Fluent Essays

Discuss the key components of human resource management.  Pick at least four concepts from chapter nine and describe how these concepts interrelate to individual performance on a team. Review table 9.2  (from the attached Journal) and select one of the dimensions listed, note why it was chosen and how you relate to this behavior.  If you have a personal experience, please share. How do leaders select the best talent? What are some tools they can use to select the best-talent? Please refer to Journal article attached. Word Count: 300 APA7 At least one scholarly article reference © 2019 Straipsnio autoriai. Leidėjas VGTU leidykla „Technika“. Šis straipsnis yra atvirosios prieigos straipsnis, turintis Kūrybinių bendrijų (Creative Commons) licenciją (https://creativecommons.org/licenses/by/4.0/), kuri leidžia neribotą straipsnio ar jo dalių panaudą su privaloma sąlyga nurodyti autorių ir pirminį šaltinį. INVESTIGATION OF DIGITAL RETAIL COMPANIES FINANCIAL PERFORMANCE USING MULTIPLE CRITERIA DECISION ANALYSIS Kotryna URBONAVIČIŪTĖ*, Nijolė MAKNICKIENĖ Vilnius Gediminas Technical University, Vilnius, Lithuania Received 02 March 2019; accepted 26 March 2019 Abstract. Digital retail (online retail or e-commerce) sector is continuously expanding its stake in the global economy each year. According to the statistics, online retail share of the total global retail sales takes approximately 11.9\% in 2018 and is expected to reach 17.5\% at the end of 2021. The same pattern of rapid growth was noticed more than 18 years ago when a burst of dot-com bubble crashed many of the internet-based online shopping companies. “Growth over profits” mental- ity and overestimated perception of the magnitude of online sales resulted in a superficial understanding of the business’ financial performance. Because of that, it is highly necessary to analyze and adequately evaluate the financial performance of digital retail companies. Thus, the purpose of this article is to investigate the top 4 digital retail companies’ financial performance by applying multiple criteria decision analysis (MCDA) TOPSIS and SAW methods to demonstrate that sales turnover is not the only and the prime measure to evaluate the successful company’s financial performance. Keywords: financial performance, digital retail, digital transformation, online retail, e-commerce, MCDA, TOPSIS meth- od, SAW method. Introduction Digitalization or digital transformation is a significant trend in nowadays business world. Digital transforma- tion and transition to digitalization are nearly in all the services of our globalized economy. One of the fields that are affected by digital transformation the most is the retail industry. E-commerce sales have been growing rapidly in the past couple of years. According to the statistics, digital retail sales increased from 1 336 billion USD in 2014 to 2  304 billion USD in 2017 (72\% growth) and is expected to grow up to 4 878 billion USD by the end of 2021 (265\% growth) (Statista, 2018a, 2018b). The growing share of this business sector and the po- tential future impact to the economics emphasises the need for proper investigation and evaluation of the finan- cial performance of this sector players. Sales turnover is a widely used financial indicator of the company’s per- formance and the magnitude of the business. However, this traditional performance determinant might not be the most adequate measurement to evaluate digital company’s success due to the vastly increasing digital transformation process and its impact to the business. Thus, identifying adequate success factors is a crucial matter to evaluate suc- cessful business performance. This scientific article aims to assess the top 4 digital re- tail companies financial performance using 2 of the multic- riteria decision analysis (MCDA) methods  – TOPSIS and SAW – in order to demonstrate that sales turnover is not the only and the prime measure to evaluate the success- ful company’s financial performance and to determine which one is the most successful business. The research conducted in this scientific article is lim- ited to digital (or e-commerce) retail companies. E-com- merce company is defined as a company that does most of its business on the Internet. It excludes Internet service providers or other information technology companies. 1. Theoretical background 1.1. Digital transformation effect to the business Digital transformation (digitalization or digitization) is a trending process of integration of digital technologies into all areas of a business. This transformational process Economics and Management Ekonomika ir vadyba Mokslas – Lietuvos ateitis / Science – Future of Lithuania ISSN 2029-2341 / eISSN 2029-2252 2019 Volume 11, Article ID: mla.2019.9737, 1–9 https://doi.org/10.3846/mla.2019.9737 *Autorius susirašinėti. El. paštas [email protected] https://doi.org/10.3846/mla.2019.9737 K. Urbonavičiūtė, N. Maknickienė. Investigation of digital retail companies financial performance using multiple criteria... 2 and its effect to business have been widely investigated by many authors in their scientific papers. Jürgen Meffert and Anand Swaminathan (2018) agreed that companies that adopt digital technologies in their business would retain their leadership and leverage their strengths. Companies that want to digitalize successfully can either improve their current business model and processes, add new streams of revenue to their business model, or replace their old business models with the new ones. However, since digital transformation is a complicated process, authors C. Matt, T. Hess and A. Benlian in their scientific article Digital Transformation Strategies (2015) argue that increasing digitalization of business processes makes it necessary to develop a better understanding of digital business trans- formation strategies. It is essential to set a clear approach and assign adequate responsibilities for implementation of such conversion change in the business. One of the core elements which helps the company to differentiate itself from the competitive environment and to create addition- al value, according to S. Mithas, A. Tafti and W. Mitchell (2013), is an investment into general information technolo- gies and IT outsourcing. Moreover, according to a survey which was conducted by MIT Sloan Management Review and Capgemini Consulting to investigate how businesses succeed or fail in using digital technology to improve busi- ness performance it was revealed that 78\% of respondents admitted that achieving digital transformation will be- come critical to their organization within next two years. The results of the survey indicated that managers believe that digital technology will bring transformative change to business (Fitzgerald, Kruschwitz, Bonnet, & Welch, 2013). 1.2. Online retail or “e-tail” concept One of the fields that are affected by digital transforma- tion the most is the retail industry. A term online retail or “e-tail” actually covers retailing using a variety of dif- ferent technologies or media (Chen & Leteney, 2000). Ac- cording to the World Trade Organization (World Trade Organization, 2018), e-commerce concept is described as “commercial transactions that are digitally-ordered and either digitally or physically delivered.” Many retail firms that have traditionally operated solely in the store channel (or offline) have been transforming their business process- es to engage with customers in the online channel. This strategic realignment is triggered by the rapid increase in online retail sales that has grown at a faster rate than in- store sales (Ishfaq, Defee, Gibson, & Raja, 2016). The online channel is an information-wealthy and cost-effective channel for product placement. It provides consumers with detailed product information worldwide (Rapp, Baker, Bachrach, Ogilvie, & Beitelspacher, 2015). Consumers are provided by the availability to reach the online site and search for product information anywhere without being bordered by time and place. The most sig- nificant advantage against offline channels is that consum- ers can more easily compare information between various products on the Internet (Zhu, Goraya, & Cai, 2018). Due to its many distinctive advantages, online retail continues to grow. Darrell Rigby (2011) also agrees that digital retail- ing will continue to grow fast because of the vast selection of goods, the reasonable prices, the convenience of shop- ping from home, and the access to product reviews and recommendations. Comfortable shopping, 24/7 conveni- ence, reducing dependence to visit physical stores, travel costs savings, reasonably quick delivery, secure payment, a wide range of products and personalization services, are only a few of many reasons why consumers choose to shop online over traditional retail options. 1.3. Online retail market overview The online retail market overview is restricted to digital (or e-commerce) retail companies only. E-commerce com- pany is defined as a company that does most of its busi- ness on the Internet. It excludes Internet service providers or other information technology companies. According to revenue (total sales turnover), the top 4 online retail companies are Amazon, Inc, JD.com, Inc, Alibaba Group Holding Ltd and eBay, Inc (Table 1). Table 1. Top 8 digital retail companies1 in the world according to turnover (in millions US$)2 Company Country 2017 2016 2015 2014 Amazon, Inc USA $177 866 $135 987 $107 006 $88 988 JD.com, Inc China $55 641 $37 167 $27 880 $18 537 Alibaba Group Holding Ltd.3 China $22 965 $15 686 $12 293 $8 463 eBay, Inc USA $9 567 $8 979 $8 592 $8 790 Rakuten, Inc Japan $8 407 $7 123 $5 896 $5 690 Zalando SE Germany $5 377 $3 834 $3 232 $2 691 ASOS plc UK $2 595 $1 777 $1 706 $1 515 B2W Companhia Digital Brazil $2 148 $2 641 $2 308 $ 2 964 1 Compiled by the author according to financial data from the official firms’ annual reports. 2 In cases were financial information was stated in other currency than US Dollars (USD), the figures were converted using the year- end-date FX exchange rate stated in https://www.oanda.com/currency/converter/ 3 Financial year of Alibaba Group Holding Ltd. ends as of 31st March. https://www.oanda.com/currency/converter/ Mokslas – Lietuvos ateitis / Science – Future of Lithuania, 2019, 11, Article ID: mla.2019.9737 3 The top 1 place is firmly occupied by e-commerce company Amazon also known as Amazon.com. Founded in 1994 in Seattle by Jeff Bezos, Amazon has become a household name when it comes to online shopping. This internet company today has the most substantial revenue and is considered as the biggest employer of all the internet companies with a workforce of more than 566 thousand employees. Jingdong or JD.com is an e-commerce company operating in Beijing. Jingdong has well over a quarter of a billion registered users as of 2018. It was founded in 1998 and started trading online six years later. Alibaba is the biggest e-commerce company in Asia with headquarters in Hangzhou; China has more than a billion users worldwide. Jack Ma, the founder of Alibaba, was rejected from more than 30 job posts in the early 1990s when he started mak- ing websites for companies with his wife and a friend. The business grew exponentially, and in the year 1999, Alibaba Group was founded. Alibaba has two major portals that run under it, Alibaba and AliExpress. eBay is an e-commerce company which was founded in 1995 by a computer pro- grammer named Pierre Omidyar. It was one of the first suc- cessful dot-com bubble companies that epitomized online shopping. Its most distinctive feature is the online auction feature, alongside a conventional buy-it-now shopping op- tion. These top 4 digital retail companies will be further assessed in this scientific article. 1.4. Factors affecting a firm’s financial performance Successful financial business performance can be de- scribed in various ways; it depends on a company’s strat- egy and management goals. However, some widely used performance indicators truly reflect a positive and a lead- ing to success business. The main factors that affect a firm’s financial performance and that are going to be analyzed in this paper are: – The company’s stock price growth – Revenue – Gross Profit margin – Net Profit margin – Return on Assets (ROA) – Return on Equity (ROE) – Return on Sales (ROS) – Cost of investment in digital technologies – GDP per capita of the country of the headquarters – Total ESG score The stock price is one of the main factors of the suc- cessful company’s financial performance. Fluctuations in a stock price not only brings gain or loss to the investors but also can indicate triggers in business performance. The stock market wholly and quickly incorporates public information into the stock price. Thus, the evaluation of the company’s market share price growth can indicate the success of a business in a given market condition. Ratio analysis is perhaps the most commonly used tool in financial analysis. Financial ratios allow to assess and analyze the strengths and weaknesses of a given company about such measures as liquidity, profitability, perfor- mance, and growth and compare them to other companies in the market or an industry standard (Hitchner, 2011). Additionally, profitability ratios are also widely used by the investors of the company since they help to measure and evaluate the ability of a company to generate income (or profit) relative to revenue, assets, operating costs, and shareholders’ equity during a specific period. Ratios in- dicate how well a firm utilizes its assets to produce profit and, thus, create value to shareholders (Corporate Finance Institute, 2018). Profitability ratios that will be used in this paper to analyze the previously identified research object are: 1 00\% Gross Profit Gross Profit Margin x Revenue = ; (1) 1 00\% Net Profit Net Profit margin x Revenue = ; (2) ( ) Operating profitReturn on Sales ROS Revenue = ; (3) ( ) Net Profit Return on Assets ROA Total Assets = ; (4) ( ) Net ProfitReturn on Equity ROE Owners Equity′ = . (5) Gross Profit margin compares gross profit to sales revenue. It shows how much a business is earning, considering the needed costs to produce its goods and services. Net Profit margin is the bottom line and takes everything into account. It provides the final picture of how profitable a company is after all expenses including interest and taxes. Return on Sales (ROS) is an opera- tional efficiency ratio. This measure provides informa- tion on how much profit is being generated per dollar of the company’s sales. Return on Assets (ROA) expresses a percentage of the company’s net revenue in relation to the total assets. ROA ratio shows how much after-tax profit a company generates for 1 dollar of assets it has. Return on equity (ROE) indicates the rate of return of 1 dollar that the company’s shareholders have invested in the business. As revenue is used in the majority of profitability ra- tios and traditionally is one of the primary measures when evaluating a company’s magnitude and selling power, it will also be assessed separately as one of the influencing factors of business financial performance. Majority of different companies are focusing on dig- italization and trying to find a way forward to develop business cases for such technology adoption. Mithas, Tafti, and Mitchell (2013) in their article How a Firm’s Com- petitive Environment and Digital Strategic Posture Influence Digital Business Strategy discuss how the competitive in- dustry environment shapes the way that digital strategic posture influences firms realized digital business strategy. According to the authors, increasing digitization of busi- ness processes, products, and services makes it impera- tive to develop a better understanding of digital business strategies. Digital strategies such as investments in general K. Urbonavičiūtė, N. Maknickienė. Investigation of digital retail companies financial performance using multiple criteria... 4 information technology and IT outsourcing are significant elements of overall business strategy. However, consider- able investments in the digital environment and the devel- opment of online platforms might lead to high operating expenses. Hence, digital companies might provide lower prices to the customer and significantly increase sales turnover but suffer in operating expenses regarding digi- tal technologies maintenance costs. Because of that, cost of investment in digital technologies is a crucial factor to evaluate successful business performance. Another influencing factor of the company’s finan- cial performance on the level of economic development measured as Gross Domestic Product per capita (GDP per capita). Tim Jackson (2009) in a book Prosperity Without Growth. Economics for a Finite Planet discuss the GDP per capita and its value to economic growth. According to the author: “The GDP is broadly speaking a measure of “eco- nomic activity” in a nation or region. The GDP counts the economic value of goods and services exchanged on the market. If we’re spending our money on more and more commodities, it’s because we value them. We wouldn’t value them if they weren’t at the same time improving our lives. Hence a continually increasing per capita GDP is a reasonable proxy for rising prosperity”. GDP per capita identifies a citizen ability to consume more, and higher incomes mean increased choices, more prosperous lives, improved quality of life for those who benefit from them. Thus, a successful business adds its part to a better GDP per capita measure and vice versa – high GDP per capita can also influence a better performance of a company in the country that it operates in. The last criteria that could be analyzed as one of the indicators of a successful company’s performance is the environmental, social and governance (ESG) rating. Be- ing a sustainable business, which also shows a social re- sponsibility is necessary in the business world. ESG rat- ings measure how well companies proactively manage the environmental, social and governance issues that are the most material to their business and provide an assessment of companies’ ability to mitigate ESG risks. The ESG rating is a quantitative score on a scale of 1–100 and is catego- rized across five risk levels: negligible, low, medium, high and severe. Most international and domestic public (and many private) companies are being evaluated and rated on their ESG performance. Institutional investors, asset managers, financial institutions and other stakeholders are increasingly relying on these reports and ratings to assess and measure company ESG performance over time and as compared to peers. Hence, ESG rating is also one of the factors that could influence a company’s success (Sustai- nanalytics, 2018). The criteria mentioned above will be further used for application of multiple criteria decision analysis methods to assess which one of the selected top 4 digital retail com- panies’ is operating the most successfully. 2. Multiple criteria decision analysis (MCDA) methodology To assess the criteria and their effect on a project (or alter- native) multiple criteria decision analysis (MCDA) meth- ods (or multicriteria methods) are widely used. TOPSIS and SAW methods will be further analyzed and applied in this article to evaluate the financial performance of the top 4 digital retail companies. 2.1. TOPSIS method TOPSIS (technique for order preference by similarity to ideal solution) method is a popular approach to multiple criteria decision analysis (MCDA) developed by Hwang and Yoon. TOPSIS has been widely used to rank the pref- erence order of alternatives and determine the optimal choice (T. Y. Chen & Tsao, 2008). The positive ideal solu- tion is a solution that maximizes the benefit criteria and minimizes the cost criteria, whereas the negative ideal solution maximizes the cost criteria and minimizes the benefit criteria. The best alternative is the one with a value which is closest to the positive ideal solution and has the farthest distance from the negative ideal solution (Wang & Elhag, 2006). The TOPSIS method can be applied using several cal- culation steps: the first step is gathering the performance values of the alternatives according to the criteria set. The second step is performance values normalization. Then, normalized values need to be weighted (multiplied by the weights of the criteria), and the distances to the positive ideal and negative ideal solutions are calculated. Lastly, the relative closeness to the positive ideal solution is indicated, and ranking of the alternatives is performed (Ishizaka & Nemery, 2013). These steps are detailed explained below: 1. Construct the decision matrix and determine the weight of criteria: ( ) ijx x= . (6) 2. Normalize the decision matrix: 2 1 ij ij m iji x n x = = ∑ . (7) 3. Calculate the weighted normalized decision matrix: ij j ijw nν = (8) for 1, , ; 1, , .i m j n= … = … the weight of the th criterion.jw j− − 4. Determine the positive ideal and negative ideal so- lutions: ( )1 2, , , max , min ; n ij ij ii V j I j J+ + + +     = ν ν … ν = ν ∈ ν ∈        (9) Mokslas – Lietuvos ateitis / Science – Future of Lithuania, 2019, 11, Article ID: mla.2019.9737 5 ( )1 2, , , min , max ,n ij ij i i V j I j J− − − −    = ν ν … ν = ν ∈ ν ∈          (10) where I is associated with benefit criteria and J with the cost criteria, i = 1, ..., m; j = 1, ..., n; 5. Calculate the separation measures from the positive ideal solution and the negative ideal solution: ( )2 1 , 1, , ; n i ij i j S i m+ + = = ν −ν = …∑ (11) ( )2 1 , 1, 2, , . n i ij i j S i m− − = = ν −ν = …∑ (12) 6. Calculate the relative closeness to the positive ideal solution: . ii i i S P S S − − + = + (13) 7. Rank the alternatives according to the relative close- ness to the ideal solution. The bigger the Pi, the bet- ter the alternative. The best alternative is the one with the highest relative closeness to the ideal solu- tion. TOPSIS is considered as a very understandable and straightforward method. However, the drawback of this method is that the extreme value of the criteria might be preferred more than the compromise one and it might provide illogical results. Thus, another MCDA method is being used to assess and rank the top 4 digital retail com- panies financial performance results. 2.2. SAW method Simple Additive Weighting (SAW) method is often also known as weighted summing method. The basic concept of SAW method is to find the weighted sum of perfor- mance ratings on each alternative on all attributes. The SAW method requires the process of normalizing the de- cision matrix (Xij) to a scale comparable to all existing alternative ratings (Anggraeni, Huda, Maseleno, Safar, & Jasmi, 2018). 1. The sum Sj of the weighted normalized values of all the criteria is calculated for the j-th object. The alternatives are then ranked according to the calcu- lated values Sj from the largest value to the lowest one. The largest value of the sum Sj reflects the best alternative: 1 , m j i ij i S w r = = ∑ (14) wi – weight of the i – th criterion normalized th criterion’s value for th object; 1, , ; 1, , ijr i j i m j n− − − = … = … – normalized i – th criterion’s value for j – th object; i = 1, ..., m; j = 1, ..., n m – the number of the criteria used n – the number of the criteria used (alternatives) compared 2. One of the limitations of the SAW method is that it can only be used when all the criteria are maximiz- ing. Thus, if the criteria are minimizing, this can be implemented by converting the criteria to maximiz- ing ones using the below the formula. In this way, the minimal criteria value minij ij j r r= acquires the largest value equal to unity: min , ij j ij ij r r r = (15) rij – i – th criterion’s value for j – th alternative, min the smallest th criterion’s value for all the alternatives comparedij j r i− − – the smallest i – th criterion’s value for all the alternatives compared, denotes the converted valuesijr −– detonates the converted values. 3. Normalization of the initial data is performed in order to the largest maximizing value of the criteria value would get the largest value equal to unity. The formula used for maximizing criteria is below: , max ij ij ij j r r r = (16) max the largest th criterion’s value of the alternatives compared. ij j r i− −– the largest i – th criterion’s value of the alternatives compared. 4. Another drawback of the SAW method is that all criteria values rij should be positive. In cases when there are negative values of the criteria used, these values are transformed into positive ones using the formula below (Podvezko, 2011): min 1 .ij ij ij j r r r= + + (17) SAW method is a very easy-to-apply method and is very commonly used in alternatives ranking. However, due to its drawbacks mentioned above and usage of trans- formational steps might distort the ranking results. 3. Investigation of top 4 digital retail companies’ financial performance 3.1. Criteria Ten criteria determined as factors influencing a company’s financial performance will be further used in applying the selected methods (Table 2). Criterion C8 will be set as a cost criterion because investment in digital technologies, product and content development, and online platforms requires a lot of ex- penses. Even though it brings efficiency to a company’s processes and more qualitative services, however, the company should be focused on how to reduce such ex- penses since it affects the profitability of a business. The remaining nine success factors will be set as benefit crite- ria because usually a firm would concentrate on the maxi- mization of these determinants and the higher the result of the factor, the better performance of a company. Factor C10 was measured according to the ranking of ESG rat- ings, applying for the position number according to five K. Urbonavičiūtė, N. Maknickienė. Investigation of digital retail companies financial performance using multiple criteria... 6 risk levels: 1  – negligible, 2  – low, 3  – medium, 4  – high and 5 – severe (Sustainanalytics, 2018). 3.2. Application of the TOPSIS method First, the decision matrix was constructed of the select- ed alternatives (top 4 digital retail companies: Amazon. com, JD.com, Alibaba.com, and eBay.com) and 10 fac- tors (or criteria) identified in the previous section. More- over, weights of the criteria were calculated. In order to have a fair and not subjective view, the weights to the criteria were equally set to 0,1 summing up to a total of 1 (Table 3). Secondly, a normalized decision matrix was computed (Table 4). Normalization was performed by dividing each criteria value by the square root of the squared sum of the total criteria values. Then the weighted normalized decision matrix was calculated (Table  5) by multiplying identified weights by each value of the normalized decision matrix cell. Table 2. Data of the top 4 digital retail companies4 (source: Alibaba Group Holding Ltd., 2019; Amazon, Inc., 2019; eBay, Inc., 2019; JD.com, Inc., 2019; The World Bank, 2019; Yahoo Finance, 2018)1 AMAZON JD ALIBABA EBAY The company’s stock price growth in 2017 (\%) 55\% 60\% 95\% 27\% Revenue (millions $) 177 866 55 641 22 965 9 567 Gross Profit margin (\%) 37\% 14\% 62\% 77\% Net Profit margin (\%) 2\% 0\% 28\% –11\% Return on Assets (ROA) (\%) 2\% 0\% 30\% 24\% Return on Equity (ROE) (\%) 2\% 0\% 9\% –4\% Return on Sales (ROS) (\%) 11\% 0\% 16\% –13\% Investment in digital technologies & product development (millions $) 22 620 1 022 2 479 1 224 GDP per capita in country of the headquarters in 2017 ($) 59 928 8 827 8 827 59 928 Total ESG score 45 (2) 43 (2) 49 (3) 64 (4) Table 3. Weights of the criteria and performances of the alternatives Benefit Benefit Benefit Benefit Benefit Benefit Benefit Cost Benefit Benefit C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 Weights 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Amazon 55\% 177 866 37\% 2\% 2\% 2\% 11\% 22 620 59 928 2 JD 60\% 55 641 14\% 0\% 0\% 0\% 0\% 1 022 8 827 2 Alibaba 95\% 22 965 62\% 28\% 30\% 9\% 16\% 2 479 8 827 3 eBay 27\% 9 567 77\% –11\% 24\% –4\% –13\% 1 224 59 928 4 Table 4. Normalized decision matrix C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 Amazon 0.431 0.946 0.347 0.066 …