Harram Aneeqa and Fozia M
This study is carried out to analyze the impact of customer oriented marketing capabilities on firm performance mediated by new product development capabilities. The manufacturing sector of Pakistan is selected for this study and data was collected from managers who are involved in marketing and product development decisions. The sample size for this research was 100. The results were analyzed using statistical tools. Reliability of data is measured through Cronbach’s Alpha, correlation, regression and mediating regression analysis was also assessed. The results were positive and all of the hypothesized relations were supported. This study is significant for the top level management of different organizations to improve their overall performance through marketing and new product development capabilities in order to retain their customers and to cope with ever dynamic market.
Musaev Alexander Azerovich, Anantchenko Igor Viktorovitch and Gazul Stanislav Mikhailovich
José G Vargas-Hernández and Arnulfo SM
Keran Song and Prasad VB
Two models are developed in this paper in order to discuss possible asymmetric business cycle effects on US sectoral stock returns. One is a GARCH model with asymmetric explanatory variables and the other one is an ARCH-M model with asymmetric external regressors. In the second model, square root of conditional variance of the business cycle proxy is characterized as positive or negative risk, depending on the algebraic sign of past innovations driving the business cycle proxy. This helps to capture any asymmetric effects of positive and negative business cycle risk on returns. We find that some sectors change their cyclicities from expansions to recessions. Negative shocks to business cycles have most power to influence sectoral volatilities. Positive and negative parts of business cycle risk have same effects on some sectors but have opposite effects on other sectors. A general conclusion of both models is that business cycles has stronger effects than own sectoral effects in driving sectoral returns.
Bangladesh achieved lower-middle income country’s status from the World Bank in July 2015. It’s GNI per capita raised into USD 1314 in 2014-15 FY from USD 1184 and USD 1054 in 2012-13 and 2013-14 Fiscal Years respectively. The government of Bangladesh declared Vision 2021 to be a higher middle income country by the year 2021. Therefor it has only 6 years in hand to raise GNI per capita from existing USD 1314 into USD 4126 to become a higher middle income country. There are many other challenges in front of it like; about 31.5% population is living below poverty line, there are 56.7 million workable populations in Bangladesh with 2 million unemployed populations. About 1.8 million educated workforces are entering into the job market in each year. GDP Growth rate is rotating around 6% to 6.6% during last decades, but it has to be increased into 8% to 10% to facilitate employment generation and poverty alleviation in Bangladesh. A huge amount of new investment is required to increase GDP growth and employment rate up to the desired level. There are options to increase local investment as well as go for foreign direct investment but preparations shall be taken in time. Otherwise Vision 2021 may not be achieved even by the year 2031. This is the time to compare Bangladesh’s performance in local and foreign investment attraction with its competitor countries. Current investment attraction tools using by the government of Bangladesh could be rechecked and initiate effective corrective measures as, when and where required.
Research is a continuous symbolic analysis of the inculcated thoughts in such experimental academic platform, where the researcher shall be in a reliable position to find an agreeable solution of the same within a short while. Nothing can split both the research cognition and that very person apart. Because a person does perceive regarding something through his/her envious strength then that very person does search for supportive variables to enhance the matured envision of that specific thinking in deed. Thereafter if the elements of the research are found or extensively available then the next robust movement is to frame the findings, which must have a communicable justification and the world-wide consequence for each and every individual to understand the valid eloquence of that specific content.
Critical Chain Project Management (CCPM) provided a tangible progress to the Project Management Body of Knowledge. The Critical Chain Project Management (CCPM) differs from the traditional Critical Path Method (CPM) which includes never changing resource dependencies. CCPM improves the project plan by aggregating uncertainty into buffers at the end of activity paths. In this research, one hundred twenty random projects were generated and analyzed using Microsoft Project software according to the traditional CPM and the CCPM once using the Sum of Squares (SSQ) method and another using the cut and past (C&PM) method. CCPM-SSQ method revealed an average savings of 13% and 43% in duration and cost, with a standard deviation of 21 and 11 for duration and cost respectively. While the CCPM-C&PM method revealed an average overestimation of about 2% in duration and 43% savings in cost, with a standard deviation of 25 and 11 for duration and cost respectively.
Forecasts are numerical estimates of the future levels of sales, demand, inventories, costs imports, exports and prices, among others. For a firm, industry, a sector of the economy or the aggregate economy let alone factors such as international trade and finance. The objective is to assist management in planning, budgeting, marketing efforts, materials requirements, sustainability, the refuse from power production and efforts to restrain the effects of pollution on climate change. We indicate several procedures which are relatively easier to compute, more accurate and not dependent on finding predictor (or explanatory) variables which have coefficients of correlation which are small. By using one set of data, (port demand in units), we do short-term forecasting of up to twelve periods which are less data dependent and produce forecasts which do not require a huge investment in computer and personnel time.