The title of this article is ambiguous and it can be misleading to readers since it does not provide a concise description of the contents of the article. According to the contents of the article, the authors should have clearly stated that the article is covering a proposed DEA-BSC model for the evaluation of the operational efficiency of airlines. As it is, the title only informs the readers that the article is about a BSC envelopment approach which does not inform the readers that the investigation is about a new model that will be proposed with the integration of DEA and BSC.
Therefore, we felt that the article title did not clearly describe the contents of the article since it doesn’t clearly show that an integrated model is being investigated. This means that readers can be disappointed by reading the article or they may not clearly understand its contents. Therefore, we have proposed a new title for the article as follows:
“An integrated balanced scorecard – data envelopment analysis (DEA-BSC) model for the evaluation of the operational efficiency of airlines.”
The abstract covers sections that include the purpose, design/methodology, findings, limitations/implications, practical implications, originality/value and the keywords. We think that this section provides clear information that can be used by the readers to navigate through the document. It clarifies the sections and provides a brief and concise description of what readers can expect from each section. In this regard the article abstract is important and it is better than other documents where the authors do not provide an abstract with clear sections.
This approach is advantageous since the reader gets a preview into the entire document meaning that one can understand the problem under study and the solutions that have been provided. When articles lack such an approach, the reader may lose interest in the article since the purpose, results and conclusion of the article are unclear. Most readers do not want to look for all this information in the document – they want a clearly presented work that is easy to read and quick to comprehend (USC, 2014). Another important point to consider is that most documents or articles include an abstract that is not divided into sections and it presents information in a paragraph or a set of sentences. The abstract on this document is clear since it is divided into sections allowing the readers to quickly pick insights and information before reading the article.
The abstract is also advantageous since it provides the findings and practical implications of the study. It details, in a short manner, the findings of the research. It also details the extent to which the objectives of the research have been met. Also, the research limitations and practical implications are provided. For instance, one practical implication of how the BSC serves as a complement of DEA is provided. Also, another practical implication is that business executives could use DEA-BSC results such as the amount of slacks, efficiency frontiers and benchmark learning partners to develop & implement strategies for improvement (Wann-Yih & Ying-Kai, 2013).
Even though the abstract is insightful and simple, it has one major disadvantage. Since it provides so much information about every section of the article, it may discourage readers from reading the entire article since all the information is presented in a precise way in the abstract (Belcher, 2009). An abstract should always provide enough information to spark the interest of readers but not a comprehensive description of the contents of the contents of the article such that readers can get all the information from this section. This may lead to a situation where readers are not curious to read the entire article to understand the study and its results.
The article introduction provides an overview into the global air transport industry. It briefly considers the competitiveness and operating environment of the global airline industry. This is an important approach to give the reader some insights into the industry and background into the area of study being considered. An overview of global operations, supply and demand factors and the general operational environment is important to provide a background and present insights to the readers. It also makes readers more curious raising their interest in the article.
This section also introduces readers into the issues of performance evaluation. It shows that most companies have approached their performance evaluation from a financial point of view. This means that companies have not in the past looked at operational efficiency as part of their evaluation. This gives readers a background of the area of study. This section also introduces the data envelopment analysis (DEA) and balanced scorecard (BSC) – it provides a description of these methods and an argument why they are advantageous or important (Luca & Stefano, 2010). Another important aspect of the introduction is that it provides the aims of the study. Readers can clearly understand the aims of the article and the important contributions. These are clearly enumerated for clarity. I think that this introduction has covered every important aspect that is required. It provides a background, introduction to new terms and a clear & concise statement of the aims and contributions of the study.
We think that the main issue being covered by the authors in this article is the issue of performance evaluation techniques. The authors note that there are several performance evaluation techniques that have been developed in the past and DEA-BSC mechanism is among the best of them. In retrospect, the authors consider the problem that most firms focus on financial performance when evaluating the performance of firms leaving operational performance evaluation untouched. The article considers DEA and BSC as some of the most important modern methods of performance evaluation (Niven, 2010). The DEA method was developed through linear programming and it is quite useful where there are multiple inputs and outputs which makes ratio based comparisons for this data quite difficult (Theodore & Maria, 2014).
The DEA method has the capacity to identify best practice groups and inefficient units compared to the best practice units. When the inefficient units are discovered, the DEA can specify the amount of improvement that is required for all units so that they can become efficiency frontiers (Wann-Yih & Ying-Kai, 2013). By combining DEA and BSC, the authors develop an integrated model to evaluate the performance of institutions. Even though such a model has been tested in 50 local exchange carrier in the US, it has not been attempted in the airline industry. The authors therefore develop a model that measure the operating performance of airline firms through the incorporating of lagging & leading factors of Balanced Scorecard for all variables (input/output) variables of the DEA. This attempt was successful since the efficiency frontiers were identified by the DEA as well as the benchmarking partners and inefficient slacks. The study integrated 7 inputs and 4 outputs parameters of BSC into DEA. This means that the DEA could accommodate the leading and lagging BSC parameters and identify their relationships. From a BSC standpoint, the DMU performance can be evaluated through a comparative review of inefficient and efficient DMUs (Akkucuk, 2014). Therefore, since no other earlier studies had completed a successful integration of DEA and BSC, the results of this study were conclusive and they provided a baseline that can be used for further validation.
Also, the model developed can be used for performance optimization on individual DMUs to come up with efficiency values. The other critical point is that managers in airline businesses should not only focus on financial performance but they should also consider the operational performance among competitors. They can use DEA-BSC model to find efficiency frontiers and sources of inefficiency.
An evaluation of the research approach used in this article reveals that the author conducted in-depth research to develop conclusions. The author began by conducting a preliminary literature review of the methods being studied in this paper. However, the literature review conducted is too short which means that the authors assumed that readers will have prior knowledge of DEA and BSC. Except for the definitions of these terms that are provided at the introduction section, the authors did not present enough evaluation of existing literature to provide information and insights to the readers. Literature review should provide a comprehensive review and assessment of the contents of recent papers to assess what other authors have written about the topic (Kumar, 2010). In this regard, we felt that the authors should have reviewed more articles and provided more information. We propose that at the authors should have reviewed at least 15 recent articles to lay a foundation for their quantitative investigations using airline data. After a literature review, the study develops a model that was used to study the data that was used in the study. The study collected samples and data from the 50 major airline businesses in the world and these companies were ranked according to their total revenue.
The revenues used to gauge these companies were extracted from the annual reports of 2012 as noted in the table of measurements of result constructs. The lack of sufficient data led to the deletion of some companies leaving the study with only 38 viable companies that were used in the study (Wann-Yih & Ying-Kai, 2013). The researchers excluded 12 companies from the samples due to lack of financial data. However, they have not explained why those companies lacked data as it is odd that those selected companies were the major ones around the world. Moreover, the authors have not explained how this could affect their results even though it is said 38 sample companies were appropriate as well.
The study considered 11 variables as noted in the Appendix which mean that the 38 samples being used were appropriate. It seems the research variables were selected carefully (as shown in appendices – Table of measurements of research constructs) and the study has explained their choice of measurements relating to the practical position of airline companies. However, it is criticized that the cause-and-effect relationship among those indicators has not been identified in detail as it is essential to achieve the purpose of the paper. Another limitation of the research is due to the fact that the canonical correlation analyses were introduced to test the interrelationship among four perspectives of the BSC. Although there were some interpretations as to the analysis and two important relationships were identified, the canonical analyses have not been explained how to be conducted that would confuse the audience, and the reader is not told why these analyses are relevant to the purpose of the paper.
The results of the study were that 27 airline DMUs showed good performance while there were 11 inefficient DMUs that required improvement. According to the operating mode of the airlines, 11 inefficient DMUs need to improve their operational performance. These results prove that out of the 38 airlines in the dataset (as shown in Appendix – A BSC envelopment Approach), at least 27 are performing in efficiency frontiers while the remaining 11 need to improve their performance (Wann-Yih & Ying-Kai, 2013).
We strongly assert that this is a result that demonstrates that the DEA-BSC model is quite useful for the decision making units or departments in airlines since it provides insights into how the airlines can reduce their inputs without decreasing their total output. Another way of looking at it is from the perspective that this model has demonstrated how airlines can increase their output without necessarily increasing their inputs. On the other hand, there are some issues with the results section of this study. The authors assumed that readers can understand information in graphs and tables, but recent studies show that most readers prefer information that is presented in a simple manner (Appa & Mathirajan, 2009). In this regard, the authors should have simplified the work by providing clear conclusions devoid of technical terms, formulas and other sources of complexity. Therefore, we propose that even though the results can be provided in graphs and tables, the authors should have translated them into understandable paragraphs for readers who are new in this area of study.
Generally, the author just provided data and evaluated that briefly, but did not include demonstrating the effects of airline companies (based on model) and not put forward solutions and recommendations that how the managers can solve. Another limitation that arises with the research in this article is due to the fact that 12 companies were deleted from the original list of 50 companies. With this regard, the authors did not consider how this action affected the results of the study. It is possible that deleting these companies had a substantial negative impact on the results. The study did not provide readers with a reason why the researchers found the financial information in these companies as inadequate and how such gaps can be addresses in future. Future researchers can improve by ensuring that all sample data is checked thoroughly and any changes or modifications are analysed to find the impacts to the study.
The conclusion points for this discussion were that the function of the DEA in this set-up is to identify efficiency frontiers, inefficient slacks and benchmarking parameters. Through the integration of 7 input and 4 output parameters from BSC to DEA, the results of the study were more clear and meaningful (Wann-Yih & Ying-Kai, 2013). From the standpoint of the DEA, it is clear that it may accommodate leading & lagging variables and the standpoint of the BSC is that it can evaluate DMU performance via quantitative comparison between the inefficient & efficient DMUs. The study concludes that since no other studies have previously integrated BSC and DEA in a bid to assess the operational efficiency of the airline industry, then this research sets a baseline for validation through future inquiries (Niven, 2005).
We think that these conclusions have achieved the intentions of the study – the author provides four detailed conclusion points that can be adopted by readers and especially professionals in the airline industry. The most important consideration here is that the DEA-BSC model has provided a baseline for further validation through academic studies. The importance here arises from the fact that no other studies have attempted to integrate BSC and DEA in assessing the operational efficiency of firms in the airline industry. In this area, the article was good and beneficial to readers. By providing a baseline study, we strongly believe that this study provides an important contribution to this area of study since future researchers can now build on this study for further validation and application of this model (Osman, 2013).
The article had several limitations in different sections that can be improved. In the research section, there is a limited literature review. There is also no explanation why 12 companies were not included in the study and how this affected the results. Another limitation is due to the fact that the canonical correlation analyses were introduced to test the interrelationship among four perspectives of the BSC. The canonical analyses have not been explained how to be conducted that would confuse the audience, and the reader is not told why these analyses are relevant to the purpose of the paper. The results section has limitations due to the unexplained impacts of deleting 12 companies. Also, the author just provided data and evaluated this data briefly, but did demonstrate the effects of airline companies (based on model) and did not put forward solutions and recommendations that how the managers can solve these issues. There were also problems in this section due to technical terms and ease of understanding of the content by readers. This can be resolved by a thorough evaluation of data, simplification of language and terms for easier understanding.
On the other hand, the conclusion may have been improved in several ways. Even though it provided an analysis of the main points, it could have been simplified for readers with limited knowledge in this area of study. Also, the authors should have presented some references in this section for readers to check technical terms. Also, the authors should have provided important areas of further study to spark the interest and attention of future researchers (Margaret & Patrick, 2011). We propose that this section could have been simplified (in terms of language), references presented and areas of further study enumerated. Overall, we think that this article is good and it presents viable information but its sections could have been improved in several ways as we have proposed in the different sections of the paper.
Example 2: ‘Balanced Scorecard financial measurement of organizational performance: A review’
This section provides some insights and suggestions after comparing this article (example 1) with a similar one (example 2). The selected article was chosen since it was about measuring performance using the BSC approach. The article, example 2, was selected since it is about BSC and it talks about performance measurement. In fact, this article is the complete opposite of the one being reviewed since it takes a different approach to finding conclusions. There is a short and clear abstract, simple introduction and deep analysis of current articles which is lacking in the work of (Wann-Yih & Ying-Kai, 2013).
The ‘example 2’ article uses extensive literature review rather than actual data from companies or surveys. The article used secondary sources of data and a conceptual framework but it presents clear and concise results (Malgwi & Dahiru, 2014). In fact, the results are itemized in a clear way such that the readers can understand them. This article does not require readers to have prior knowledge of BSC since the concept is clearly explained and evaluated. We think that the article under review should have taken a similar approach in ensuring thorough research on the issues at hand instead of delving straight into the calculations or technical considerations. We find that this article both appealing to readers with prior understanding and those who are new to this area of study. The authors of the example 1 paper under review can learn a lot in terms of style and research from the work of (Malgwi & Dahiru, 2014).
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Belcher, W. L. (2009). Writing Your Journal Article in Twelve Weeks: A Guide to Academic Publishing Success. SAGE Publications.
Kumar, R. (2010). Research Methodology: A Step-by-Step Guide for Beginners. SAGE.
Luca, Q., & Stefano, T. (2010). Performance Measurement: Linking Balanced Scorecard to Business Intelligence. Springer Science & Business Media.
Malgwi, & Dahiru. (2014). Balanced Scorecard financial measurement of organizational performance: A review. IOSR Journal of Economics and Finance (IOSR-JEF), 4(6), 1-10.
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Taticchi, P. (2010). Business Performance Measurement and Management: New Contexts, Themes and Challenges. Springer Science & Business Media.
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Wann-Yih, W., & Ying-Kai, L. (2013). A balanced scorecard envelopment approach to assess airlines’ performance. Industrial Management & Data Systems, 114(1), 123 – 143.
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