BUSINESS PROCESS MODELING

Business Process Modeling and Business Performance in the Mobile Industry

Background

Before starting the analysis process, it is necessary to give a brief description of the dependent variable and how it will be employed in the analysis in later stages. Mobile subscriber's retention behaviour as a dependent variable is used to check whether they have reached a predetermined decision to renew with or switch their mobile operators. The sample participants? possibility of repeat purchasing is used as a dependent variable that needs to be studied with respect to the two pre-behaviour and four post-behaviour predictors that affect subscribers in the retention situation. According to actual customer behaviour, current customer-supplier relationship, and mutual interaction, the repeat purchase question aims to check whether a customer is planning to renew his or her mobile service subscription or contract with his/her current mobile supplier or not. Based on different contextual and relational factors, a customer reaches a decision to renew with or switch his/her current mobile supplier when he or she compares, within the retention circumstances, what the current supplier is providing with what other mobile suppliers are offering in price plans containing a variety of reinforcements and punishments.

Data Analysis and Discussion

Analysis of the data is consisted of two parts. The first is the extraction of principal components for principle components analysis. For this purpose the correlation matrix is first obtained for the original variables. Then the number of principal components is determined using Eigen values and communalities. Successfully extracted principal components are then used to reproduce the correlation matrix. In the second part, dimensionality is tested and canonical correlation analysis is performed because the associations might have multiple dimensions.

The following table gives the correlation between the original variables. Obtaining the correlation matrix of original variables is appropriate prior to the principle component analysis. One reason is that very highly correlated variables needed to be removed from the analysis, by very high here it means above 0.9. When two variables have this much high correlation, the two variables seem to be measuring same thing. Such variables can be combined by various methods like taking average. For very low values of correlation between two or more variables, here by low value it means less than 0.1, these variables load only onto one principle component. In simpler words, variables with very low value of correlation make their own principle component. This is not appropriate as principle component analysis aims at reducing the number of variables. The following table depicts that no pair of variables have correlation above 0.9 hence each variable is supposed to be measuring different aspects. The variables for which value of correlation coefficient is less than 0.1 are not to be included in a component and are incorporated in the model as separate variables.

Table 1: Correlation Matrix of Original Variables

SQ1

SQ2

SQ3

SQ4

SQ5

SQ6

SQ7

CS1

CS2

CS3

CS4

BPC1

BPC2

BPC3

BPC4

BPT1

BPT2

BPT3

BPM1

BPM2

BPM3

BPM4

BPM5

BPM6

BPM7

SQ1

1.000

.839

.682

.704

.648

.081

.487

.551

.530

.555

.525

.405

.355

.361

.434

.474

.433

.486

.383

.219

.351

.353

.470

.338

.375

SQ2

.839

1.000

.756

.706

.625

.153

.449

.532

.511

.474

.485

.356

.316

.319

.343

.431

.403

.477

.388

.207

.401

.403

.489

.365

.442

SQ3

.682

.756

1.000

.702

.616

.239

.441

.498

.457

.460

.419

.314

.231

.281

.369

.347

.318

.446

.403

.236

.394

.396

.437

.292

.386

SQ4

.704

.706

.702

1.000

.787

.242

.557

.513

.535

.508

.445

.374

.279

.343

.453

.371

.325

.391

.411

.189

.421

.412

.469

.299

.364

SQ5

.648

.625

.616

.787

1.000

.345

.681

.597

.595

.591

.507

.411

.345

.385

.408

.369

.338

.400

.478

.251

.488

.499

.530

.282

.313

SQ6

.081

.153

.239

.242

.345

1.000

.412

.248

.199

.201

.100

.001

.049

.154

.049

.044

.025

.031

.132

.193

.182

.205

.245

.053

.211

SQ7

.487

.449

.441

.557

.681

.412

1.000

.615

.593

.483

.511

.452

.395

.480

.446

.490

.454

.428

.438

.278

.398

.389

.479

.200

.326

CS1

.551

.532

.498

.513

.597

.248

.615

1.000

.899

.706

.630

.424

.398

.372

.444

.509

.467

.433

.416

.179

.542

.553

.502

.117

.281

CS2

.530

.511

.457

.535

.595

.199

.593

.899

1.000

.713

.617

.442

.414

.364

.452

.519

.466

.391

.444

.183

.563

.562

.532

.109

.239

CS3

.555

.474

.460

.508

.591

.201

.483

.706

.713

1.000

.679

.387

.395

.339

.483

.511

.427

.353

.502

.269

.501

.501

.475

.195

.379

CS4

.525

.485

.419

.445

.507

.100

.511

.630

.617

.679

1.000

.312

.265

.342

.327

.446

.417

.419

.391

.292

.496

.497

.502

.149

.364

BPC1

.405

.356

.314

.374

.411

.001

.452

.424

.442

.387

.312

1.000

.881

.696

.610

.485

.484

.350

.360

.213

.332

.319

.413

.168

.254

BPC2

.355

.316

.231

.279

.345

.049

.395

.398

.414

.395

.265

.881

1.000

.744

.641

.461

.441

.302

.345

.203

.342

.329

.396

.120

.221

BPC3

.361

.319

.281

.343

.385

.154

.480

.372

.364

.339

.342

.696

.744

1.000

.685

.435

.433

.340

.348

.236

.326

.328

.431

.062

.241

BPC4

.434

.343

.369

.453

.408

.049

.446

.444

.452

.483

.327

.610

.641

.685

1.000

.520

.543

.387

.435

.241

.412

.389

.456

.164

.268

BPT1

.474

.431

.347

.371

.369

.044

.490

.509

.519

.511

.446

.485

.461

.435

.520

1.000

.868

.630

.474

.289

.410

.398

.513

.262

.406

BPT2

.433

.403

.318

.325

.338

.025

.454

.467

.466

.427

.417

.484

.441

.433

.543

.868

1.000

.687

.440

.245

.387

.376

.509

.224

.399

BPT3

.486

.477

.446

.391

.400

.031

.428

.433

.391

.353

.419

.350

.302

.340

.387

.630

.687

1.000

.412

.297

.303

.304

.400

.289

.436

BPM1

.383

.388

.403

.411

.478

.132

.438

.416

.444

.502

.391

.360

.345

.348

.435

.474

.440

.412

1.000

.525

.454

.448

.524

.225

.438

BPM2

.219

.207

.236

.189

.251

.193

.278

.179

.183

.269

.292

.213

.203

.236

.241

.289

.245

.297

.525

1.000

.335

.347

.422

.301

.382

BPM3

.351

.401

.394

.421

.488

.182

.398

.542

.563

.501

.496

.332

.342

.326

.412

.410

.387

.303

.454

.335

1.000

.993

.731

-.001

.281

BPM4

.353

.403

.396

.412

.499

.205

.389

.553

.562

.501

.497

.319

.329

.328

.389

.398

.376

.304

.448

.347

.993

1.000

.738

.002

.286

BPM5

.470

.489

.437

.469

.530

.245

.479

.502

.532

.475

.502

.413

.396

.431

.456

.513

.509

.400

.524

.422

.731

.738

1.000

.247

.353

BPM6

.338

.365

.292

.299

.282

.053

.200

.117

.109

.195

.149

.168

.120

.062

.164

.262

.224

.289

.225

.301

-.001

.002

.247

1.000

.491

BPM7

.375

.442

.386

.364

.313

.211

.326

.281

.239

.379

.364

.254

.221

.241

.268

.406

.399

.436

.438

.382

.281

.286

.353

.491

1.000

Table 2: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.879

Bartlett's Test of Sphericity

Approx. Chi-Square

2925.662

df

300

Sig.

.000



In the above table, value Kaiser-Meyer-Olkin Measure of Sampling Adequacy varies between 0 and ...