PurposeFirms use design capability across the globe to compete and increase sales, e.g. Apple. However, the payoff from design know-how has been overlooked thus far. Academic research lags in this space despite the intersection of sales, technology and design in practice. This paper provides researchers and managers with implications of the interplay between design capability and technological market conditions to enhance a firm's sales.Design/methodology/approachFirms' capability design, and sales impact have been studied in this paper across different technological market conditions. Primary technological conditions of the industry under which firms operate are captured, which are technological intensity (TI), technological competitive intensity (TCI) and technological maturity (TM). Their interplay has been studied using panel data analysis, examining fixed and random effects.FindingsDesign is an important, interesting and non-imitable capacity that yields positive firm execution results. It provides an urgent differentiator and improves deal development. This study found that all four hypotheses are generally supported. The main finding is that, provided underlying technology is good, design significantly improves sales, but design alone cannot substitute for poor technology.Practical implicationsThe results of this study link the three technological environment conditions, namely, TI, TCI and TM with sales growth. The authors find that design can and does add to superior performance, provided technological excellence exists prior. But, in the absence of good technology, design alone will hinder performance.Originality/valueThis paper examines the effect of firm design capability on sales growth. The paper finds a positive moderating effect of TCI and TM but a negative moderating effect of TI. The researchers believe these aspects of the design have not been studied before.
This study is conducted to understand consumers’ preferences with different demographic variables on their car purchase decision based on features the car offers and the cost consciousness variables considered mainly by consumers, as suggested by previous studies on this topic. The judgmental survey method was used for this research using a structured & non-disguised questionnaire to collect the responses. The pilot survey was used to understand the instrument’s reliability and validity total of 200 respondents were contacted, but 143 responses were received. The response rate was almost 72% of the reached respondents. But, only 103 usable responses were considered for analysis as there were 40 responses found not to be a worthwhile while. 13 were inconsistent with their answers, 22 had missing values with essential questions, and the remaining 5 were outliers in their response. The shortlisted sample size (with almost 50% responses) is adequate for this type of research. Factor analysis with PCA is performed to group the variables and define the dependent variables for this study. The two dependent variables were defined from this. They are described as features of the cars and cost consciousness. Then ANOVA is used to get p-values for the regression scores of the independent demographic variables to understand the impact. The findings of this study show that none of the essential demographic variables of consumers (here gender, education, occupation and age) has shown a significant impact with features and cost consciousness as dependent variables in car purchase decisions. These findings contradict the studies done in the past. It is find from this study, that the consumers are more fashion-conscious and environmentally conscious than cost-conscious. This result may be because of the characteristics of the sample, which shows that there is no significant impact of any of the demographic variables on the car purchase decision based on the feature of the car as well as cost-consciousness factors like resale value, maintenance and fuel. The results of this study may change if the sample contains equal percentages of the consumers for all the demographic factors. The results may vary if the sample has more part-time employees and other types such as students, retired and unemployed.
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