Competition in the Smart Phone industry - Brand-Oriented and Price-Sensitive Strategies - Case Study Example

Competition in the Smart Phone industry: An analysis of Brand-oriented and Price-sensitive Strategies Declaration This report is original work and has not been presented to any other institution for any rewards. Signature…………………………… Date………………………… Letter of transmittal Dear Lecturer, Enclosed is a report on why smart phone manufacturers should consider being more price-sensitive in the future due to rising competition in the industry. The main recommendations are: Smart-phone companies should pay attention to the price sensitive customers as such as the brand oriented customers Smart phones manufacturers should keep up with new technology in the industry to keep off competition. New technological advancements should be included in the more affordable models. The research was conducted by the help of other students and colleagues in the university Regards Executive summary The study below involved market research involving various brands of smart phones. Competition among smart phone brands has increased over time and manufacturers have tried different strategies to gain more market share. Most consumers in the smart phone market are either brand- oriented or price-sensitive when it comes to the selection of a smart phone. Although major global brands like Apple and Samsung are experiencing high profits, their returns are expected to stagnate due to increased competition from other brands[Tay14]. This will make them consider their pricing strategies over time. Multiple regression and correlation analysis was conducted to determine the relationship between the variables. The findings show that manufacturers should keep close attention their pricing strategies while keeping up with the technological advancements that may be accommodated by affordable products. Although features like 4G connectivity, applications, home screen design, camera resolution and after sale services are relevant in explaining the brand an individual may choose in buying a phone, due to recent trends, affordable models are able to incorporate all this components. This will cause manufacturers to reconsider their strategies in acquiring market share. Contents Introduction 6 Statement of the problem 7 Objectives 8 Limitation of the study 8 Method 9 Findings 10 Conclusion 13 Recommendations 14 Appendix 15 Appendix 1 15 Appendix 2 18 Appendix 3 21 Appendix 4 22 Works Cited 26 Introduction Since mobile phones were introduced in the 1980’s, their use has increased at an alarming rate around the world. The smart phone sector has not penetrated the global market, and there is a large scope and opportunities for players to seize the future. The most popular operating systems in the world are Android, Windows Phone, iOS and Blackberry[Olu12]. The smart phone market over the years has become fractionalized. Customers who lie the "bottom of the pyramid" are looking for smart phones with a lower price while customers who are at the "top of the pyramid" are looking for more sophisticated smart phones[For13]. The transformation of the market is changing from a scenario where one company takes all the profit, but rather a scenario where many companies take most of the profits. Different players have entered the market tailoring their products to meet different sectors of the market[Tri14]. The smart phone market, like any other market, is suffering from two major threats, competition and saturation from alternative products. Each company has its financial position which determines its risk reward ration from the committed funds[For13]. According to the international data company, half of the smart phones sold in the world today are worth less than $100. Smart phone prices have become standardised, and manufacturers are trying to capture the budget end of the market. In developing markets, customers are becoming more prices sensitive and have less concern to the brand. Wiko, a French manufacturer which has been in the market for about three years now, claims to have attained ten percent of the French market and is expanding across Europe. Since consumers are benefiting from the rapid technology that is happening in the smart phone industry, cheap smart phones do not necessarily mean bad quality[BBC14]. There is a large possibility that smart phones are bound to get even “smarter”. The iPhone contributes to almost half the profits of apple. Google's android operating system is used by almost three-quarters of all smart phone users in the world[Mic14]. While Samsung manufactures a wide range of smart phones with different prices, competition from other brands might force them to lower their prices[BBC14]. The market has become way more competitive as compared to recent times and the two leaders Samsung and Apple are losing the market share to manufacturers in china and other parts of the world[Dan14]. In the fourth quarter of 2013, analysts predicted that the entire market rose by 33.7%. Approximately 290 million units were shipped across the world during the fourth quarter of the year[Lom14]. The shipment of the whole year reached almost 990 million units[Com14]. Samsung was the leading seller of smart phones with a market share of 32.3%, followed by Apple with 15.5% and Hawaii, LG Lenovo and others taking 5.1%, 4.8%, 4.6% and 37.7% respectively[Ray14]. Statement of the problem Competition within any market ends up creating recognisable distinct and repeating patterns. Market leaders will have to emerge, and the rest of the market is left for other players in the market. The success of any competitive market will vary[Rey13]. The smart phone market is extremely competitive at the moment. However, the industry is still growing, and this could mean that competition would have to increase[Mar14]. This presents a case to manufacturers of smart phones that have to either use branding technology to differentiate their products or lower their prices while still providing quality goods to meet both ends of the market. In 2012, half of the smart phones valued less than $80 had a processer faster than one gigahertz. These budget phones are also following the trend for larger screens that were earlier shown by expensive models. While India is seen as one of the untapped markets, affordability will be a key factor to access such markets. Since some phone models cost more than $500, they are perceived to be more a luxury and niche brand[BBC14]. To fight off competition, smart phone brands need to identify which strategy is best for getting a larger market share Objectives The main objective of this study is to determine if smart phone producers should become more brand-oriented as opposed to being more price- sensitive. To this end, we formulate the null hypothesis Ho: smart phone manufacturers should concentrate on being brand-oriented rather than being price sensitive H1: smart phone manufacturers should not concentrate on being brand-oriented rather than being price sensitive Limitation of the study The sample used to conduct the study only consisted of 55 respondents; this could not properly explain global trends in the industry. The respondents of the questionnaire also mainly consisted of students. This meant that some of the variables could have been skewed to a certain direction. Method The data that was collected consisted of information regarding age, occupation, brand of phone used and functions mostly used and considered when acquiring a Smartphone. Collecting the data was done using a questionnaire that consisted of two Likert scales. The first scale was used to represent functions of a smart phone 1=never, infrequently 2=, 3=only occasionally, 4=frequently, 5=very frequently. The Likert scale used to collect data on functions mostly used on a smart phone were 1=strongly agree, 2=agree, 3=uncertain 4=disagree 5=strongly disagree. Multiple regression was conducted to determine if the features considered in buying a smart phone showed any significance in the choice of smart phone. Correlation analysis was also conducted to determine the relationship of the independent variable (brand of smart phone used) with the features considered when buying a smart phone. Although the questionnaire that was analysed covered most parts of the survey, there are some important aspects that it dint cover, such as gender that could have been used to compare results between men and women. The questionnaire was also too long making the analysis a bit tiresome. Some questions could have been avoided to save on time. Findings Appendix 1 shows frequencies of the data in regards to the ones that own a smart phone, the brand that is mostly used, the length of time used while using the gadget, the age group and the occupation. From the frequencies, we can tell that 52 from the 55 respondents had reported having smart phones. From the respondents that had smart phones 17 had the Samsung model, 4 had the HTC, iPhone had 27, Sony Ericsson had 2, LG had 1, while other models were only 4. The respective percentages were 17%, 4%, 27%, 2%, and 1% with apple having the highest frequency, followed by Samsung, HTC, Sony Ericsson, LG and other models specifically. Observing the time the smart phone has been used by the user shows that most users have used those smart phones for about between 1 and two years followed by 2 and 3 years. This information could mean that in time consumers are bound to try out new products. Various brands have been in the market for years now, but the respondents in this data show that they have been using that phone for about 1 to two years. The data also collected shows that 69% of the people owning smart phones belonged to the age group of 22-25 years, and 26% of the respondents were between the ages of 18-21 years. 96% of the respondents were also students. Appendix two shows the Adjusted R-squared which means 43.9% of the independent variable (brand of smart phone used) can be explained by the independent variables (features considered when buying a smart phone). The results from the ANOVA table show that there is a significance level of 0.004 which is less than the p value of 0.005. This means that the regression model was significant. A closer inspection of the model showed that only 6 variables were significant in explaining the model. The variables that were significant were 4G connectivity, applications, synchronising with the computer, camera resolution, icloud, home screen design and format and after sale services. A closer look at the Beta standardised coefficients shows the contribution of the significant variables to the model. After sale services contributed most to the model with a beta value of .546 followed by the icloud with a beta value of .544 followed by camera resolution with .476 and 4 G connectivity, home screen design and format and applications respectively. From appendix 3 we can tell that there is significant correlation between the dependent variable (brand of the smart phone used) and the significant independent variables (4G connectivity, apps, icloud, screen definition, home screen design and format and after sale services). The Pearson correlation results show that 4G connectivity is weakly correlated to the dependent variable with a score of -.104. Applications variable on moderately negatively correlated to the dependent variable with a score of -0.326; icloud variable is also moderately negatively correlated with a score of -0.408. After sale services is weakly positively correlated with a score of 0.071 while screen definition and home screen design and format are moderately negatively correlated with scores of -.143 and-0.290 respectively. From appendix 4 the questionnaire included asking questions with regards to the most function used in a smart phone. The functions included Email, SMS, Music, News, applications, photo taking, phone calls, watching movies, document processing, synchronising with a computer and icloud services. Most modern smart phones are assumed to be able to perform most if not all of the functions above. According to Emails, 31% of respondents said that they used the email application on their smart phone while 29%, 22%, 16%, 2% said they used the emails on their phones Very frequently, only occasionally, infrequently and never respectively. For SMS, the percentages for respondents who said they use the function: infrequently, only occasionally, frequently, very frequently represented 3%, 10%, 23% and 19% respectively. To inquire about news, the respondents who said they used that function : never, infrequently, only occasionally, frequently and very frequently were 2%, 9%, 11%, 21% and 56%. For applications use, participants who stated that they use the function: infrequently, only occasionally, frequently and very frequently represented 2%, 5%, 13%, 35% respectively. For photo-taking, participants who stated that they use the function: infrequently, only occasionally, frequently and very frequently represented 2%, 4%, 42%, and 52% respectively. For watching movies, participants who stated that they use the function: never, infrequently, only occasionally, frequently and very frequently represented 16%, 9%,6%, 32%and 36% respectively. For document processing, participants who stated that they use the function: never, infrequently, only occasionally, frequently and very frequently represented 5%, 18%,20%, 25%and 27% respectively. For document processing, participants who stated that they use the function: never, infrequently, only occasionally, frequently and very frequently represented 5%, 18%,20%, 25%and 27% respectively. For icloud services, participants who stated that they use the function: never, infrequently, only occasionally, frequently and very frequently represented 26%, 15%,10%, 14%and 32.7% respectively. From the information from appendix 4 we can arrive at a couple of point’s manufacturers should note in order to maintain sales. Apart from icloud which is only available to iPhone users, all other basic functions that have been mentioned above are assumed to be usual components for an ordinary smart phone. Manufactures should at least make sure that all their promoted products should be able to handle all those functions. This is because most of the participants pointed out that their either frequently or very frequently use the functions above which include email, photo taking, SMS, music , news, applications, phone calls, photo taking, movies, document processing, synchronising with the computer and icloud functions From the findings, it is possible to conclude that the rapid growth of technology could see cheaper smart phone models having the significant models shown in the analysis. Since all the significant variables have been covered by most smart phone manufacturers who manufacture a wide range of products, it would be wise for manufacturers to improve these basic necessities such as 4G connectivity, application, icloud, camera resolution home screen design and format and after sale services, while also providing competitive prices. Smart phone operators should, therefore, concentrate on adjusting the prices of the phones while also paying attention to the model of the phone. We can, therefore, reject the null hypothesis and conclude that phone manufacturers should not concentrate being brand-oriented but should also consider being price sensitive. Conclusion The smart-phone industry still has prospects for more growth, and this could see more competition in the industry. While manufacturers may try and target both ends of the market, that is the ones who are brand oriented and the ones who are price sensitive, it is quite clear that people in developing countries are becoming more prices sensitive and expensive models could have a hard time selling in the future. From the analysis that was conducted, it was shown that the significant variables were: 4G connectivity, applications, synchronising with the computer, camera resolution, icloud, home screen design and format and after sale services. However, these variables are features that are frequently used by most smart phone users and are what most consumers consider when they buy a smart phone. The rapid growth of technology could also mean that cheaper models could have better features that are viewed in expensive models such as high-resolution cameras and faster internet connectivity. The null hypothesis should, therefore, not be accepted, and smart phone manufacturers should consider pricing their phones while providing the latest technology. Recommendations Since consumers are becoming more literate, they are now more aware of substitutes and are willing to maximise their utility. Phone manufacturers should come up with cheaper models that have the latest technology while still maintaining their brand. In the case the cost of manufacturing the phone is expensive, manufacturers should partner with other institutions such as telecommunication companies who can finance consumers allowing them to pay for the phone over a long period. Manufacturers could also build on the already known technology on the commonly used functions in a smart phone. This could lead to a more enhanced experience with customers who could form loyalties with the manufacture who provides unique services. Appendix Appendix 1 Statistics Do you own a smartphone, or use a smartphone supplied by your employer? What is the brand of phone you own or use most at present? How long have you been using that phone? What is your age group? Your occupation Other (please specify) N Valid 55 55 55 55 54 55 Missing 0 0 0 0 1 0 Minimum 1.00 .00 1.00 1.00 .00 Maximum 2.00 6.00 4.00 8.00 6.00 Frequency Table Do you own a smartphone, or use a smartphone supplied by your employer? Frequency Percent Valid Percent Cumulative Percent Valid Yes 52 94.5 94.5 94.5 No 3 5.5 5.5 100.0 Total 55 100.0 100.0 What is the brand of phone you own or use most at present? Frequency Percent Valid Percent Cumulative Percent Valid Other (please specify) 4 7.3 7.3 7.3 Samsung 17 30.9 30.9 38.2 HTC 4 7.3 7.3 45.5 iPhone 27 49.1 49.1 94.5 Sony Ericsson 2 3.6 3.6 98.2 LG 1 1.8 1.8 100.0 Total 55 100.0 100.0 How long have you been using that phone? Frequency Percent Valid Percent Cumulative Percent Valid less than 1 year 8 14.5 14.5 14.5 between 1 and 2 years 27 49.1 49.1 63.6 between 2 and 3 years 17 30.9 30.9 94.5 3 or more years 3 5.5 5.5 100.0 Total 55 100.0 100.0 What is your age group? Frequency Percent Valid Percent Cumulative Percent Valid Under 18 2 3.6 3.6 3.6 18 – 21 14 25.5 25.5 29.1 22 – 25 38 69.1 69.1 98.2 42 – 45 1 1.8 1.8 100.0 Total 55 100.0 100.0 Your occupation Frequency Percent Valid Percent Cumulative Percent Valid Other (please specify) 1 1.8 1.9 1.9 Student 53 96.4 98.1 100.0 Total 54 98.2 100.0 Missing System 1 1.8 Total 55 100.0 Other (please specify) Frequency Percent Valid Percent Cumulative Percent Valid 54 98.2 98.2 98.2 housewife 1 1.8 1.8 100.0 Total 55 100.0 100.0 Statistics Your occupation N Valid 54 Missing 1 Minimum .00 Maximum 6.00 Your occupation Frequency Percent Valid Percent Cumulative Percent Valid Other (please specify) 1 1.8 1.9 1.9 Student 53 96.4 98.1 100.0 Total 54 98.2 100.0 Missing System 1 1.8 Total 55 100.0 Appendix 2 Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .822a .676 .439 .97459 a. Predictors: (Constant), User friendliness, Speed, Synchronise with computer, Price, Design, Home screen design and format, Internal storage capacity, Weight, Apps, Range of accessories, Screen size, Privacy and security, After sales service, Voice clarity, Camera resolution, Brand image, iCloud, 4G connectivity, Durability (if dropped for example), Screen definition, Battery capacity, Thickness ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 59.502 22 2.705 2.848 .004a Residual 28.495 30 .950 Total 87.997 52 a. Predictors: (Constant), User friendliness, Speed, Synchronise with computer, Price, Design, Home screen design and format, Internal storage capacity, Weight, Apps, Range of accessories, Screen size, Privacy and security, After sales service, Voice clarity, Camera resolution, Brand image, iCloud, 4G connectivity, Durability (if dropped for example), Screen definition, Battery capacity, Thickness b. Dependent Variable: What is the brand of phone you own or use most at present? Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Correlations Collinearity Statistics B Std. Error Beta Zero-order Partial Part Tolerance VIF 1 (Constant) 3.320 .654 5.079 .000 Thickness .522 .366 .364 1.427 .164 -.179 .252 .148 .166 6.021 Weight -.456 .319 -.298 -1.429 .163 -.164 -.252 -.148 .249 4.021 Screen size -.213 .253 -.147 -.840 .407 -.018 -.152 -.087 .351 2.851 4G connectivity .581 .271 .421 2.145 .040 -.104 .365 .223 .281 3.565 Price .121 .254 .098 .475 .638 .215 .086 .049 .253 3.959 Battery capacity -.110 .267 -.095 -.412 .683 .209 -.075 -.043 .201 4.972 Design .058 .273 .039 .211 .834 -.001 .039 .022 .318 3.143 Internal storage capacity .019 .201 .015 .096 .924 -.165 .018 .010 .447 2.236 Apps -.602 .241 -.454 -2.498 .018 -.326 -.415 -.259 .327 3.055 Speed -.113 .222 -.079 -.509 .615 .162 -.092 -.053 .446 2.242 Synchronise with computer .555 .249 .426 2.224 .034 -.040 .376 .231 .294 3.402 iCloud -.539 .210 -.544 -2.566 .016 -.408 -.424 -.267 .240 4.170 Camera resolution -.665 .255 -.476 -2.608 .014 -.065 -.430 -.271 .324 3.083 Screen definition -.008 .252 -.007 -.030 .976 -.143 -.006 -.003 .222 4.507 Voice clarity -.295 .223 -.225 -1.325 .195 -.394 -.235 -.138 .376 2.662 Home screen design and format -.568 .211 -.424 -2.699 .011 -.290 -.442 -.280 .438 2.284 Privacy and security .377 .238 .332 1.584 .124 .141 .278 .165 .246 4.062 Durability (if dropped for example) .358 .241 .292 1.484 .148 .018 .262 .154 .279 3.590 After sales service .836 .262 .546 3.195 .003 .071 .504 .332 .370 2.706 Brand image -.113 .261 -.083 -.433 .668 -.221 -.079 -.045 .297 3.365 Range of accessories .076 .182 .072 .419 .678 -.354 .076 .044 .366 2.731 User friendliness -.304 .257 -.251 -1.185 .246 -.272 -.211 -.123 .240 4.175 a. Dependent Variable: What is the brand of phone you own or use most at present? Appendix 3 Correlations What is the brand of phone you own or use most at present? 4G connectivity Apps iCloud Screen definition Home screen design and format After sales service What is the brand of phone you own or use most at present? Pearson Correlation 1 -.104 -.326* -.408** -.143 -.290* .071 Sig. (2-tailed) .454 .015 .002 .297 .032 .608 N 55 54 55 54 55 55 55 4G connectivity Pearson Correlation -.104 1 .507** .201 .441** .384** .379** Sig. (2-tailed) .454 .000 .150 .001 .004 .005 N 54 54 54 53 54 54 54 Apps Pearson Correlation -.326* .507** 1 .434** .646** .384** .579** Sig. (2-tailed) .015 .000 .001 .000 .004 .000 N 55 54 55 54 55 55 55 iCloud Pearson Correlation -.408** .201 .434** 1 .156 .301* .222 Sig. (2-tailed) .002 .150 .001 .259 .027 .107 N 54 53 54 54 54 54 54 Screen definition Pearson Correlation -.143 .441** .646** .156 1 .196 .499** Sig. (2-tailed) .297 .001 .000 .259 .151 .000 N 55 54 55 54 55 55 55 Home screen design and format Pearson Correlation -.290* .384** .384** .301* .196 1 .430** Sig. (2-tailed) .032 .004 .004 .027 .151 .001 N 55 54 55 54 55 55 55 After sales service Pearson Correlation .071 .379** .579** .222 .499** .430** 1 Sig. (2-tailed) .608 .005 .000 .107 .000 .001 N 55 54 55 54 55 55 55 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). Appendix 4 Email Frequency Percent Valid Percent Cumulative Percent Valid Never 1 1.8 1.8 1.8 Infrequently 9 16.4 16.4 18.2 Only occasionally 12 21.8 21.8 40.0 Frequently 17 30.9 30.9 70.9 Very frequently 16 29.1 29.1 100.0 Total 55 100.0 100.0 SMS Frequency Percent Valid Percent Cumulative Percent Valid Infrequently 3 5.5 5.5 5.5 Only occasionally 10 18.2 18.2 23.6 Frequently 23 41.8 41.8 65.5 Very frequently 19 34.5 34.5 100.0 Total 55 100.0 100.0 Music Frequency Percent Valid Percent Cumulative Percent Valid Infrequently 1 1.8 1.8 1.8 Only occasionally 8 14.5 14.5 16.4 Frequently 12 21.8 21.8 38.2 Very frequently 34 61.8 61.8 100.0 Total 55 100.0 100.0 News Frequency Percent Valid Percent Cumulative Percent Valid Never 1 1.8 1.8 1.8 Infrequently 5 9.1 9.1 10.9 Only occasionally 6 10.9 10.9 21.8 Frequently 12 21.8 21.8 43.6 Very frequently 31 56.4 56.4 100.0 Total 55 100.0 100.0 Apps Frequency Percent Valid Percent Cumulative Percent Valid Infrequently 2 3.6 3.6 3.6 Only occasionally 5 9.1 9.1 12.7 Frequently 13 23.6 23.6 36.4 Very frequently 35 63.6 63.6 100.0 Total 55 100.0 100.0 Phone calls Frequency Percent Valid Percent Cumulative Percent Valid Only occasionally 12 21.8 21.8 21.8 Frequently 22 40.0 40.0 61.8 Very frequently 21 38.2 38.2 100.0 Total 55 100.0 100.0 Photo taking Frequency Percent Valid Percent Cumulative Percent Valid Infrequently 1 1.8 1.8 1.8 Only occasionally 2 3.6 3.6 5.5 Frequently 23 41.8 41.8 47.3 Very frequently 29 52.7 52.7 100.0 Total 55 100.0 100.0 Watching movies Frequency Percent Valid Percent Cumulative Percent Valid Never 9 16.4 16.4 16.4 Infrequently 5 9.1 9.1 25.5 Only occasionally 3 5.5 5.5 30.9 Frequently 18 32.7 32.7 63.6 Very frequently 20 36.4 36.4 100.0 Total 55 100.0 100.0 Document processing Frequency Percent Valid Percent Cumulative Percent Valid Never 5 9.1 9.1 9.1 Infrequently 10 18.2 18.2 27.3 Only occasionally 11 20.0 20.0 47.3 Frequently 14 25.5 25.5 72.7 Very frequently 15 27.3 27.3 100.0 Total 55 100.0 100.0 Synchronising with a computer Frequency Percent Valid Percent Cumulative Percent Valid Never 4 7.3 7.3 7.3 Infrequently 10 18.2 18.2 25.5 Only occasionally 11 20.0 20.0 45.5 Frequently 15 27.3 27.3 72.7 Very frequently 15 27.3 27.3 100.0 Total 55 100.0 100.0 iCloud Frequency Percent Valid Percent Cumulative Percent Valid Never 14 25.5 25.9 25.9 Infrequently 8 14.5 14.8 40.7 Only occasionally 6 10.9 11.1 51.9 Frequently 8 14.5 14.8 66.7 Very frequently 18 32.7 33.3 100.0 Total 54 98.2 100.0 Missing System 1 1.8 Total 55 100.0 Works Cited Tay14: , (Taylor), Olu12: , (Olumide), For13: , (Forbes), Tri14: , (Triggs), BBC14: , (BBC ), Mic14: , (Kline), Dan14: , (Danova), Lom14: , (Lomas), Com14: , (Comscore), Ray14: , (Ray), Rey13: , (Reynolds), Mar14: , (Market Watch), Read More