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Building upon the insights of the resource-based view and internationalization theories, the purpose of this paper is to examine the role networks play in SMEs' readiness for internationalization. By investigating three different types of knowledge sharing, namely economic-setting, market-specific and customer-specific, the study analyzes their effect on SMEs' readiness for internationalization.

The four research hypotheses derived by from the analysis of the literature have been investigated by applying the multiple regression technique. By means of an online survey, 300 valid questionnaires were collected and information from a sample of Italian SMEs belonging to 11 agro-food consortia have been analyzed.

The results suggest that SMEs' readiness for internationalization could be supported by sharing customer-specific, market-specific and economic-setting knowledge with other firms operating within the same agro-food consortium. Additionally, data analysis highlights a negative relation between the risk perception in the process and readiness for internationalization, suggesting the importance of knowledge sharing in reducing the criticality issues of being a newcomer entering international markets.

From a theoretical perspective, this study aims to fill the gap in knowledge management and international relationship marketing literature. Since proposes a combination of different kinds of knowledge that contribute to reducing the criticalities SMEs must face by identifying useful information to be conveyed within the network. From a managerial perspective, the study provides useful insights for the agro-food sector, highlighting how experiential and network knowledge constitutes a pre-condition for managing internationalization complexity and discovering opportunities on foreign markets.

Recent legislation resulted in an elevation of operating and support (O&S) costs’ relative importance for decision-making in Department of Defense programs. However, a lack of research in O&S hinders a cost analyst’s abilities to provide accurate sustainment estimates. Thus, the purpose of this paper is to investigate when Air Force aircraft O&S costs stabilize and to what degree. Next, a parametric O&S model is developed to predict median O&S costs for use as a new tool for cost analyst practitioners.

Utilizing the Air Force total ownership cost database, 44 programs consisting of 765 observations from 1996 to 2016 are analyzed. First, stability is examined in three areas: total O&S costs, the six O&S cost element structures and by aircraft type. Next, stepwise regression is used to predict median O&S costs per total active inventory (CPTAI) and identify influential variables.

Stability results vary by category but generally are found to occur approximately five years from initial operating capability. The regression model explains 89.01 per cent of the variance in the data set when predicting median O&S CPTAI. Aircraft type, location of lead logistics center and unit cost are the three largest contributing factors.

Results from this research provide insight to cost analysts on when to start using actual O&S costs as a baseline for estimates in lieu of analogous cost program data and also derives a new parametric O&S estimating tool designed as a cross-check to current estimating methodologies.

The introduction of “should cost” in 2011 required all Major Defense Acquisition Programs (MDAP) to create efficiencies and improvements to reduce a program’s “will-cost” estimate. Realistic “will-cost” estimates are a necessary condition for the “should cost” analysis to be effectively implemented. Owing to the inherent difficulties in establishing a program’s will-cost estimate, this paper aims to propose a new model to infuse realism into this estimate.

Using historical data from 73 Departments of Defense programs as recorded in the selected acquisition reports (SARs), the analysis uses mixed stepwise regression to predict a program’s cost from Milestone B (MS B) to initial operational capability (IOC).

The presented model explains 83 per cent of the variation in the program acquisition cost. Significant predictor variables include: projected duration (months from MS B to IOC); the amount of research development test and evaluation (RDT&E) funding spent at the start of MS B; whether the program is considered a fixed-wing aircraft; whether a program is considered an electronic system program; whether a program is considered ACAT I at MS B; and the program size relative to the total program’s projected acquisition costs at MS B.

The model supports the “will-cost and should-cost” requirement levied in 2011 by providing an objective and defensible cost for what a program should actually cost based on what has been achieved in the past. A quality will-cost estimate provides a starting point for program managers to examine processes and find efficiencies that lead to reduced program costs.

Aircraft availability (AA) is a key metric for assessing operational readiness. The declining trend in AA is a documented concern for senior Air Force leaders. This paper aims to investigate the components of non-available time and subsequently focuses on the largest and fastest growing category: not mission capable maintenance unscheduled (NMCMU). Then, utilization of aircraft platforms is examined to determine the readiness benefits of increasing available hours.

Stepwise regression is conducted on a data set of 30 aircraft platforms, consisting of 542 observations from 1998 to 2017, to reveal drivers of NMCMU. Next, utilization of aircraft platforms is examined through regression and correlation analysis of aircraft platforms and sorties or hours flown.

Regression analysis reveals drivers of NMCMU include platform type, average age of aircraft, fleet size, breaks and cannibalization. These factors explain 80.6 per cent of the variance in the data set when predicting NMCMU. Additionally, the utilization results show that when more hours are made available, 5 per cent of each new hour is used for flying. Further analysis at the individual platform level finds a strong or moderate correlation between available hours and sorties flown for 93 per cent of the platforms.

Implications from the regression analysis demonstrate there are remedies to increase AA, but many of these remedies may be costly. The utilization analysis expresses the potential readiness benefits of increasing available hours.