The "industrial halo" and its impact on the assessment of corporate reputation

Calvo-Iriarte, Emilio; Esteban, María Victoria; Rodríguez Castellanos, Arturo Article The "industrial halo" and its impact on the assessment of corporate reputation European Journal of Management and Business Economics (EJM&BE) Provided in Cooperation with: European Academy of Management and Business Economics (AEDEM), Vigo (Pontevedra) Suggested Citation: Calvo-Iriarte, Emilio; Esteban, María Victoria; Rodríguez Castellanos, Arturo (2024) : The "industrial halo" and its impact on the assessment of corporate reputation, European Journal of Management and Business Economics (EJM&BE), ISSN 2444-8451, Emerald, Leeds, Vol. 33, Iss. 2, pp. 237-252, https://doi.org/10.1108/EJMBE-02-2022-0028 This Version is available at: https://hdl.handle.net/10419/325567 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ The “industrial halo”and its impact on the assessment of corporate reputation Emilio Calvo-Iriarte Doctoral School, University of the Basque Country UPV∕EHU, Bilbao, Spain Mar ıa Victoria Esteban-Gonz alez Department of Quantitative Methods, Facultad de Econom ıa y Empresa, University of the Basque Country UPV∕EHU, Bilbao, Spain, and Arturo Rodr ıguez-Castellanos Doctoral School, University of the Basque Country UPV∕EHU, Bilbao, Spain Abstract Purpose –The gap that this research attempts to fill is to analyse the explanatory factor “industry”when assessing the reputation ofa corporate group. In other words, this research attemptsto demonstrate the impact of the “industrial halo”on the assessment of corporate reputation, given that, to date, the academic literature has not considered industry as an explanatory variable in the assessment of the reputation of private companies. Design/methodology/approach –A sample of 43 Spanish companies was used to analyse the relationship between the reputation of firms as measured by the Merco Empresas index, and the industries to which they belong, after controlling for company performance, size, turnover, public recognition of their leadership, and corporate responsibility. This involved conducting a cross-sectional analysis of the relationship between the variables for each year in the time period from 2005 to 2016. The available data were taken from the firms’ annual financial reports and websites, as well as from the Merco. Findings –The paper shows the existence of industrial halos that account for the corporate reputation of businesses in Spain. It is also shown that industrial halos are not permanent over time, and that they tend to occur in years of crisis. Research limitations/implications –It would have been desirable for this study to have had sufficient data to include other industries, but this was not possible. As for possible extensions, in addition to expanding the period considered, other analytical techniques, such as panel data models, could be applied to allow comparison with the results obtained here. Practical and social implications –The results of this study have some practical implications. Firstly, firms that publish corporate reputation rankings should be aware of the distortion that the industrial halo can produce, especially in times of uncertainty, and seek to correct for it in their measurements. And secondly, corporate groups themselves should assume that the reputation of the industry affects their individual reputation, and consequently, they should see the other companies in the industry not only as competitors but also as “reputational allies”. They should therefore make collective efforts to improve in this respect, especially in the face of reputational crises. Originality/value –This paper provides a better understanding of the relationship between the reputation of a company and the industry to which it belongs, and of its permanence over time. This relationship has been little studied in the Spanish market to date. Keywords Reputation, Reputational risk, Stakeholders, Reputation measurement, Industry, Industrial halo Paper type Research paper “Industrial halo”and corporate reputation 237 © Emilio Calvo-Iriarte, Mar ıa Victoria Esteban-Gonz alez and Arturo Rodr ıguez-Castellanos. Published in European Journal of Management and Business Economics. 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://creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2444-8494.htm Received 2 February 2022 Revised 5 February 2023 Accepted 6 March 2023 European Journal of Management and Business Economics Vol. 33 No. 2, 2024 pp. 237-252 Emerald Publishing Limited e-ISSN: 2444-8494 p-ISSN: 2444-8451 DOI 10.1108/EJMBE-02-2022-0028 1. Introduction The advent of globalisation in recent decades has made corporate risks increasingly evident. The need to make investments profitable, reduce production costs and capture new markets has led to a continuous growth in the size of business organisations, a logical reaction to the new perception of the planet as a vast open market. However, deregulation and increased demand volatility mean that companies are exposed to greater risks in the course of their business. Indeed, globalisation has generated a “complex”, increasingly interrelated world that (probably because of this) is also more uncertain and unpredictable (Rodr ıguez- Castellanos and San-Mart ın-Albizuri, 2020). This has clear repercussions for the business world. In addition, there have been rising, changing and more sophisticated demands by consumers/users, which have resulted in a substantial reduction of their traditional habits of brand loyalty, while also demanding responsible behaviour from the business world. However, the vast majority of the risks to which business is currently exposed have existed for many decades; the only difference is that new circumstances have now awakened some that were dormant, that had a small impact or a limited impact on certain business units. This is the case of “reputational risk”, i.e. the possibility that a company may lose its “reputation”or corporate prestige, or may see it significantly reduced, which can negatively affect the business. Reputation management has therefore become a decisive component of business strategy, and measuring it is essential in managing reputational risk. Nevertheless, this measurement may be distorted by cognitive biases that misrepresent the perception of stakeholders. These include belonging to certain industries, that is, the “industrial halo”, which we consider important for an accurate analysis of reputational perception. This type of halo has been little analysed in the academic literature to date. The aim of this study is to demonstrate the impact of the “industrial halo”on the valuation of corporate reputation. Therefore, the research focuses on whether this phenomenon occurred in Spanish companies during a broad period around the economic crisis that began in 2008– 2009, considering three sub-periods: pre-crisis (2005–2008), full crisis (2009–2012) and postcrisis (2013–2016). In line with this objective, the structure of this paper is as follows: after this introductory section, the conceptual framework is presented. The concepts of “reputation”and “industrial halo”are delimited, and the hypotheses to be tested are established. The methodology used is then described, including sampling and data used. This is followed by the presentation and discussion of the results, and the conclusions and limitations. The paper ends with the references section. 2. Conceptual framework The numerous definitions of corporate reputation (Dowling, 2016;Fombrun, 2012) indicate the difficulty involved in comprehensively capturing this concept. A well-known definition is that by Fombrun, “a corporate reputation is a collective assessment of a company’s attractiveness to a specific group of stakeholders relative to a reference group of companies with which the Company competes for resources”(2012, p. 100). As can be deduced from this definition, it is a latent, multidimensional, slow-burning concept, which is not directly observable and reflects the collective view of the company by certain actors, specifically, the stakeholders (Freeman, 1984). These peculiarities will be discussed in detail below. Firstly, it is a collective assessment carried out by certain stakeholders who have different types of links with the company or organisation being assessed: consumers or users, EJMBE 33,2 238 employees, managers, financial analysts, competitors, financial journalists, opinion leaders, public regulators, etc. Secondly, the attractiveness that the company offers to these evaluators needs accounted for. This requires identifying the elements that determine what makes it attractive, something that refers to a range of economic/financial, social, employment-related, environmental, ethical and good governance attributes. These even include emotional factors, which therefore reflect the multidimensional nature of reputation. Moreover, the formation and strengthening of corporate reputation require a long period of time to establish the perceptions that shape it; it does not depend on one-off promotional activities. Finally, this assessment has a clear comparative purpose, as it takes as a benchmark the group of companies competing with the one being evaluated within a given industry or number of industries. Academic research has shown the effect of corporate reputation on the value creation of companies, both internationally (Vig et al.,2017;Roberts and Dowling, 2002;Dunbar and Schwalbach, 2000), and in Spain (Fern andez-S anchez et al.,2015;De Quevedo-Puente, 2003). This makes it necessary to control the procedures for measuring it, to try to ensure an assessment that is bias-free and allows for correct strategic decisions to be made. Biases may include a cognitive distortion in the perceptions of stakeholders: the industrial halo. The term halo effect was first coined by Thorndike (1920). It can be described as a cognitive distortion or bias that causes the unconscious generalisation to the whole of a particular (positive or negative) trait of the subject being analysed. In short, it is an illogical generalisation that is usually accompanied by insufficient information on the subject matter. Thorndike’s conclusion has been confirmed in subsequent decades by a large body of research, both in the field of psychology (Keeley et al., 2013;Kahneman, 2011) and in the area of corporate behaviour and performance. In the field of business, different types of halos have been identified, both in relation to individual decisions and to the company as a whole (Thaler, 2015;Rosenzweig, 2007), as well as to various specific aspects, such as commercial (especially brand image) (Leuthesser et al., 1995), corporate social responsibility (Moliner et al., 2019), the company’s country of origin ( Sapi cet al., 2018) and the company’s corporate reputation (De Quevedo-Puente, 2003). These references point to the existence of various typologies of business ’halos’, such as financial, managerial, marketing and product’s country of origin. Ithasalsobeenarguedthattheremaybean“industrial halo”in the perception of corporate reputation by stakeholders, in the sense that corporate reputation may be strongly influenced by the industry to which the assessed firm belongs. This would therefore involve a distortion of corporate reputational assessments in the form of a negative or positive bias about a company’s reputation based on its membership of certain industries. Ultimately, there seems to be a transfer of the reputation of the industry to the firms that make up the industry. This “halo”could be expected to be stronger in circumstances where there is less information or greater uncertainty (economic crises, for example), or in groups with restricted access to information (such as the general public). It could also have a stronger impact when “industrial”reputation is particularly favourable or unfavourable. The effect that belonging to a certain industry has on corporate reputation assessment and strategies has attracted the interest of a number of researchers (Melo and Garrido- Morgado, 2012;Rouviere and Soubeyran, 2011;Susaeta et al., 2008;Csiszar and Heidrich, 2006). Moreover, reputation assessment bodies such as the Reputation Institute and the Edelman Group take business industries into consideration in their analyses. The interest of considering the “industrial halo”in the evaluation of corporate reputation lies in the views discussed above and in two additional aspects. Although previous academic research has shown some interest in the effect that belonging to a certain industry may have “Industrial halo”and corporate reputation 239 on the evaluation of corporate reputation, this interest has so far been rather limited. Additionally, it should be borne in mind that part of the sample in reputational assessment procedures based on surveys usually includes non-specialists [1]. They are particularly exposed to a possible “industrial halo”that may distort their perceptions, as a result of prejudices due to ignorance or to limited or even biased external information received. Based on the above considerations, the following hypothesis is proposed: H1. The reputation of Spanish firms had an “industrial halo”, i.e. their reputation was influenced by the industry to which the firm belongs. Moreover, it seems reasonable to expect that this “industrial halo”will be maintained over time. This assumption is corroborated by the fact that public satisfaction with the different industries seems to change only slightly over time, as shown by data on consumer satisfaction indices across various industries over time in the USA, for example [2]. It can therefore be inferred that reputational perception with respect to the different industries will not vary excessively over time. This second hypothesis is also therefore proposed: H2. The “industrial halo”effect on the reputation of Spanish firms remains over time. 3. Methodology 3.1 Model To test these hypotheses, we relied on the specification and estimation of a model for the evaluation of corporate reputation. We used cross-section regression and industries as explanatory variables to test whether (or not) industry membership is significant for the reputation of companies. The “halo”is measured by analysing the statistical significance of the explanatory variable “industry”. A series of control variables were also used, including profitability, size, turnover growth, leadership and corporate responsibility. The following specification for corporate reputation is proposed: RPit ¼ α 1þ α 2RoAit−1þ α 3FSit þ α 4SGit þ α 5LEAit þ α 6CRit þΣjβjINDji þuit i¼1;...;N;t¼1;...;T;j¼1;...;JðÞ(1) where RP it is the reputation of firm iin period t;RoA t1 is the ratio between the income of firm iand its Total Assets in t1; FS it is the size of iin t;SG it is the annual sales growth rate of iin t; LEA it is the leadership of iin t;CR it is the corporate responsibility for iin t; and IND ji , a binary independent variable (dummy variable) is industry jto which company ibelongs. The choice of variables (both of the variable to be explained and of the control and explanatory variables) to be included in the specification of the model requires some clarification. For the variable corporate reputation, the Naperian logarithm of the Merco Espa~ na index for the years 2005–2016, developed by An alisis & Investigaci on (2010, 2013, 2014, 2016) was used as a measurement tool. Regarding the control variables, considering firstly the Profitability of the company, oneperiod lagged RoA was used. This ratio, measured as Return on Total Assets, seems to be the most suitable profitability indicator compared to other profitability measures. Although earnings before interest and taxes are ordinarily used as the numerator for the calculation, in this case we have decided to use the “Annual Profit”reported in firms’financial statements, as we believe that it provides greater visibility of the “Return”magnitude for an uninformed audience. Several papers have explored the relationship between profitability and reputation, EJMBE 33,2 240 either as an explanatory variable or as a control variable, usually with a time lag. A positive relationship with reputation has been generally found (Musteen et al., 2010;Brammer et al., 2009;Brammer and Pavelin, 2006;Dunbar and Schwalbach, 2000), as financial return is presumed to be a reliable reference for the reputational perception of stakeholders. A similar relationship is therefore expected to be found here. The Size (FS) of the corporate groups obtained from their number of employees, i.e. their headcount at the end of each year, taking Naperian logarithms. We believe that this measure is more objective than the volume of assets, which is subject to accounting criteria, and is more stable than other measures such as turnover or stock market value. In studies on the determinants of corporate reputation, this variable is usually a control variable. In principle, firm size may have an ambiguous relationship to reputation, as at first sight there does not necessarily seem to be a clear relationship, either positive or negative, to reputation. However, if a significant relationship is found, it is often positive (Musteen et al., 2010;Dunbar and Schwalbach, 2000), which is also expected here. Sales growth (SG) was calculated as a rate of change, i.e. f it being the sale of iin t,F i. t 5(f it  f it1 )/f it1 . This choice was influenced by the difficulties in obtaining reliable data on aspects of reputational assessment such as the quality and effectiveness of the business offering, referring, among others, to innovation (in products, processes, marketing and organisation), an important aspect that has made it necessary to take this variable as a proxy for business quality and effectiveness. It has also been used by other authors (Musteen et al., 2010;Urra-Urbieta et al., 2009). A positive relationship with corporate reputation is expected. Considering the Leadership (LEA), the role and public image of an organisation’s leader are important elements in establishing corporate reputation. Their prominence as the visible head of business activities, their initiative in social responsibility actions, their prominent role in the media, their integrity and their capacity to respond to crises and anticipate change are taken into account by stakeholders in assessing the credibility and appreciation of an organisation. Several studies have found a positive and significant relationship of this variable to corporate reputation (Love et al., 2017;Urra-Urbieta et al., 2009), so a similar relationship is expected here. This study has taken the Naperian logarithm of the Merco L ıderes score as a measure. It also seems clear that the last variable, Corporate Responsibility (CR), contributes to companies’reputation. Social agents have demanded that the business world behaves in a way that, apart from economic sustainability, also seeks social, labour, environmental and ethical sustainability. A positive and significant relationship has been found between variousmeasuresofthisvariableandcorporatereputation(Quintana-Garc ıaet al., 2021; Melo and Garrido-Morgado, 2012;Brammer and Pavelin, 2006). We also expect a similar kind of relationship. The Naperian logarithm of the Merco L ıderes score has been taken as a measure. Regarding the independent variables, the different Industries (IND) have been reflected in the model by including binary dummy variables with a value of 0 or 1, depending on whether the corporate group considered belongs to the specific industry or not. Eleven industries were considered, the members of which are indicated below, although only ten appear in the regression, as one of them (Media), which is established as a benchmark, is represented by the independent term of the regression. Therefore, in equation (1) J510. 3.2 Geographical area, period and sample The geographical area was Spain. The analysis spans the period from 2005 until 2016 and it was divided into three subperiods of four years each, based on the Spanish economic situation as measured by the rate of change of the GDP: (1) 2005–2008, pre-crisis (2005: 3.7%; 2006: 4.1%; 2007: 3.6%; 2008: “Industrial halo”and corporate reputation 241 0.9%); (2) 2009–2012, crisis (2009: 3.8%; 2010: 0.2%; 2011: 0.8%; 2012: 3%); and (3) 2013–2016, end of crisis (2013: 1.4%; 2014: 1.4%; 2015: 3.8%; 2016: 3.0%). This study therefore covers a time span of interest, as it has allowed us to examine the behaviour of industrial halos before, during and after the economic crisis that began in 2008–2009. The reference group was made up of corporate groups operating in Spain that feature often enough in the Merco Empresas monitor ranking, classified into eleven industries. There were initially 43 corporations, but there were some changes over time. The corporate groups included in each industry are listed below. Any variations over time are indicated in brackets. IND 1 - Insurance: DKV Seguros - MAPFRE - Mutua Madrile~ na - Sanitas Seguros (featured in 2013–2016). IND 2 - Banking: BBVA-Bankia - Bankinter-CaixaBank - Popular-Sabadell - Santander- BANESTO (featured in 2013–2016). IND 3 - Construction: ACCIONA - ACS - FCC-Ferrovial - SACYR (not featured in 2009–2012 or 2013–2016). IND 4 - Department Stores: El Corte Ingl es - EROSKI - Inditex-Mango - Mercadona. IND 5 - Utilities: ENDESA - Gas Natural - Iberdrola - REE - Agbar (featured in 2013–2016) - Enag as (first featured in 2013–2016). IND 6 - Consumer Electronics: BSH Electrodom esticos - Prosegur. IND 7 - Hotels: NH Hoteles - Meli a Hoteles. IND 8 - Energy: CEPSA - Corporaci on Log ıstica de Hidrocarburos (CLH) - Repsol. IND 9 - Passenger Transport: Abertis - IAG Iberia - RENFE-Operadora. IND 10 - Engineering: Abengoa - GAMESA - Indra - Corporaci on Mondrag on (featured in 2013–2016) - T ecnicas Reunidas (first featured in 2013–2016). IND 11 - Media: Vocento - Grupo Prisa. Therefore, the sample size was 43 corporate groups in the 2005–2008 sub-period, 42 in 2009– 2012 (as Sacyr no longer featured in the ranking) and 40 for 2013–2016 (as Sanitas Seguros, Banesto, Aguas de Barcelona (Agbar) and Corporaci on Mondrag on no longer featured, and Enag as and T ecnicas Reunidas featured for the first time in that period). In terms of representativeness, as of 31 December 1986, the 26 listed groups in the sample represented 76% of the total Spanish stock market capitalisation. However, given the available source of reputational data, a bias towards high-volume firms is inevitable. 3.3 Data collection The three main sources of information were the Monitor Empresarial de Reputaci on Corporativa (Merco) (Corporate Reputation Business Monitor) (www.merco.info), the companies’annual financial statements (National Securities Market Commission, CNMV) and their corporate websites. 4. Results The model estimation results in equation (1) for each year are shown below. Tables 1,2and 3show the results for each year in the three sub-periods considered: pre-crisis (2005–2008), crisis (2009–2012) and post-crisis (2013–2016). Least squares estimation was used, with robust inference under heteroscedasticity where necessary [3] (see Table 3) (see Table 4). EJMBE 33,2 242 Year 2005 a 2006 2007 2008 Constant 0.2186 0.8641 0.5690 1.0309*** RoA t1 0.0137 0.0107 0.0065 0.0099* Firm Size 0.0635 0.0211 0.0032 0.0125 Sales Growth 0.0674 0.1315 0.0391 0.0155 Leadership 0.3648*** 0.5909*** 0.8035*** 0.7743*** Corporate Response 0.5170*** 0.3139*** 0.1492** 0.1120*** Insurance –––0.4282*** Banking –––0.2308** Construction –––0.1606* Department Stores 0.2517* ––0.3689*** Utilities –––0.3727*** Consumer Electronics –0.4419* –0.2999*** Hotels –0.5410** –0.3009*** Energy –––0.3893*** Passenger Transport 0.2946** ––0.3212*** Engineering ––0.2349* 0.3232*** Adjusted R 2 0.6944 0.8147 0.8917 0.9480 F-value all industries p: 0.124 p: 0.0983* p: 0.4411 p: 0.0036*** F-value all regressors p: 3.96e15*** p: 8.81e09*** p: 8.65e12*** p: 5.45e16*** Note(s): a indicates that the variance and covariance have been robustly corrected for heteroscedasticity in the regression using the White estimator F-value all industries shows the p-value for H 0 :β 1 5β 2 5...5β 10 50yF-value all regressors shows the p-value for the contrast for H 0 : α 2 5 α 3 5...5 α 6 5β 1 5β 2 5...5β 10 50 *, **, *** indicate significance at 10%, 5% y 1%, respectively Source(s): Table by authors Year 2009 2010 2011 a 2012 Constant 2.0804*** 0.4987 3.5216*** 1.1557* RoA t1 0.0104 0.0061 0.0036 0.0032 Firm Size 0.0344 0.0232 0.0559*** 0.0351 Sales Growth 0.0590 0.1550 0.0119 0.0290 Leadership 0.6424*** 0.4186*** 0.4888*** 0.1757 Corporate Response 0.1751*** 0.4741*** 0.0134 0.6452*** Insurance –0.3702*** 0.6321*** 0.2685* Banking –0.3072*** 0.2837*** – Construction –0.2592*** 0.2735*** – Department Stores –0.1622* 0.4293*** – Utilities –0.3458*** 0.5383*** 0.2345* Consumer Electronics –0.3181*** –– Hotels –0.5056*** 0.5409*** – Energy –0.3599*** 0.4556*** 0.3029** Passenger Transport –0.3953*** 0.3961*** – Engineering –0.3155*** 0.4191*** – Adjusted R 2 0.8675 0.9163 0.7741 0.8712 F-value all industries p: 0.7149 p: 0.0039*** p: 5.74 e10*** p: 0.2275 F-value all regressors p: 2.83 e10*** p: 8.83 e13*** p: 1.06 e16*** p: 1.99 e10*** Note(s): a indicates that the variance and covariance have been robustly corrected for heteroscedasticity in the regression using the White estimator F-value all industries shows the p-value for H 0 :β 1 5β 2 5...5β 10 50yF-value all regressors shows the p-value for the contrast for H 0 : α 2 5 α 3 5...5 α 6 5β 1 5β 2 5...5β 10 50 *, **, *** indicate significance at 10%, 5% y 1%, respectively Sourcee(s): Table by authors Table 1. Pre-crisis sub-period (2005–2008) N 543 Table 2. Crisis sub-period (2009–2012) N 542 “Industrial halo”and corporate reputation 243 In each table, the rows of the first column show the variables for which results are displayed, and the other columns display these results. The corrected coefficient of determination and the p-values associated with joint significance tests are shown for the industries and for all the model’s regressors. Only the estimated coefficients of the individually significant industries are shown, in order to avoid overloading of the tables. As can be seen in Table 1, the variables are jointly significant for all the years in the subperiod, as shown by the p-value associated with the corresponding F-test. However, the joint test for industry significance only rejects the null hypothesis for 2006 (at 10%) and 2008 (at 1%). It is remarkable that 2008, when the crisis began to manifest itself clearly, was also the year in which all industries showed a significant explanatory capacity of the corporate Year 2013 2014 2015 2016 Constant 1.9554** 0.9934 0.3656 2.0654*** RoA t1 0.0008 0.0030 0.0001 0.0009 Firm Size 0.0336 0.0442** 0.0347 0.0332* Sales Growth 0.0331 0.0014 0.3002* 0.0410 Leadership 0.1725 0.0586 0.1604 0.1311 Corporate Response 0.5600*** 0.7744*** 0.7642*** 0.5733*** Insurance 0.2287* ––0.2436** Banking –––0.2349*** Construction ––– – Department Stores –––0.1977** Utilities –––0.1763** Consumer Electronics ––– – Hotels 0.2781** ––0.2396** Energy 0.2670** ––0.2927*** Passengers Transport –––0.1865* Engineering ––– – Adjusted R 2 0.8192 0.8752 0.8794 0.9113 F-value all industries p: 0.2122 p: 0.8626 p: 0.6930 p: 0.0528* F-value all regressors p: 5.49e08*** p: 8.05e10*** p: 5.41e10*** p: 1.55 e11*** Note(s): F-value all industries shows the p-value for H0: β15β25...5β10 50yF-value all regressors shows the p-value for the contrast for H0: α 25 α 35...5 α 65β15β25...5β10 50 *, **, *** indicate significance at 10%, 5% y 1%, respectively Source(s): Table by authors Industry Years Total Insurance 2008–2010-2011–2012-2013–2016 6 Hotels 2006–2008-2010–2011-2013–2016 6 Energy 2008–2010-2011–2012-2013–2016 6 Utilities 2008–2010-2011–2012-2016 5 Passenger Transport 2005–2008-2010–2011-2016 5 Department Stores 2005–2008-2010–2011-2016 5 Banking 2008–2010-2011–2016 4 Engineering 2007–2008-2010–2011 4 Consumer Electronics 2006–2008-2010 3 Construction 2008–2010-2011 3 Note(s): The table shows the years for which the corresponding industry has been statistically significant Source(s): Table by authors Table 3. 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