Skip to main content
Log in

Impact of information technology on the performance of logistics industry: the case of Hong Kong and Pearl Delta region

  • Special Issue Paper
  • Published:
Journal of the Operational Research Society

Abstract

Over the last decade, a number of research studies have advocated the use of information technology (IT) in different aspects of logistics and distribution operations. This study examines the current state of the use of IT and its impact on logistics service performance through a survey of 210 logistics companies in Hong Kong and the Pearl River Delta region. A hypothetical model is also proposed in which the theories of the market-based view and the resource-based view are applied to link up the implications of IT capabilities with logistic performance. The model was tested using structural equation modelling. The findings suggested that (i) IT implementation directly enhances the service quality of the logistics companies; (ii) the impact of IT implementation improves service quality thereby creating competitiveness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2

References

  • Acur N and Bititci U (2004). A balanced approach to strategy process. International Journal of Operations and Production Management 24 (3/4): 388–408.

    Article  Google Scholar 

  • Aktas E, Agaran B, Ulengin F and Onsel S (2011). The use of outsourcing logistics activities: The case of Turkey. Transportation Research Part C 19 (5): 833–852.

    Article  Google Scholar 

  • Alt R, Fleisch E and Österle H (2000). Electronic commerce and supply chain management at ETA fabriques d’Ebauches. Journal of Electronic Commerce Research 1 (2): 67–78.

    Google Scholar 

  • Avolio BJ, Yammarino FJ and Bassm BM (1991). Identifying common methods variance with data collected from a single source: An unresolved sticky issue. Journal of Management 17 (3): 571–587.

    Article  Google Scholar 

  • Bagozzi RP, Yi Y and Phillips LW (1991). Assessing construct validity in organizational research. Administrative Science Quarterly 36 (3): 421–459.

    Article  Google Scholar 

  • Baraldi E and Nadin G (2006). The challenges in digitalising business relationships. The construction of an IT infrastructure for a textile-related business network. Technovation 26 (10): 1111–1126.

    Article  Google Scholar 

  • Bardaki C, Kourouthanassis P and Pramatari K (2011). Modeling the information completeness of object tracking systems. Journal of Strategic Information Systems 20 (3): 268–282.

    Article  Google Scholar 

  • Barlas Y and Gunduz B (2011). Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains. Journal of the Operational Research Society 62 (3): 458–473.

    Article  Google Scholar 

  • Baron RM and Kenny DA (1986). The moderator-mediator variable distinction in social psychological research—Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51 (6): 1173–1182.

    Article  Google Scholar 

  • Bharadwaj A (2000). A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quarterly 24 (1): 169–196.

    Article  Google Scholar 

  • Bobillo AM, Ló pez-Iturriaga F and Tejerina-Gaite F (2010). Firm performance and international diversification: The internal and external competitive advantages. International Business Review 19 (6): 607–618.

    Article  Google Scholar 

  • Bourne M, Mills J, Wilcox M, Neely A and Platts K (2000). Designing, implementing and updating performance measurement systems. International Journal of Operations and Production Management 20 (7): 754–771.

    Article  Google Scholar 

  • Bouwman AF, Van Drecht G and Van der Hoek KW (2005). Surface nitrogen balances and reactive N loss to the environment from global intensive agricultural production systems for the period 1970–2030. Science in China. Ser. C. Life Sciences 48 (2): 767–779.

    Article  Google Scholar 

  • Byrd TA and Davidson NW (2003). Examining possible antecedents of IT impact on the supply chain and its effect on firm performance. Information and Management 41 (2): 243–255.

    Article  Google Scholar 

  • Chang SC and Lee MS (2007). The effects of organizational culture and knowledge management mechanisms on organizational innovation: An empirical study in Taiwan. The Business Review, Cambridge 7 (1): 295–302.

    Google Scholar 

  • Chang SJ, Van Witteloostuijin A and Eden L (2010). From the editors: Common method variance in international business research. Journal of International Business Studies 41 (2): 178–184.

    Article  Google Scholar 

  • Closs DJ and Savitskie K (2003). Internal and external logistics information technology integration. The International Journal of Logistics Management 14 (10): 63–76.

    Article  Google Scholar 

  • Craighead CW, Ketchen DJ, Dunn Jr KS and Hult GTM (2011). Addressing common method variance: Guidelines for survey research on information technology, operations, and supply chain management. IEEE Transactions on Engineering Management 58 (3): 578–588.

    Article  Google Scholar 

  • Dyson LE and Koruth S (2004). Improving business performance through supply chain intelligence: An Australian perspective. In: Soliman KS (ed). Information Technology and Organizations in the 21st Century: Challenges & Solutions Proceedings of the 2004 International Business Information Management Conference. IEEE, published online pp 342–348.

  • Elbashir MZ, Collier AP and Davern MJ (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems 9 (3): 135–153.

    Article  Google Scholar 

  • Gangadharan GR and Swamy NS (2004). Business intelligence systems: Design and implementation strategies. Proceedings of 26th International Conference on Information Technology Interfaces. Cavtat, Croatia.

  • Golicic SL, Davis DF, McCarthy TM and Mentzer JT (2002). The impact of e-commerce on supply chain relationships. International Journal of Physical Distribution and Logistics Management 32 (10): 851–871.

    Article  Google Scholar 

  • Gu J, Goetschalckx M and McGinnis LF (2010). Solving the forward-reserve allocation problem in warehouse order picking systems. Journal of the Operational Research Society 61 (6): 1013–1021.

    Article  Google Scholar 

  • Hair JF, Anderson RE, Tatham RL and Black WC (1995). Multivariate Data Analysis with Readings. Prentice-Hall: Englewood Cliffs, NJ.

    Google Scholar 

  • Hannula M and Pirttimaki V (2003). Business intelligence empirical study on the top 50 Finnish companies. American Academy of Business 2 (2): 593–599.

    Google Scholar 

  • Helfat CE and Peteraf MA (2003). The dynamic resource-based view: Capability lifecycles. Strategic Management Journal 24 (10): 997–1010.

    Article  Google Scholar 

  • Herschel RT and Jones NE (2005). Knowledge management and business intelligence: The importance of integration. Journal of Knowledge Management 9 (4): 45–55.

    Article  Google Scholar 

  • Hofenk D, Schipper R, Semeijn J and Gelderman C (2011). The influence of contractual and relational factors on the effectiveness of third party logistics relationships. Journal of Purchasing and Supply Management 17 (3): 167–175.

    Article  Google Scholar 

  • Holter AR, Grant DB, Ritchie J and Shaw N (2008). A framework for purchasing transport services in small and medium size enterprises. International Journal of Physical Distribution and Logistics Management 38 (1): 21–38.

    Article  Google Scholar 

  • Hong KK and Kim YG (2002). The critical success factors for ERP implementation: An organization fit perspective. Information and Management 40 (1): 25–40.

    Article  Google Scholar 

  • Howell DC (2009). Statistical Methods for Psychology. 7th edn. Cengage Learning: Belmont, CA.

    Google Scholar 

  • Hoyle RH (ed) (1995). Structural equation modeling approach: Basic concepts and fundamental issues. In: Structural Equation Modeling: Concepts, Issues and Applications. Sage Publications: Thousand Oaks, CA, pp 1–15.

    Google Scholar 

  • Hsiao HI, Kemp RGM, Vorst JGAJ and Omta SWF (2010). A classification of logistic outsourcing levels and their impact on service performance: Evidence from the food processing industry. International Journal of Production Economics 124 (1): 75–86.

    Article  Google Scholar 

  • Iyer KN, Germain R and Frankwick GL (2004). Supply chain B2B e-commerce and time-based delivery performance. International Journal of Physical Distribution and Logistics Management 34 (8): 645–661.

    Article  Google Scholar 

  • Janoff B (2000). Click and stick. Progressive Grocer 79 (2): 61–64.

    Google Scholar 

  • Jensen HS, Poulfelt F and Kraus S (2010). Managerial routines in professional service firms: Transforming knowledge into competitive advantages. The Service Industries Journal 30 (12): 2045–2062.

    Article  Google Scholar 

  • Kalakota R and Robinson M (2000). e-Business. Addison-Wesley: Reading, MA.

    Google Scholar 

  • Kent JT and Mentzer JT (2003). The effect of investment in interorganizational information technology in a retail supply chain. Journal of Business Logistics 24 (2): 155–175.

    Article  Google Scholar 

  • Kim SW (2009). An investigation on the direct and indirect effect of supply chain integration on firm performance. International Journal of Production Economics 119 (2): 328–346.

    Article  Google Scholar 

  • Kilgore SS, Orlov LM and Nakashima T (2002). Grading apps for inventory and order visibility, Forrester Research, Cambridge.

  • Kline RB (1998). Principles and Practice of Structural Equation Modeling. The Guilford Press: New York.

    Google Scholar 

  • Knoppen D, Christiaanse E and Huysman M (2010). Supply chain relationships: Exploring the linkage between inter-organisational adaptation and learning. Journal of Purchasing and Supply Management 16 (3): 195–205.

    Article  Google Scholar 

  • Kuwaiti ME and Kay JM (2000). The role of performance measurement in business process re-engineering. International Journal of Operations and Production Management 20 (12): 1411–1426.

    Article  Google Scholar 

  • Lai KH, Wong CWY and Cheng TCE (2010). Bundling digitized logistics activities and its performance implications. Industrial Marketing Management 39 (2): 273–286.

    Article  Google Scholar 

  • Lewis I and Talalayevsky A (2000). Third-party logistics: Leveraging information technology. Journal of Business Logistics 21 (2): 173–185.

    Google Scholar 

  • Li G, Yang H, Sun L and Sohal AS (2009). The impact of IT implementation on supply chain integration and performance. International Journal of Production Economics 120 (1): 125–138.

    Article  Google Scholar 

  • Lindell HK and Whitney DJ (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology 86 (1): 114–121.

    Article  Google Scholar 

  • Liu CL and Lyons AC (2011). An analysis of third-party logistics performance and service provision. Transportation Research Part E 47 (4): 547–570.

    Article  Google Scholar 

  • Makhija M (2003). Comparing the resource-based and market-based views of the firm: Empirical evidence from Czech privatization. Strategic Management Journal 24 (5): 433–451.

    Article  Google Scholar 

  • McGinnis MA and Kohn JW (2002). Logistics strategy – revisited. Journal of Business Logistics 23 (2): 1–17.

    Article  Google Scholar 

  • Morgan AJ and Inks SA (2001). Technology and the sales force: Increasing acceptance of sales force automation. Industrial Marketing Management 30 (5): 463–472.

    Article  Google Scholar 

  • Naim M, Aryee G and Potter A (2010). Determining a logistics provider’s flexibility capability. International Journal of Production Economics 127 (1): 39–45.

    Article  Google Scholar 

  • O’Leary-Kelly S and Vokurka RJ (1998). The empirical assessment of construct validity. Journal of Operations Management 16 (4): 387–406.

    Article  Google Scholar 

  • Panayides PM (2007). The impact of organizational learning on relationship orientation, logistics service effectiveness and performance. Industrial Marketing Management 36 (1): 68–80.

    Article  Google Scholar 

  • Pires GD and Aisbett J (2003). The relationship between technology adoption and strategy in business-to-business markets: The case of e-commerce. Industrial Marketing Management 32 (4): 291–300.

    Article  Google Scholar 

  • Podsakoff PM and Organ DW (1986). Self-reports in organizational research: Problems and prospects. Journal of Management 12 (4): 531–544.

    Article  Google Scholar 

  • Podsakoff PM, MacKenzie SB, Lee JY and Podsakoff NP (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology 88 (5): 879–903.

    Article  Google Scholar 

  • Richardson HA, Simmering MJ and Sturman MC (2009). A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods 12 (4): 762–800.

    Article  Google Scholar 

  • Rosenzweig ED and Roth AV (2007). B2B seller competence: Construct development and measurement using a supply chain strategy lens. Journal of Operations Management 25 (6): 1311–1331.

    Article  Google Scholar 

  • Sanders NR and Premus R (2002). IT applications in supply chain organizations: A link between competitive priorities and organizational benefits. Journal of Business Logistics 23 (1): 65–83.

    Article  Google Scholar 

  • Saura IG, Molina MER and Francés DS (2008). Logistic service quality and technology: A comparison between supplier-retailer and retailer-consumer relationships. The International Review of Retail, Distribution and Consumer Research 18 (5): 495–510.

    Article  Google Scholar 

  • Savitskie K (2007). Internal and external logistics information technologies: The performance impact in an international setting. International Journal of Physical Distribution and Logistics Management 37 (6): 454–468.

    Article  Google Scholar 

  • Seufert A and Schiefer J (2005). Enhanced business intelligence – supporting business processes with real-time business analytics. In: Proceedings of the 16th International Workshop on Database and Expert System Applications-DEXA’05, pp 919–925. IEEE computer Society, doi:10.1109/DEXA.2005.86.

  • Shi N, Cheung RK, Xu H and Lai KK (2011). An adaptive routing strategy for freight transportation networks. Journal of the Operational Research Society 62 (4): 799–805.

    Article  Google Scholar 

  • Spector PE (2006). Method variance in organizational research: Truth or urban legend? Organizational Research Methods 9 (2): 221–232.

    Article  Google Scholar 

  • Sriram V and Stump R (2004). Information technology investments in purchasing: An empirical investigation of communications, relationship and performance outcomes. Omega: The International Journal of Management Science 32 (1): 41–55.

    Article  Google Scholar 

  • Tallon PP, Kraemer KL and Gurbaxani V (2000). Executives' perceptions of the business value of information technology: A process-oriented approach. Journal of Management Information Systems 16 (4): 145–174.

    Article  Google Scholar 

  • Teach E (2002). Working on the chain. CFO 18 (9): 83–90.

    Google Scholar 

  • Thomsen E (2002). OLAP Solutions: Building Multidimensional Information Systems. 2nd edn John Wiley & Sons: New York.

    Google Scholar 

  • Toyli J, Hakkinen L, Ojala L and Naula T (2008). Logistics and financial performance: An analysis of 424 Finnish small and medium-sized enterprises. International Journal of Physical Distribution and Logistics Management 38 (1): 57–80.

    Article  Google Scholar 

  • Vickery SK, Jayaram J, Droge C and Calantone R (2003). The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships. Journal of Operations Management 21 (5): 523–539.

    Article  Google Scholar 

  • Wade M and Hulland J (2004). Review: The resource-based view and information systems research: Review, extension, and suggestions for future research. MIS Quarterly 28 (1): 107–142.

    Google Scholar 

  • Wang N, Zhang N and Wang M (2006). Wireless sensors in agriculture and food industry—recent development and future perspective. Computer and Electronics in Agriculture 50 (1): 1–14.

    Article  Google Scholar 

  • Webb BR and Schlemmer F (2008). Predicting web services performance from internet performance: An empirical study of resources and capabilities in e-business SMEs. Journal of Knowledge Management 12 (6): 137–154.

    Article  Google Scholar 

  • Wong CWY, Lai K-H and Cheng TCE (2009). Complementarities and alignment of information systems management, and supply chain management. International Journal of Shipping and Transport Logistics 1 (2): 156–171.

    Article  Google Scholar 

  • Wong CY and Karia N (2010). Explaining the competitive advantage of logistics service providers: A resource-based view approach. International Journal of Production Economics 128 (1): 51–67.

    Article  Google Scholar 

  • Yan L, Zhang Y, Yang LT and Ning HS (2008). The Internet of Things: From RFID to the Next-Generation Pervasive Networked Systems. Auerbach Publications: New York.

    Book  Google Scholar 

  • Zhang AN, Goh M and Meng F (2011). Conceptual modeling for supply chain inventory visibility. International Journal of Production Economics 133 (2): 578–585.

    Article  Google Scholar 

  • Zhao M and Stank TP (2003). Interactions between operational and relational capabilities in fast food service delivery. Transportation Research E 39 (2): 161–173.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are most grateful to Professor Thomas Archibald, the Editor-in-Chief, and the reviewers of Journal of Operational Research Society for their constructive and helpful comments which helped to improve the presentation of the paper considerably. In addition, the authors wish to thank the Research Office of the Hong Kong Polytechnic University for supporting the project (Project Code: G-YK03).

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Choy, K., Gunasekaran, A., Lam, H. et al. Impact of information technology on the performance of logistics industry: the case of Hong Kong and Pearl Delta region. J Oper Res Soc 65, 904–916 (2014). https://doi.org/10.1057/jors.2013.121

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jors.2013.121

Keywords

Navigation