7. The following statements concern Strategic Flexibility for your organization Please indicate to what extent the statements do not apply to your firm at all
8.2 Survey in Dutch
1. Wat is uw geboortedatum?
2. Hoe lang werkt u voor dit bedrijf? 3. Wat is uw geslacht?
4. Wat is uw hoogst genoten opleidingsniveau? 5. Hoeveel hectare heeft uw bedrijf?
6. De volgende 5 stellingen gaan over de IT vaardigheden van uw bedrijf. Geef alstublieft aan tot in hoeverre ze helemaal niet van toepassing zijn op uw bedrijf (1) of juist wel heel erg van toepassing zijn(5).
a. Effecten ten gevolge van IT zijn meegenomen in onze bedrijfsstrategie. b. Wij kunnen zeer nauwkeurig de potentie van IT analyseren ter
verbetering van onze concurrentiepositie.
c. Het effect van IT op onze bedrijfsstrategie is goed begrepen.
d. Het afstemmen tussen bedrijfsstrategie en IT-strategie is niet behaald. e. We hebben verschillende prioriteiten gesteld voor IT projecten in onze
IT strategie.
7. The volgende 20 stellingen gaan over de volwassenheid van IT binnen uw bedrijf. Geef alstublieft aan tot in hoeverre deze stellingen helemaal niet (1) van toepassing zijn op uw bedrijf of juist wel heel erg (5) van toepassing zijn.
a. Onze IT projecten ondersteunen de doelstellingen en strategieën van ons bedrijf.
b. We zijn voortdurend op zoek naar innovatieve mogelijkheden in IT die voor concurrentievoordeel kunnen zorgen.
c. We zijn adequaat geïnformeerd over het huidig IT gebruik door de concurrentie (inkopers, leveranciers en andere concurrenten) in onze industrie.
d. We zijn adequaat geïnformeerd over de mogelijkheden in gebruik van IT door onze concurrentie (inkopers, leveranciers en andere
concurrenten) in onze industrie.
e. We hebben een adequaat beeld van dekking en kwaliteit van onze IT systemen.
f. We zijn tevreden met hoe de prioriteiten van onze IT projecten zijn gesteld.
g. Het is duidelijk wie er binnen uw organisatie de
eindverantwoordelijkheid en de beslissingsbevoegdheid rond IT management en ontwikkeling heeft.
h. Het is duidelijk wie binnen uw organisatie de
eindverantwoordelijkheid en de beslissingsbevoegdheid rond IT activiteiten heeft.
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k. Onze IT functie is duidelijk over doelen en verantwoordelijkheden. l. Onze IT functie is duidelijk over de prestatiecriteria.
m. Ideeën van gebruikers krijgen voldoende aandacht binnen de organisatie met betrekking tot IT planning en implementatie. n. Onze IT specialisten begrijpen de industrie en ons bedrijf. o. De structuur van onze IT functie past bij ons bedrijf.
p. De relatie tussen de IT specialist en de gebruiker is opbouwend. q. Het top management binnen mijn bedrijf beschouwt het toekomstige
IT gebruik van strategisch belang.
r. Er is een top-down planningsproces voor het verbinden van de strategie van informatiesystemen met de behoeften van het bedrijf. s. Sommige IT-ontwikkelingen vinden plaats vanuit de business unit. t. De introductie van, of het experimenteren met, nieuwe technologieën
vind plaats op business unit niveau onder de verantwoordelijkheid van het business unit.
8. De volgende 5 stellingen gaan over de strategische flexibiliteit van uw bedrijf. Geef alstublieft aan tot in hoeverre ze helemaal niet van toepassing zijn op uw bedrijf (1) of juist wel heel erg van toepassing zijn(5).
a. We delen regelmatig informatie en kosten tussen verschillende bedrijfsactiviteiten.
b. We veranderen regelmatig van strategie en structuur om voordeel te halen uit veranderingen die plaatsvinden in de externe omgeving. c. Onze strategie legt de nadruk op het benutten van nieuwe
mogelijkheden die voortvloeien uit de externe omgeving.
d. Onze strategie weerspiegelt een hoge mate van flexibiliteit bij het managen van politieke, economische en financiële risico’s.
e. Onze strategie benadrukt de veelzijdigheid en kracht van het toewijzen van human resources.
9. De volgende 3 stellingen gaat over de prestaties van uw bedrijf. Geef alstublieft aan tot in hoeverre ze helemaal niet van toepassing zijn op uw bedrijf (1) of juist wel heel erg van toepassing zijn(5).
a. Ons bedrijf voldoet regelmatig aan, of overtreft, de financiële doelstellingen.
b. Ons bedrijf is zeer winstgevend.
c. Ons bedrijf voldoet regelmatig aan, of overtreft, de verkoopdoelstellingen.
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84 Y = Firm_Pf X = IT_Cap_4 M = S_Flex Statistical Controls: CONTROL= Q5 Q6 Sample size 102 ********************************************************************* Outcome: Firm_Pf Model Summary R R-sq MSE F df1 df2 p .3625 .1314 .2112 2.3030 5.0000 96.0000 .0505 Model
coeff se t p LLCI ULCI
constant 2.7631 .2540 10.8778 .0000 2.2589 3.2674 S_Flex .0830 .0725 1.1450 .2550 -.0609 .2269 IT_Cap_4 .0790 .0836 .9451 .3470 -.0869 .2449 int_1 -.1427 .0700 -2.0388 .0442 -.2816 -.0038 Q5 -.0395 .0373 -1.0587 .2924 -.1135 .0345 Q6 .0276 .0302 .9121 .3640 -.0324 .0876 Product terms key:
int_1 IT_Cap_4 X S_Flex R-square increase due to interaction(s): R2-chng F df1 df2 p
int_1 .0322 4.1568 1.0000 96.0000 .0442
********************************************************************* Conditional effect of X on Y at values of the moderator(s):
S_Flex Effect se t p LLCI ULCI -.6833 .1765 .0775 2.2771 .0250 .0226 .3303 .0000 .0790 .0836 .9451 .3470 -.0869 .2449 .6833 -.0185 .1120 -.1651 .8692 -.2408 .2038
Values for quantitative moderators are the mean and plus/minus one SD from mean. Values for dichotomous moderators are the two values of the moderator.
********************* JOHNSON-NEYMAN TECHNIQUE **************** Moderator value(s) defining Johnson-Neyman significance region(s)
Value % below % above -.5079 13.7255 86.2745
Conditional effect of X on Y at values of the moderator (M) S_Flex Effect se t p LLCI ULCI
85 -1.5794 .3044 .1080 2.8194 .0058 .0901 .5186 -1.4294 .2829 .1008 2.8071 .0061 .0829 .4830 -1.2794 .2615 .0943 2.7744 .0066 .0744 .4487 -1.1294 .2401 .0885 2.7130 .0079 .0644 .4158 -.9794 .2187 .0837 2.6139 .0104 .0526 .3849 -.8294 .1973 .0799 2.4684 .0153 .0386 .3560 -.6794 .1759 .0775 2.2714 .0254 .0222 .3297 -.5294 .1545 .0763 2.0241 .0457 .0030 .3061 -.5079 .1515 .0763 1.9850 .0500 .0000 .3029 -.3794 .1331 .0767 1.7364 .0857 -.0191 .2853 -.2294 .1117 .0784 1.4249 .1574 -.0439 .2674 -.0794 .0903 .0815 1.1086 .2704 -.0714 .2521 .0706 .0689 .0857 .8040 .4234 -.1012 .2391 .2206 .0475 .0910 .5223 .6027 -.1331 .2282 .3706 .0261 .0971 .2690 .7885 -.1667 .2189 .5206 .0047 .1039 .0454 .9639 -.2016 .2110 .6706 -.0167 .1113 -.1498 .8812 -.2377 .2043 .8206 -.0381 .1192 -.3195 .7501 -.2747 .1985 .9706 -.0595 .1274 -.4667 .6417 -.3125 .1935 ********************************************************************* Data for visualizing conditional effect of X on Y
Paste text below into a SPSS syntax window and execute to produce plot. DATA LIST FREE/IT_Cap_4 S_Flex Firm_Pf.
BEGIN DATA. -.6855 -.6833 2.4702 .0000 -.6833 2.5912 .6855 -.6833 2.7122 -.6855 .0000 2.5938 .0000 .0000 2.6479 .6855 .0000 2.7021 -.6855 .6833 2.7173 .0000 .6833 2.7046 .6855 .6833 2.6920 END DATA.
GRAPH/SCATTERPLOT=IT_Cap_4 WITH Firm_Pf BY S_Flex. * Estimates are based on setting covariates to their sample means.
******************** ANALYSIS NOTES AND WARNINGS *************** Level of confidence for all confidence intervals in output:
95.00
NOTE: The following variables were mean centered prior to analysis: IT_Cap_4 S_Flex
86 Model = 1 Y = Firm_Pf X = IT_mat M = S_Flex Sample size 98 ************************************************************* Outcome: Firm_Pf Model Summary R R-sq MSE F df1 df2 p .2968 .0881 .2221 1.9935 3.0000 94.0000 .1203 Model
coeff se t p LLCI ULCI
constant 2.6120 .0498 52.4024 .0000 2.5130 2.7109 S_Flex .0959 .0761 1.2598 .2108 -.0552 .2470 IT_mat .1987 .1166 1.7039 .0917 -.0328 .4302 int_1 -.0210 .1779 -.1179 .9064 -.3743 .3323 Product terms key:
int_1 IT_mat X S_Flex
R-square increase due to interaction(s): R2-chng F df1 df2 p
int_1 .0003 .0139 1.0000 94.0000 .9064
************************************************************* Conditional effect of X on Y at values of the moderator(s):
S_Flex Effect se t p LLCI ULCI -.6900 .2131 .1904 1.1193 .2658 -.1649 .5912 .0000 .1987 .1166 1.7039 .0917 -.0328 .4302 .6900 .1842 .1452 1.2687 .2077 -.1041 .4725
Values for quantitative moderators are the mean and plus/minus one SD from mean. Values for dichotomous moderators are the two values of the moderator.
***********JOHNSON-NEYMAN TECHNIQUE *********** There are no statistical significance transition points within the observed range of the moderator.
********************************************************************* Data for visualizing conditional effect of X on Y
Paste text below into a SPSS syntax window and execute to produce plot. DATA LIST FREE/IT_mat S_Flex Firm_Pf.
BEGIN DATA.
-.4824 -.6900 2.4430 .0000 -.6900 2.5458
87 .4824 .0000 2.7078 -.4824 .6900 2.5893 .0000 .6900 2.6781 .4824 .6900 2.7670 END DATA.
GRAPH/SCATTERPLOT=IT_mat WITH Firm_Pf BY S_Flex.
******************** ANALYSIS NOTES AND WARNINGS *************** Level of confidence for all confidence intervals in output:
95.00
NOTE: The following variables were mean centered prior to analysis: IT_mat S_Flex
NOTE: Some cases were deleted due to missing data. The number of such cases was:4 NOTE: All standard errors for continuous outcome models are based on the HC3 estimator --- END MATRIX ---
8.3.5 Tables and plots concerning H3 – mediation
Model = 4 Y = Firm_Pf X = IT_Cap_4 M = S_Flex Statistical Controls: CONTROL= Q5 Q6 Sample size = 102 ********************************************************************* Outcome: S_Flex Model Summary R R-sq MSE F df1 df2 p .4184 .1750 .3969 6.9316 3.0000 98.0000 .0003 Model
coeff se t p LLCI ULCI
constant 2.3178 .4152 5.5820 .0000 1.4938 3.1418 IT_Cap_4 .3785 .0957 3.9531 .0001 .1885 .5685 Q5 .0359 .0555 .6475 .5188 -.0742 .1460 Q6 .0276 .0397 .6971 .4874 -.0511 .1064 ********************************************************************* Outcome: Firm_Pf Model Summary R R-sq MSE F df1 df2 p
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coeff se t p LLCI ULCI
constant 1.7302 .3523 4.9116 .0000 1.0311 2.4294 S_Flex .1185 .0747 1.5878 .1156 -.0296 .2667 IT_Cap_4 .1261 .0762 1.6556 .1010 -.0251 .2773 Q5 -.0284 .0411 -.6919 .4907 -.1099 .0531 Q6 .0267 .0294 .9101 .3650 -.0316 .0851
******************** DIRECT AND INDIRECT EFFECTS ***************** Direct effect of X on Y
Effect SE t p LLCI ULCI .1261 .0762 1.6556 .1010 -.0251 .2773 Indirect effect of X on Y
Effect Boot SE BootLLCI BootULCI S_Flex .0449 .0350 -.0041 .1385
******************** ANALYSIS NOTES AND WARNINGS *************** Number of bootstrap samples for bias corrected bootstrap confidence intervals: 5000
Level of confidence for all confidence intervals in output: 95.00
--- END MATRIX ---
8.3.6 Tables and plots concerning H3a – mediation
Model = 4 Y = Firm_Pf X = IT_mat M = S_Flex Statistical Controls: CONTROL= Q5 Q6 Sample size = 98 ****************************************************** Outcome: S_Flex Model Summary R R-sq MSE F df1 df2 p .4564 .2083 .3890 6.8430 3.0000 94.0000 .0003 Model
coeff se t p LLCI ULCI
constant 1.4028 .6096 2.3012 .0236 .1924 2.6132 IT_mat .6037 .1673 3.6077 .0005 .2715 .9360 Q5 .0297 .0575 .5170 .6064 -.0844 .1439 Q6 .0206 .0463 .4458 .6568 -.0713 .1126 ************************************************************ Outcome: Firm_Pf Model Summary R R-sq MSE F df1 df2 p
89 constant 1.4897 .6185 2.4086 .0180 .2615 2.7179 S_Flex .0997 .0810 1.2311 .2214 -.0611 .2606 IT_mat .1998 .1272 1.5715 .1195 -.0527 .4524 Q5 -.0294 .0379 -.7745 .4406 -.1047 .0460 Q6 .0259 .0318 .8120 .4189 -.0374 .0891 ************************** TOTAL EFFECT MODEL ******* Outcome: Firm_Pf
Model Summary
R R-sq MSE F df1 df2 p
.2877 .0828 .2234 1.7679 3.0000 94.0000 .1586 Model
coeff se t p LLCI ULCI
constant 1.6296 .5700 2.8592 .0052 .4979 2.7613 IT_mat .2601 .1360 1.9121 .0589 -.0100 .5301 Q5 -.0264 .0380 -.6955 .4885 -.1019 .0490 Q6 .0279 .0307 .9084 .3660 -.0331 .0889
***************** TOTAL, DIRECT, AND INDIRECT EFFECTS **** Total effect of X on Y
Effect SE t p LLCI ULCI .2601 .1360 1.9121 .0589 -.0100 .5301 Direct effect of X on Y
Effect SE t p LLCI ULCI .1998 .1272 1.5715 .1195 -.0527 .4524 Indirect effect of X on Y
Effect Boot SE BootLLCI BootULCI S_Flex .0602 .0524 -.0193 .1899
******************** ANALYSIS NOTES AND WARNINGS ***
Number of bootstrap samples for bias corrected bootstrap confidence intervals: 5000
Level of confidence for all confidence intervals in output: 95.00
NOTE: Some cases were deleted due to missing data. The number of such cases was: 4
NOTE: All standard errors for continuous outcome models are based on the HC3 estimator --- END MATRIX ---
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most important trends in the agricultural industry. This information is meant to provide background information that is relevant to the societal link of this paper.
i. Growth of the world population
The current global food system is not meeting the world's dietary needs. (De Schutter, 2011) About one billion people are hungry, while two billion people are overweight. (Patel, 2008) This contradiction is complex but requires a solution. (Assiouras et al, 2013) Besides these numbers, it is impossible to ignore the growing world population. In 2050 the population will grow to 9 billion people, 36% more that the world
currently inhabits. (FAO, 2009) These developments are posing serious threats for the environment, overall health, global safety and political stability.
A threefold challenge now faces the world: match the rapidly changing demand for food from a larger and more affluent population to its supply; do so in ways that are environmentally and socially sustainable; and ensure that the world’s poorest people are no longer hungry. (Von Braun, 2007) A multifaceted and linked global strategy is needed to ensure sustainable and equitable food security, in order to make an attempt to feed the population and solve the problems in the global food system. (Godfray et al, 2010) Adjacent topics in these global issues are sustainability, climate change, profit driven firms, and political stability.
ii. Automation & Digitalization
According to dr. ir. S.J.C. Janssen (WUR/LEI, 2016), Big Data Technology represents a disruptive innovation that market orientated organisations will use to drive
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offer insights one maybe would not have thought about it without the gathered data. Besides the opportunities Big Data can bring an organisation, there are also risks involved. Regulations are behind which led to issues concerning ownership and privacy. To secure value from Big Data Technologies, organisations need a holistic and strategic plan for identifying opportunities and overcoming hurdles and managing risks. (Janssen, 2016)
The WUR provided Big Data timeline to show one the one hand use of data has been of all ages, and on the other hand, the development of gathering and use of all the possible data is different than 20 years ago. At the WUR/LEI they have followed these new applications for data very cautiously. According to Dr.ir. MW den Besten and dr.ir. GJ Steeneveld (WUR, 2016) the most important difference in applying data nowadays is the combination of multidisciplinary domains and the levels of detail that can help solving different but adjacent problems, like for example food safety and sustainability.
According to the researchers from the WUR/LEI, Big Data is playing a role at three levels. On micro-level is Big Data helping to understand and predict the behavior of micro-organisms in the food chain. Secondly, at meso level, big data provides a view on local environmental factors, such as fluctuations in air temperatures. Finally, at the macro level, big data predicts the behaviour of the international consumers and manufacturers on a global scale. The data scientists with the right combined data can detect any global issues. By analysing the macro level, it could show how end-users are putting the products to use. By knowing this information new strategies can be
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advantage of the Dutch agricultural industry, as well as the global food problems. You cannot manage what you are not measuring, is a known management saying. For exactly that reason is the Big Data trend so inspiring and promising for the industry.
iii. Sustainability & Avoidance of waste
Sustainability is one of the key topics for international governance for the future since the growing world population has different needs the society can offer right now. Besides the macro level problems, also on a smaller scale it can be stated that society has a general increased interest in sustainable solutions for different industries. Even though there are numerous definitions of sustainability or a sustainable society, the three core pillars that are repeatedly included are: (1) economic development, (2) social development and (3) environmental protection (Opp & Saunders 2013. An additional fourth pillar has been appointed which focusses on cultural preservation (Nurse, 2006).
Even though it impossible to feed society without it, agriculture is extremely polluting for the global environment according to the FAO summary report on World
Agriculture 2015 – 2030 (published in 2002). Agriculture is the accounts for the major share of human use of land. Pasture and crops alone
took up 37% of the earth’s land area in 1999. Over two-thirds of human water use is for agriculture. Besides these numbers – which also indicate the importance of agriculture – there are several reasons that are worsening the state of the global environment even more. Examples are: use of fertilizers and pesticides, pollution of
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The global awareness is showing society the current situation of the environmental impact of our agricultural system. This both affects and informs firms, consumers, policy makers and many more stakeholders when making decisions on how to deal with food. There are countless initiatives based on cutting down food waste. There is a lot to win. According to the Rabobank 33% of the wasted food happens at the homes of the consumers. In the European Union, there is food wasted that is valued over 30 billion euro’s every year. The effects are threefold: consumers are spending too much money on food that is thrown away; the food chain is wasting money on food that is processed and distributed but not eaten; and there are still too many people without any food at all. According to the Rabobank is it hard to find one best solution since every food chain comes with its own challenges. However, the main solution for this global issue is investing is providing the right networks and innovation in big data and technological applications. (Rabobank, 2016)