol. 27 No. 2, pp. 119-140. Bryman, A. and Bell, E. (2011), Business Research Methods, 3rd ed., Oxford University Press, Oxford. Chopra, S. and Sodhi, M.S. (2004), “Managing risk to avoid supply chain break down”, MIT Sloan MSCRA Management Review, Vol. 46 No. 1, pp. 53-61. 2,2 Christopher, M. (1996), “From brand values to customer value”, Journal of Marketing Practice: Applied Marketing Science, Vol. 2 No. 1, pp. 55-66. Christopher, M. and Lee, H. (2004), “Mitigating supply chain risk through improved confidence”, International Journal of Physical Distribution and Logistics Management, Vol. 34 No. 5, pp. 388-396. Christopher, M. and Peck, H. (2004), “Building the resilient supply chain”, The International Journal of Logistics Management, Vol. 15 No. 2, pp. 1-14. Chrwan-jyh, H. and Phillip, L.C. (1994), “Adopting rescheduling capability in DRP to deal with operational uncertainty in logistics systems”, The International Journal of Logistics Management, Vol. 5 No. 1, pp. 33-42. Cucchiella, F. and Gastaldi, M. (2006), “Risk management in supply chain: a real option approach”, Journal of Manufacturing Technology Management, Vol. 17 No. 6, pp. 700-720. Dani, S. (2010), “Managing risk in virtual enterprise networks: a supply chain innovation perspective”, in Managing Risk in Virtual Enterprise Networks: Implementing Supply Chain Principles, National Technical University of Athens, Greece, pp. 72-91. Daniel, H. and Fredrik, N. (2011), “Logistics-driven packaging innovation: a case study at IKEA”, International Journal of Retail and Distribution Management, Vol. 39 No. 9, pp. 638-657. Davis, T. (1993), “Effective supply chain management”, Sloan Management Review, Vol. 34 No. 4, pp. 35-46. Day, G.S. (1994), “The capabilities of market driven organisations”, Journal of Marketing, Vol. 4 No. 58, pp. 37-52. Dorfman, M.S. (2008), Introduction to Risk Management and Insurance, 9th ed., Pearson/Prentice Hall, Upper Saddle River, NJ. Ellegaard, C. (2008), “Supply risk management in a small company perspective”, Supply Chain Management: An International Journal, Vol. 13 No. 6, pp. 425-434. Fawcett, S.E. and Stanley, L.L. (1997), “Developing a logistics capability to improve the performance of international operations”, Journal of Business Logistics, Vol. 18 No. 2, pp. 101-127. Flint, D.J., Larsson, E., Gammelgaard, B. and Mentzer, J.T. (2005), “Logistics innovation: a customer value-oriented social process”, Journal of Business Logistics, Vol. 26 No. 1, pp. 113-147. Flynn, B., Pagell, M. and Fugate, B. (2018), “Editorial: survey research design in supply chain management: the need for evolution in our expectations”, Journal of Supply Chain Management, Vol. 54 No. 1, pp. 1-15. Flynn, B.B., Koufteros, X. and Lu, G. (2016), “On theory in supply chain uncertainty and its implications for supply chain integration”, Journal of Supply Chain Management, Vol. 52 No. 3, pp. 3-27. Frank, A.G., Dalenogare, L.S. and Ayala, N.F. (2019), “Industry 4.0 technologies: implementation patterns in manufacturing companies”, International Journal of Production Economics, Vol. 210, pp. 15-26. Gilchrist, A. (2016), Industry 4.0: The Industrial Internet of Things, Apress, Berkeley, CA. Grant, R.M. (1991), “The resource-based theory of competitive advantage: implications for strategy formulation”, California Management Review, Vol. 33 No. 3, pp. 114-135. Grinnell, R.M. and Unrau, Y.A. (2011), Social Work Research and Evaluation: Foundations of Evidence- Based Practice, 9th ed., Oxford University Press, New York. Guido, J.L.M., Enrico, C. and Marta, Z. (2008), “Supply risk management vs supplier selection to manage the supply risk in the EPC supply chain”, Management Research News, Vol. 31 No. 11, pp. 846-866. Guo, Z., Fang, F. and Whinston, A.B. (2006), “Supply chain information sharing in a macro prediction Logistics market”, Decision Support Systems, Vol. 42 No. 3, pp. 1944-1958. innovation in Hafeez, K., Zhang, Y. and Malak, N. (2002), “Determining key capabilities of a firm using analytic the industry hierarchy process”, International Journal of Production Economics, Vol. 76 No. 1, pp. 39-51. 4.0 era Hair, J.F. (2010), Multivariate Data Analysis, 7th ed., Prentice Hall, Upper Saddle River, NJ. Hair,J.F., Sarstedt,M., Pieper,T.M.and Ringle,C.M.(2012), “The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications”, Long Range Planning, Vol. 45 No. 5-6, pp. 320-340. Hammersley, M. (1992), “Mixing methods: qualitative and quantitative research”, The International Journal of Allied Health Sciences and Practice, Vol. 8 No. 4, pp. 39-55. Hauser, L.M. (2003), “Risk-adjusted supply chain management”, Supply Chain Management Review, Vol. 7 No. 6, pp. 64-71. Hayes, R.H., Wheelwright, S.C. and Clark, K.B. (1988), Dynamic Manufacturing: Creating the Learning Organization, Free Press of Glencoe Collier-Macmillan, New York, London. Helfat, C.E. (2007), Dynamic Capabilities: Understanding Strategic Change in Organizations, Blackwell Pub., Malden, MA. Ho, W., Zheng, T., Yildiz, H. and Talluri, S. (2015), “Supply chain risk management: a literature review”, International Journal of Production Research, pp. 1-39. Hoffman, W. (2006), “Warning bells for 3PLs”, Traffic World, Vol. 270 No. 37, p. 1. Juttner, € U., Peck, H. and Christopher, M. (2003), “Supply chain risk management: outlining an agenda for future research”, International Journal of Logistics Research and Applications, Vol. 6 No. 4, pp. 197-210. Kim, S.W. (2006), “Effects of supply chain management practices, integration and competition capability on performance”, Supply Chain Management: An International Journal, Vol. 11 No. 3, pp. 241-248. Lai, K.H (2004), “Service capability and performance of logistics service providers”, Transportation Research Part E: Logistics and Transportation Review, Vol. 40, pp. 385-399. Lambert, D.M. and Cooper, M.C. (2000), “Issues in supply chain management”, Industrial Marketing Managment, Vol. 29 No. 2, pp. 65-83. Lasi, H., Fettke, P., Kemper, H.G., Feld, T. and Hoffmann, M. (2014), “Industry 4.0”, Business and Information Systems Engineering, Vol. 6 No. 4, pp. 239-242. Lawson, B. and Samson, D. (2001), “Developing innovation capability in organisations: a dynamic capabilities approach”, International Journal of Innovation Management, Vol. 5 No. 3, pp. 377-400. Lee, H.L. (2002), “Aligning supply chain strategies with product uncertainties”, California Management Review, Vol. 44 No. 3, pp. 105-119. Lin, C.Y. (2006), Influences of Individual, Organisational and Environmental Factors on Technological Innovation in Taiwan’s Logistics Industry, Chang Jung Christian University, Taiwan. Lin, C.Y. (2013), “Determinants of organisational innovation for logistics service providers in Taiwan”, Journal of Statistics and Management Systems, Vol. 9 No. 3, pp. 613-631. Loehlin, J.C. (2004), Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis, 4th ed., L. Erlbaum Associates, Mahwah, NJ. Lu, C.S. and Yang, C.C. (2010), “Logistics service capabilities and firm performance of international distribution center operators”, The Service Industries Journal, Vol. 30 No. 2, pp. 281-298. Lu, D., Ding, Y., Asian, S. and Paul, S.K. (2018), “From supply chain integration to operational performance: the moderating effect of market uncertainty”, Global Journal of Flexible Systems Management, Vol. 19 No. 1, pp. 3-20. Manuj, I. and Mentzer, J.T. (2008), “Global supply chain risk management strategies”, Journal of MSCRA Business Logistics, Vol. 29 No. 1, pp. 133-155. 2,2 McKinnon, A.C. and Ge, Y. (2004), “Use of a synchronised vehicle audit to determine opportunities for improving transport efficiency in a supply chain”, International Journal of Logistics Research and Applications, Vol. 7 No. 3, pp. 219-238. Mentzer, J.T. and Flint, D.J. (1999), “Developing a logistics service quality scale”, Journal of Business Logistics, Vol. 20 No. 1, pp. 9-32. Miller, K.D. (1992), “A framework for integrated risk management in international business”, Journal of International Business Studies, Vol. 23 No. 2, pp. 311-331. Mohamed, Z.A., Ann, H.J. and Yee, W.F. (2014), Strategic Management, Oxford University Press, Selangor Darul Ehsan, Malaysia. Morash, E.A. (2001), “Supply chain strategies, capabilities, and performance”, Transportation Journal, Vol. 41 No. 1, pp. 37-51. Morash, J.T., Dewitt, W. and Keebler, J.S. (1996), “Strategic logistics capabilities for competitive advantage and firm success”, Journal of Business Logistics, Vol. 17 No. 1, pp. 1-22. Murugesan, P., Natarajan, T. and Lakshminarayanan, M. (2013), “Assessment of supply chain risk: scale development and validation”, Benchmarking: An International Journal, Vol. 20 No. 1, pp. 79-105. Olavarrieta, S. and Ellinger, A.E. (1997), “Resource based theory and strategic logistics research”, International Journal of Physical Distribution and Logistics Management, Vol. 27 No. 9/10, pp. 559-587. Pettit, T.J., Croxton, K.L. and Fiksel, J. (2013), “Ensuring supply chain resilience: development and implementation of an assessment tool”, Journal of Business Logistics, Vol. 34 No. 1, pp. 46-76. Prater, E. (2005), “A framework for understanding the interaction of uncertainty and information systems on supply chains”, International Journal of Physical Distribution and Logistics Management, Vol. 35 No. 7, pp. 524-539. Rodrigues, V.S., Stantchev, D., Potter, A., Naim, M. and Whiteing, A. (2008), “Establishing a transport operation focused uncertainty model for the supply chain”, International Journal of Physical Distribution and Logistics Management, Vol. 38 No. 5-6, pp. 388-411. Russo, M.V. and Fouts, P.A. (1997), “A resource-based perspective on corporate environmental performance and profitability”, Academy of Management Journal, Vol. 40 No. 3, p. 534. Sanchez-Rodrigues, V., Potter, A. and Naim, M.M. (2009), “The impact of logistics uncertainty on sustainable transport operations”, International Journal of Physical Distribution and Logistics Management, Vol. 40 No. 1-2, pp. 61-83. Sanchez-Rodrigues, V., Potter, A. and Naim, M.M. (2010), “Evaluating the causes of uncertainty in logistics operations”, International Journal of Logistics Management, Vol. 21 No. 1, pp. 45-64. Sanchez-Rodrigues, V., Stantchev, D., Potter, A., Naim, M. and Whiteing, A. (2008), “Establishing a transport operation focused uncertainty model for the supply chain”, International Journal of Physical Distribution and Logistics Management, Vol. 38 No. 5, pp. 388-411. Schaefer, D. and Cheung, W.M. (2018), “Smart packaging: opportunities and challenges”, Procedia CIRP, Vol. 72 No. C, pp. 1022-1027. Scott, J.G. (2009), “Logistics innovation: a literature-based conceptual framework”, The International Journal of Logistics Management, Vol. 20 No. 3, pp. 360-377. Simangunsong, E., Hendry, L.C. and Stevenson, M. (2012), “Supply-chain uncertainty: a review and theoretical foundation for future research”, International Journal of Production Research, Vol. 50 No. 16, pp. 4493-4523. Sodhi, M.S. and Lee, S. (2007), “An analysis of sources of risk in the consumer electronics industry”, Journal of the Operational Research Society, Vol. 58 No. 11, pp. 1430-1439. Sodhi, M.S. and Tang, C.S. (2012), Managing Supply Chain Risk, Springer, US, Boston, MA. Logistics Szu-Yuan, S., Meng-Hsiang, H. and Wen-Jin, H. (2009), “The impact of alignment between supply chain innovation in strategy and environmental uncertainty on SCM performance”, Supply Chain Management: An the industry International Journal, Vol. 14 No. 3, pp. 201-212. 4.0 era Tang, O. and Nurmaya Musa, S. (2011), “Identifying risk issues and research advancements in supply chain risk management”, International Journal of Production Economics, Vol. 133 No. 1, pp. 25-34. Teece, D.J., Pisano, G. and Shuen, A. (1997), “Dynamic capabilities and strategic management”, Strategic Management Journal, Vol. 18 No. 7, pp. 509-533. Vasco, S.R., Andrew, P. and Mohamed, M.N. (2010), “The impact of logistics uncertainty on sustainable transport operations”, International Journal of Physical Distribution and Logistics Management, Vol. 40 No. 1/2, pp. 61-83. Wagner, S.M. and Sutter, R. (2012), “A qualitative investigation of innovation between third-party logistics providers and customers”, International Journal of Production Economics, Vol. 140 No. 2, pp. 944-958. Wang, M. (2011), Reverse Logistics Optimization, Massey University, Auckland, New Zealand. Wang, M. (2016a), Logistics Capability, Supply Chain Uncertainty and Risk, and Logistics Performance: An Empirical Analysis of the Australian Courier Industry, RMIT University, Melbourne, Australia. Wang, M. (2016b), “The role of innovation capability in the Australian courier industry”, International Journal of Innovation Management, Vol. 20 No. 7, pp. 1-18. Wang, M. (2018), “Impacts of supply chain uncertainty and risk on the logistics performance”, Asia Pacific Journal of Marketing and Logistics, Vol. 30 No. 3, pp. 689-704. Wang, M. and Jie, F. (2019), “Managing supply chain uncertainty and risk in the pharmaceutical industry”, Health Services Management Research, pp. 1-9. Wang, M., Jie, F. and Abareshi, A. (2014), “The measurement model of supply chain uncertainty and risk in the Australian courier industry”, Operations and Supply Chain Management: An International Journal, Vol. 7 No. 3, pp. 89-96. Wang, M., Jie, F. and Abareshi, A. (2015), “A conceptual framework for mitigating supply chain uncertainties and risks in the courier industry”, International Journal of Supply Chain and Operations Resilience, Vol. 1 No. 4, pp. 319-338. Wang, M., Jie, F. and Abareshi, A. (2018), “Improving logistics performance for one belt one road: a conceptual framework for supply chain risk management in Chinese third-party logistics providers”, International Journal of Agile Systems and Management, Vol. 11 No. 4, pp. 364-380. Waters, D. (2011), Supply Chain Risk Management Vulnerability and Resilience in Logistics, 2nd ed., Kogan Page, London. Wei, C., Asian, S., Ertek, G. and Hu, Z.H. (2018), “Location-based pricing and channel selection in a supply chain: a case study from the food retail industry”, Annals of Operations Research, pp. 1-26, doi: 10.1007/s10479-018-3040-7. Wernerfelt, B. (1984), “A resource based view of the firm”, Strategic Management Journal, Vol. 5 No. 2, pp. 171-180. Winter, S.G. (2003), “Understanding dynamic capabilities”, Strategic management journal, Vol. 24 No. 10, pp. 991-995. Wollschlaeger, M., Sauter, T. and Jasperneite, J. (2017), “The future of industrial communication: automation networks in the era of the internet of things and industry 4.0”, IEEE Industrial Electronics Magazine, Vol. 11 No. 1, pp. 17-27. Yang, C.C. (2012), “Assessing the moderating effect of innovation capability on the relationship between logistics service capability and firm performance for ocean freight forwarders”, A Leading Journal of Supply Chain Management, Vol. 15 No. 1, pp. 53-69. Zikmund, W.G. (2013), Business Research Methods, 9th ed., South-Western, Mason, OH. MSCRA Zsidisin, G.A. (2003), “Managerial perceptions of supply risk”, Journal of Supply Chain Management, 2,2 Vol. 39 No. 4, pp. 14-26. Further reading Christopher, M. (1998), Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, 2nd ed., Finacial Times Management, London. Corresponding author Michael Wang can be contacted at: michael.wang@scionresearch.com / michaelwangaus@gmail.com For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com

Journal

Modern Supply Chain Research and Applications – Emerald Publishing

Published: May 12, 2020

Keywords: Innovation; Capability; Supply chain risk; Logistics management; Transport; Industry 4.0

There are no references for this article.