Team diversity and performance: evidence from international sport leagues

Assessing the impact of the cultural heterogeneity of teams of international sports leagues on team performance. A survey of 8 regular hockey seasons and 6 regular basketball seasons. The main metric of heterogeneity is the Herfindahl-Hirschm.

Рубрика Спорт и туризм
Вид дипломная работа
Язык английский
Дата добавления 07.12.2019
Размер файла 364,5 K

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The included control variables turn to be insignificant in the both (6) and (7) models. Hence, we insert interaction term between the budget and diversity and re-estimate the models. Interaction term is included since it is assumed that higher diversity and the budget higher than average simultaneously might affect team performance.

According to the Table 11, interaction term between the dummy for budget higher than mean and hhi_nat is insignificant, the same results obtained for the (7) model with the interaction term.

Table 10

Regression analysis output for the hockey sample

Model (6) hhi_nat is metric

Model (7) n_nat is metric

Coefficient

P-value

Coefficient

P-value

hhi_nat

-0.3781**

0.030

n_nat

0.0324**

0.022

age

0.0144

0.204

0.0151

0.182

height

0.1076

0.274

0.0097

0.326

weight

-0.0194

0.121

-0.0164

0.193

d_coach_nat

-0.0429

0.299

-0.0458

0.268

budget_mln

0.0002

0.169

0.0002

0.167

C

0.9161

0.465

0.4602

0.710

Rwithin2 0.0618

Rbetween2 0.3151

Roverall2 0.2799

Rwithin2 0.0649

Rbetween2 0.2416

Roverall2 0.2668

*** p < 0.01, ** p < 0.05, * p < 0.1

Table 11

Regression analysis output with interaction term for the hockey sample

Model (6) hhi_nat is metric

Model (7) n_nat is metric

Coefficient

P-value

Coefficient

P-value

hhi_nat

-0.5017**

0.019

n_nat

0.0226

0.199

age

0.0145

0.204

0.0136

0.236

height

0.0089

0.274

0.0085

0.392

weight

-0.078

0.121

-0.0149

0.240

d_coach_nat

-0.0448

0.299

-0.0542

0.194

top_budget

-0.0858

0.169

-0.0710

0.704

top_budget*hhi_nat

0.3726

0.266

0.0190

0.443

C

1.2279

0.327

0.7586

0.540

Rwithin2 0.0616

Rbetween2 0.2291

Roverall2 0.2267

Rwithin2 0.0599

Rbetween2 0.0001

Roverall2 0.0268

*** p < 0.01, ** p < 0.05, * p < 0.1

Then we should test the robustness of obtained coefficients, so we include squared term of the explanatory variable and re-estimate the equations (6) and (7).

Table 12

Robustness check for the hockey sample

Model (6) hhi_nat is metric

Model (7) n_nat is metric

Coefficient

P-value

Coefficient

P-value

hhi_nat

-0.2747

0.772

sqr_hhi_nat

-0.0901

0.912

n_nat

0.0578

0.292

sqr_n_nat

-0.0023

0.632

age

0.0143

0.208

0.0153

0.177

height

0.0107

0.276

0.0096

0.333

weight

-0.0193

0.124

-0.0163

0.196

d_coach_nat

-0.0428

0.302

-0.0455

0.273

budget_mln

0.0002

0.169

0.0002

0.161

C

0.8859

0.461

0.4012

0.747

Rwithin2 0.0619

Rbetween2 0.3362

Roverall2 0.2857

Rwithin2 0.0661

Rbetween2 0.3175

Roverall2 0.2959

*** p < 0.01, ** p < 0.05, * p < 0.1

So, the regression models (6) and (7) are robust since explanatory variables turns insignificant when squared term is inserted.

3.2 The regression model results for the regular season in basketball

According to the methodology described in the Section 2, we re-estimate the models (6) and (7) for the basketball sample.

As it is evident from the Table 13, hhi_nat is statistically significant on the 5% significance level. The interpretation is as follows: if hhi_nat increases by 1%, scores per game will fall by (-9.7088).

As for the Model (7) the explanatory variable n_nat is insignificant in its impact to the scores per game.

Table 13

Regression analysis output for the basketball sample

Model (6) hhi_nat is metric

Model (7) n_nat is metric

Coefficient

P-value

Coefficient

P-value

hhi_nat

-9.7088**

0.038

n_nat

0.6780

0.201

age

-0.1846

0.727

-0.1316

0.808

height

-0.2141

0.311

-0.2319

0.285

weight

-0.2400

0.234

-0.1894

0.352

d_coach_nat

-1.6551

0.214

-1.9844

0.148

budget_mln

0.0114***

0.000

0.0109***

0.001

C

148.7375***

0.001

139.9079***

0.001

Rwithin2 0.3001

Rbetween2 0.2345

Roverall2 0.3039

Rwithin2 0.2709

Rbetween2 0.2382

Roverall2 0.2952

*** p < 0.01, ** p < 0.05, * p < 0.1

To be consistent, we employ the interaction term and re-estimate the equations (6) and (7). The regression output is presented below (Table 14).

According to the Table 14, inserted interaction term is statistically insignificant in its impact to the dependent variable for the both models (6)-(7).

Table 14

Regression analysis output with interaction term for the basketball sample

Model (6) hhi_nat is metric

Model (7) n_nat is metric

Coefficient

P-value

Coefficient

P-value

hhi_nat

-6.534648

0.258

n_nat

-0.1341

0.863

age

0.5046

0.386

0.5463

0.331

height

-0.0365

0.871

-0.0413

0.856

weight

-0.5289**

0.011

-0.4013*

0.052

d_coach_nat

-2.4280*

0.099

-2.6289*

0.075

top_budget

6.3821

0.209

-5.2431

0.400

top_budget*hhi_nat

-9.8271

0.425

1.8514

0.120

C

128.0779***

0.006

0.7586**

0.015

Rwithin2 0.1762

Rbetween2 0.1861

Roverall2 0.1941

Rwithin2 0.1793

Rbetween2 0.1225

Roverall2 0.1534

*** p < 0.01, ** p < 0.05, * p < 0.1

According to the proposed methodology, the effect of diversity in basketball is measured at two level - aggregated and disaggregated. So, the regression output of disaggregated has been presented above (Table 13).

The equations (8) and (9) are also estimated, the results are reported in the Table 15. These equations are also considered to be a part of the robustness check.

Table 15

Regression analysis output for the basketball sample

Model (8) hhi_lan is metric

Model (9) n_lan is metric

Coefficient

P-value

Coefficient

P-value

hhi_lan

-8.5255*

0.055

n_lan

0.8916

0.133

age

-0.3956

0.454

-0.3081

0.563

height

-0.2376

0.268

-0.2461

0.256

weight

-0.2220

0.281

-0.1669

0.417

d_coach_lan

0.7499

0.561

-0.8971

0.493

budget_mln

0.0124***

0.000

0.0122***

0.000

C

155.2603***

0.000

142.5935***

0.001

Rwithin2 0.2784

Rbetween2 0.2973

Roverall2 0.3191

Rwithin2 0.2626

Rbetween2 0.2760

Roverall2 0.3063

*** p < 0.01, ** p < 0.05, * p < 0.1

As it can been seen from the Table 15, hhi_lan is significant on the 10% significance level. The explanation of the obtained results is as follows: if hhi_lan grows by 0.01 ceteris paribus, team performance will decline by (-8.5255). However, the number of languages (aggregated level) turns to be insignificant.

Hence, both models (8) and (9) are robust, since the estimation output does not change when another diversity metric is employed.

As the additional robustness check, we re-estimate the equations (8)-(9) with inserted quadratic term.

Table 16

Robustness check for the basketball sample

Model (8) hhi_nat is metric

Model (9) n_nat is metric

Coefficient

P-value

Coefficient

P-value

hhi_nat

-34.3062*

0.053

sqr_hhi_nat

23.42055

0.148

n_lan

-2.1168

0.376

sqr_n_nat

0.3508

0.232

age

-0.0125

0.981

-0.1255

0.816

height

-0.2297

0.274

-0.2478

0.253

weight

-0.2556

0.202

-0.1597

0.434

d_coach_nat

-1.4636

0.270

-1.9016

0.164

budget_mln

0.0116***

0.000

0.0107***

0.001

C

154.3005***

0.000

145.2338***

0.001

Rwithin2 0.3224

Rbetween2 0.2226

Roverall2 0.2984

Rwithin2 0.2869

Rbetween2 0.2311

Roverall2 0.2965

*** p < 0.01, ** p < 0.05, * p < 0.1

So, the Models (8) and (9) can be considered as robust ones as they have been successfully tested. So, we can rely on the obtained regression models inferences.

To summarize, hhi_nat is statistically significant in its impact to the scores per game in both hockey and basketball. Moreover, the alternative diversity metrics such as n_nat turns to be significant solely in hockey, but its effect is insignificant. The additional quadratic specification confirms the regression models output so that the obtained results are considered to be reliable.

Concluding discussion

In this part of the paper it is reasonable to return to the stated aim of the research. So, the study aims at evaluating the impact of diversity of teams of the international sports leagues on the team performance and then compare the obtained output. As a result, the effects of diversity which is measured through Herfindahl-Hirschman index and Number of nationalities using the database on hockey (particularly, the Continental Hockey League) and on basketball (specifically, the VTB United League) have been estimated for both samples and then the obtained inferences have been compared.

As a consequence, the Hypothesis 1 has been confirmed: cultural team diversity does matter in the team performance in both considered sports. Looking at the results in more detail, diversity positively affects team performance in both sports. In general, the obtained results for hockey are consistent with the results of Kahane et. al (2013); however, the inferences for the basketball sample contradict the results of Timmerman (2000).

The Hypothesis 2 is also confirmed: the effects for hockey and for basketball are different. The finding can be interpreted as follows: the effects differ because of the dissimilar degree of interaction between players during the game due to the specifics of the sport. Besides, in the KHL the wage ceiling is set; while in the VTB United League the players' earnings are not limited, according to the Leagues's policy. In addition, due to the specifics of the sports the incurred costs and received benefits can be different. So, as it has been suggested by Lazear (1999), the integration and transaction costs incurred due to the diversity increase might outweight the benefits of diversity; however, this statement is not confirmed in the current paper due to the imposed legionnaires' quotas that regulate the number of foreigners.

The difference in the effects of diversity in sports under consideration can be also explained with the different size of the legionnaires' quotas (the limit is stricter for hockey). When the Leagues' Management put restrictions on the permitted number of legionnaires, the clubs tend to hire high-quality foreign players who demonstrate the highest level of utility; that's why foreign sportsmen amplify the whole team results. Beside increasing team performance through the individual efficiency, the legionnaires are supposed to introduce innovative game strategies and show advanced style of playing.

The main finding of the current paper is that we have proved that cultural team diversity is an essential factor of boosting team performance. As team sport can be regarded as an international business, the obtained inferences might be transferred to the real business sector. So, the entity which aims at enhancing its financial performance should engage high-skilled foreign employees. However, the entity's management should sensibly hire the foreigners since the incurred costs might surpass potential benefits of diversity as it is stated by Lazear (1999).

Moving to the prospects for the further research, it would be extremely valuable for the firm interest to put forward the performance maximization staffing formula which focuses on the native/foreign staff share. Probably, it is possible if employ the data on sport without legionnaires' quotas.

Current research includes some limitations. Firstly, the club budgets in the KHL are not disclosed that's why the missing data has been filled according to our own assumptions; it might be the reason for the statistical insignificance of the team budget control variable in the model for hockey. Secondly, another diversity metrics of diversity (for example, the gini coefficient) can be employed which would influence the inferences; similarly, performance might be measured from another angle therefore the outcome could differ from the obtained results.

References

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