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Interview & survey method to understand the factors affecting online shopping behaviour

The previous article discussed the conceptual framework for factors affecting the online shopping behaviour of generation Z and millennial consumers. The conceptual framework showed the important variables of the study. It also presented the research questions and the hypotheses to be tested. The aim of this study is to determine the factors affecting the buying behaviour of generation Z and millennials. Furthermore, this study will also identify the strategies adopted by e-commerce businesses to influence them. For this purpose, the study adopts a multi-dimensional research approach that includes interview and survey methods.

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Order fulfilment strategies in supply chain management

Order fulfilment is an integral part of the Supply Chain Management (SCM) process. It works towards fulfilling the requirements of the consumers in the process (Wu et al., 2016). However, even though fulfilling orders is cardinal in setting a supply chain in motion, the order fulfilment process of SCM involves other activities. It is a composite process that involves designing a framework or a process to reduce costs. Furthermore, it requires the establishment of a cross-functional network with all SCM stakeholders and coordination between them, along with sound logistics management (Croxton, 2003). A perfectly implemented order fulfilment procedure helps improve the overall SCM quality (Mishra and Sharma, 2014).

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Activity-based costing or ABC model in logistics

The Activity-based Costing (ABC) model is one of the many approaches to estimate logistics costs. The term was coined in the late 1980s by Robert Cooper and Robert Kaplan in their article titled, “Measure Costs Right: Make the Right Decisions.” ABC model is defined as a management accounting technique which allocates costs to different activities that consume organizational resources thereby identifying the costs per product or service or customer (Themido et al., 2000). Conventionally, logistics costs were determined as a gross figure, but later on, it was realized that such measures usually present a fallacious account of the logistics costs. This is because logistics functions take place in a dynamic environment.

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Understanding the value of logistical cost in a business

Often the efficiency of the logistics function of a business is estimated in terms of value. But there is no concrete definition of the term ‘value’ in logistics (Francis et al., 2014). The need for defining value did not arise in the past because earlier, logistics was considered to be a cost. Since two decades emphasis over recognizing logistics as a value-adding function was made (Rutner and Langley, 2000; Kilibarda, Andrejić and Popović, 2013). Value is created in the logistics when the capabilities of the elements of the supply chain are combined in a way that it leads to the improvement of competitive advantage of all parties (Hammervoll, 2009). Conventionally value in logistics denoted cost efficiency but now variables like mutual learning, customer satisfaction and agility in the supply chain have also gained significance.

Different methods of value measurement in logistics

Measuring logistics performance implies quantification of the current state of logistics and identifying the improvement potentials (Dörnhöfer, Schröder and Günthner, 2016). Like the concept of value, there exist several financial and non-financial value measurement metrics in logistics. Some authors relate value to the timeliness of logistics operations and calculate it by taking the total time taken to complete the logistics process.

Another common value measurement metric in logistics is Customer Value Added (CVA) which denotes the customers’ perception of value-added from logistics. It is measured as the comparison of total costs in relation to the perceived benefits of the customer. Furthermore, the logistics value can also be measured as total revenue generated, profitability, and shareholder value (Kilibarda, Andrejić and Popović, 2013). The selection of a specific performance measurement indicator depends upon multiple factors such as the:

  • type of industry,
  • overall organizational goal and
  • organizational goal of the organization (Dörnhöfer, Schröder and Günthner, 2016).

However, there is no precise value measure in logistics. While financial measures overlook other aspects of value, the measures relating to customer satisfaction and customer value-added are inconsistent in nature because the notion of value may keep changing for the customers over time (Lambert, 2014).

Value metrics Advantages Disadvantages
Customer satisfaction Improves revenues, logistics costs, market share, and customer experience. Too much reliance on customers for making logistics-related decisions, in spite of high costs.
Customer value-added Also leads to higher revenues, profit margins and shareholder value Fails to measure the financial impact of focusing on customer value-added.
Total cost analysis Focus is on costs. Makes it easy for managers to reduce logistics costs. It is more time-consuming as each customer’s evaluation has to be assessed. Does not study logistics costs in a standalone manner. It is also expensive to obtain information from every customer.
Strategic profit model Measures net profit, return on assets and return on net worth. Helps managers make better investment decisions. Does not take into consideration the timing of cash flows.
Shareholder value Values money in terms of time. Considers the risk of investment. It is challenging to implement in areas such as discount rates, projected cash flows, planning etc. due to a missing link between business strategy and shareholder value. Moreover, it is time-consuming, expensive and requires a vast amount of data.

Figure 1: Comparative advantages and disadvantages of value metrics in logistics (Lambert, 2014)

An example of time value in logistics

In February 2018, almost two-thirds of the UK outlets of the renowned global fast-food chain KFC had to close their operations for a day because of their new logistics partner, DHL had delayed the supply of raw material. As the company sells perishable food items, timeliness represents the value in KFC’s logistics system. This not only caused financial damage to the company but also a reputational setback for the brand KFC (Pooley, 2018).

An example of cost-efficiency in logistics

IKEA, the world’s largest retailer of home furnishing operates in over 29 nations. Its reputation precedes with not only its high-quality products but also the affordability. A massive share of this success has been contributed by the company’s well-designed logistics. IKEA focuses on reducing the costs so that its products can be afforded by the masses. To that purpose, IKEA manufactures most of its products from recycled products and uses comparatively lesser material in the production. This not only reduces the inventory of raw materials but also the transportation costs. Furthermore, the integrated supplier network and the selling of unassembled products saves costs. This way IKEA creates value in logistics through cost-cutting.

An overview of different logistical costs

The essence of calculating logistical costs lies in the fact that logistics while creating value is also a cost incurring function. The estimation of costs incurred for the logistics functions is important to enabling better decision-making. But, logistics involves several processes hence the assessment of logistical costs is usually complicated as they are fragmented around several processes (Halinen, 2015). The logistical costs can be fixed and variable or planned and unplanned.

For example; warehouse costs are a planned cost but, due to adverse weather conditions, the business might maintain excess inventory. The additional warehousing and inventory management costs are unplanned costs.

Similarly, the costs incurred on wastages can be divided by:

  • their nature,
  • cost centre,
  • the relationship with other processes and
  • on the basis of volatility (Stępień et al., 2016).

Another recent approach to classify logistics costs includes categorizing the costs on the basis of functional areas of the business processes. This has been depicted through the below diagram (Chukurna, 2016).

Classification of logistical costs by functional areas

Figure 2: Classification of logistical costs by functional areas. (Chukurna, 2016)
Classification of logistical costs by business processes

Figure 3: Classification of logistical costs by business processes (Chukarna, 2016)

Why it is important to balance logistical costs and value?

Businesses need to have a clear perspective on how they define value and what methods to use to estimate the logistical costs. The logistics function can create value for the customers and the business. This can be explained through the example of Tesla Incorporation which is known for its innovative electric cars. Tesla cars are embedded with Artificial Intelligence (AI) and thus considered as a premium name in the technologically-driven car market. In fact, with its high paced innovation ability, Tesla often threatens its competitors to become obsolete soon. But, the company struggles to satiate its target audience due to its:

  • poor logistics functions,
  • poor quality control,
  • the timelines are missed and,
  • the supplier network is narrow and insufficient.

The outcome is that the company often delivers cars with defects thus inviting a refund or exchange. Tesla also misses the production and delivery schedules and ultimately faces customer dissatisfaction (Lambert, 2017; Boudette, 2018; Jurvetson, 2018). Tesla is probably unclear about what value it wants to achieve through its logistics function and is letting its endeavours go in vain.


  • Boudette, N. (2018) What Tesla’s ‘Delivery Logistics Hell’ Is Like for Model 3 Buyers, The New York Times.
  • Dörnhöfer, M., Schröder, F. and Günthner, W. A. (2016) ‘Logistics performance measurement system for the automotive industry’, Logistics Research. Springer Berlin Heidelberg, 9(1), p. 11. doi: 10.1007/s12159-016-0138-7.
  • Francis, M. et al. (2014) ‘The meaning of “value” in purchasing, logistics and operations management’, International Journal of Production Research, 52(22), pp. 6576–6589. doi: 10.1080/00207543.2014.903349.
  • Halinen, H.-M. (2015) Understanding the Concept of Logistics Cost in Manufacturing.
  • Hammervoll, T. (2009) ‘Value-Creation Logic in Supply Chain Relationships’, Journal of Business-to-Business Marketing.  Taylor & Francis Group , 16(3), pp. 220–241. doi: 10.1080/10517120802484577.
  • Jurvetson, S. (2018) Case study: How Tesla changed the auto industry, Supply Chain Dive.
  • Kilibarda, M. J., Andrejić, M. M. and Popović, V. J. (2013) ‘CREATING AND MEASURING LOGISTICS VALUE’, in 1st Logistics International Conference. Belgrade: LOGIC, pp. 197–202.
  • Lambert, D. M. (2014) ‘Measuring and Selling the Value of Logistics’, The International Journal of Logistics Management, 11(1), pp. 1–17. doi: 10.1108/09574090010806038.
  • Lambert, F. (2017) Tesla’s over-the-air software updates make other vehicles ‘highly vulnerable to obsolescence’, says analyst, Electrek.
  • Pooley, C. R. (2018) KFC runs out of chicken in logistics fiasco, Financial Times.
  • Rutner, S. M. and Langley, C. J. (2000) ‘Logistics Value: Definition, Process and Measurement’, The International Journal of Logistics Management. MCB UP Ltd, 11(2), pp. 73–82. doi: 10.1108/09574090010806173.
  • Stępień, M. et al. (2016) ‘Identification and Measurement of Logistics Cost Parameters in the Company’, Transportation Research Procedia, 16, pp. 490–497. doi: 10.1016/j.trpro.2016.11.046.

Use of Hawkins Stern’s impulse buying theory (1962) in online shopping

The widespread popularity of online shopping in current times has undoubtedly enhanced the efficiency of the entire buying process. It has also posed to digital marketers the threat of losing to competition. This is why the marketers keep on trying novel tactics to fascinate new customers as well as retain the existing ones. One of the many tactics includes encouraging customers to buy impulsively (Foroughi et al., 2013). This phenomenon can be better explained through Hawkins Stern’s impulse buying theory (1962). This theory offers valuable insight into different circumstances under which the consumers are likely to indulge in impulse buying.

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A review of models on buying decision-making process in online shopping

A previous article in this study explored a few key models of the consumer decision-making process. The article explained its application in the present shopping environment where online shopping has overtaken traditional in-store shopping in terms of growth. Over the years, many more such models of consumer behaviour have been proposed, particularly in the context of the online shopping environment. A few of these models are; Engel-Kollat Blackwell Model, Kim, Ferrin and Rao’s (2008) model, Yan and Dai’s (2009) model, and Fang’s (2012) model.

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Maslow’s hierarchy needs as a theory for online marketing

Maslow’s hierarchal need is known as a theoretical psychology concept which deals with five stages of the human need in a pyramid (Lee & Hanna, 2015). The basic needs of the human being comprise:

  1. psychological needs,
  2. safety and security needs,
  3. love and belongings need,
  4. self-esteem needs and,
  5. self-actualisation (Harrigan & Commons, 2015).

This model remains used by the businesses to target a particular group of customers through social media by community or group for promotions.

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The challenges of using forecasting techniques in logistics

Quantitative forecasting techniques refers to the approaches of forecasting used for examining the future trends by analysing the historical data. These forecasting techniques are applied through static methods like time series forecasting and casual forecasting (Spedding & Chan, 2010). The casual forecasting is conducted using simple or multiple regression models. On the other hand, casual forecasting is executed through the use of autoregressive moving average models. In logistics, time series forecasting focuses on analyzing the change in business strategies over a period of time. This forecasting is done using moving average and exponential smoothing which uses mathematical formulas to identify the forthcoming claim of the consumers addressed (Dombi, et al., 2018).

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