Recruitment | Πρόσληψη | energy transition | ενεργειακή μετάβαση | sustainability | βιωσιμότητα | human resources | ανθρώπινοι πόροι | human capital | ανθρώπινο δυναμικό | interdisciplinarity | διεπιστημονικότητα | interdisciplinarity | διαθεματικότητα | skills | δεξιότητες | employment | απασχόληση | disciplines | επιστημονικοί κλάδοι | energy sector | ενεργειακός τομέας | energy professonal | επαγγελματίας ενέργειας
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Figure 1: Areas where AI will impact recruiting (Source: LinkedIn Global Recruiting Trends 2018) 29
Figure 2: Human Resources and energy sector relationship (Source: Angheluta, Margina et al., 2014) 31
Figure 3: Leading oil and gas companies worldwide based on number of employees in 2019 (Source: Statista, 2020) 33
Figure 4: Change in the number of employees globally, 2016 vs 2014 (Source, 2018) 34
Figure 5 : Characteristics of integrated research approaches (Source: Stock & Burton, 2011) 42
Figure 6: Employment skills distribution per discipline class 50
Figure 7: Interdisciplinarity constructs and their relationship (multiplicity) 59
Figure 8: EU base for our sample 66
Figure 9: Respondent professional roles in the sample 67
Figure 10: Respondent gender and age zone 68
Figure 11: Respondent professional experience (years) 68
Figure 12: Respondent professional experience (years) 69
Figure 13: Professional roles for respondents having hired an employee 69
Figure 14: Professional experience of respondents having hired an employee 69
Figure 15: Hired employees shares (left) and when the recruiting/hiring took place (right) 70
Figure 16: Energy topics that offered vacancies in the past (responses per professional role) 72
Figure 17: Future energy topics that may offer vacancies (responses per professional role) 73
Figure 18: How long the recruiting/hiring lasted (left) & will last (right) 74
Figure 19: How long the recruiting/hiring will last (responses from recruiters and HR departments) 74
Figure 20: Recruiting pools – existing database, past (up) and future (down) 75
Figure 21: Recruiting pools – social media campaign, past (up) and future (down) 75
Figure 22: Recruiting pools – referral programme, past (up) and future (down) 76
Figure 23: Recruiting pools – other, past (up) and future (down) 76
Figure 24: Other candidate pools analysis, past (left) and future (right) 77
Figure 25: Candidate attributes - age, past (up) and future (down) 77
Figure 26: Candidate attributes - gender, past (up) and future (down) 78
Figure 27: Candidate attributes - ethnicity, past (up) and future (down) 78
Figure 28: Candidate attributes – education level, past (up) and future (down) 79
Figure 29: Candidate attributes – previous working experience, past (up) and future (down) 79
Figure 30: Candidate attributes – technical skills, past (up) and future (down) 80
Figure 31: Candidate attributes – economical skills, past (up) and future (down) 80
Figure 32: Candidate attributes – social skills, past (up) and future (down) 81
Figure 33: Candidate attributes – political skills, past (up) and future (down) 81
Figure 34: Likert scale arithmetic means and standard deviations – Past experience 82
Figure 35: Likert scale arithmetic means and standard deviations – Future experience 82
Figure 36: Number of skills per selected energy topic for our questionnaire 84
Figure 37: Discipline class shares for the skills of the selected energy topics 84
Figure 38: Discipline class shares per selected energy topic 85
Figure 39: Energy Infrastructure - Smart Grids - Distribution Networks | Importance per skill 87
Figure 40: Energy Infrastructure - Smart Grids - Distribution Networks | Importance per skill in bins 88
Figure 41: (Renewable) Technologies / Energy Sectors - Chemical (e.g. bio-fuels) | Importance per skill 89
Figure 42: (Renewable) Technologies / Energy Sectors - Chemical (e.g. bio-fuels) | Importance per skill in bins 90
Figure 43: (Renewable) Technologies / Energy Sectors - Electrical / Thermal | Importance per skill 91
Figure 44: (Renewable) Technologies / Energy Sectors - Electrical / Thermal | Importance per skill in bins 92
Figure 45: (Renewable) Technologies / Energy Sectors - Energy Conversions | Importance per skill 94
Figure 46: (Renewable) Technologies / Energy Sectors - Energy Conversions | Importance per skill in bins 95
Figure 47: Energy Efficiency | Importance per skill 96
Figure 48: Energy Efficiency | Importance per skill in bins 97
Figure 49: Energy Storage Technologies | Importance per skill 99
Figure 50: Energy Storage Technologies | Importance per skill in bins 100
Figure 51: Energy Market / Economic Model(s) | Importance per skill 101
Figure 52: Energy Market / Economic Model(s) | Importance per skill in bins 102
Figure 53: Pearson correlation for each discipline class under and between the selected energy topics 104
Figure 54: Energy Infrastructure - Smart Grids - Distribution Networks | Significant correlation between disciplines 106
Figure 55: (Renewable) Technologies / Energy Sectors - Chemical (e.g. bio-fuels) | Significant correlations between disciplines 107
Figure 56: (Renewable) Technologies / Energy Sectors - Electrical / Thermal | Significant correlations between disciplines 107
Figure 57: (Renewable) Technologies / Energy Sectors - Energy Conversions | Significant correlations between disciplines 108
Figure 58: Energy Efficiency | Significant correlations between disciplines 108
Figure 59: Energy Storage Technologies | Significant correlations between disciplines 109
Figure 60: Energy Market / Economic Model(s) | Significant correlations between disciplines 109
Figure 61: Significant correlations between disciplines of different energy topics – finding 1 110
Figure 62: Significant correlations between disciplines of different energy topics – finding 2 110
Figure 63: Significant correlations between disciplines of different energy topics – finding 3 111
Figure 64: Significant correlations between disciplines of different energy topics – finding 4 112
Figure 65:The three dimensions of the energy trilemma (Source: World Energy Council) 202
Figure 66: Renewable and non-renewable power capacity additions 2001-2016 (Source: IRENA, OECD/IEA and REN21) 203
Figure 67: Renewable installed capacity by technology, 2005-2016 (Source: IRENA, OECD/IEA and REN21) 204
Figure 68: World electricity production shares by type for 2016 (Source: Stravopodis, 2019) 205
Figure 69: Digitalisation’s potential impact on industry, transport and buildings (Source: IEA, 2017) 208
Figure 70: Traditionally electricity grid (yesterday) versus smart grid (future) (Source: Wikipedia 2019) 209
Figure 71: Digital disruption S-Curve by industry (Source: BNEF) 210
Table 1: Differences between personnel management and HRM (Source: Global Journal of HRM, 2015) 19
Table 2: Employment skills identified per discipline class 50
Table 3: Employment skills identified per energy related topic 51
Table 4: Theoretical constructs of our study 61
Table 5: Included topics and related skills for rating in our questionnaire 64
Table 6: Energy topics included in the questionnaire for past and future recruiting/hiring experience 71
Table 7: Arithmetic mean and standard deviation of the Likert scale for our questions 82
Table 8: Absolute, percentage and symmetrical change for the 6-10 rankings of the Likert scale for our questions 83
Table 9: Energy Infrastructure - Smart Grids - Distribution Networks | Mean and standard deviation per skill 86
Table 10: (Renewable) Technologies / Energy Sectors - Chemical (e.g. bio-fuels) | Mean and standard deviation per skill 89
Table 11: (Renewable) Technologies / Energy Sectors - Electrical / Thermal | Mean and standard deviation per skill 91
Table 12: (Renewable) Technologies / Energy Sectors - Energy Conversions | Mean and standard deviation per skill 93
Table 13: Energy Efficiency | Mean and standard deviation per skill 96
Table 14: Energy Storage Technologies | Mean and standard deviation per skill 98
Table 15: Energy Market / Economic Model(s) | Mean and standard deviation per skill 101
Table 16: Important correlation findings between discipline classes under and between the selected energy topics 106
Table 17: Significant correlation coefficients between skills of all selected topics 116
Table 18: Significant skill correlation coefficients per discipline 117
Table 19: Cronbach a test results for energy topic T1 and skills T1S1-T1S12 of this topic 189
Table 20: Cronbach a test results for energy topic T2 and skills T2S1-T2S5 of this topic 190
Table 21: Cronbach a test results for energy topic T3 and skills T3S1-T3S4 of this topic 191
Table 22: Cronbach a test results for energy topic T4 and skills T4S1-T4S9 of this topic 192
Table 23: Cronbach a test results for energy topic T5 and skills T5S1-T5S7 of this topic 193
Table 24: Cronbach a test results for energy topic T6 and skills T6S1-T6S9 of this topic 194
Table 25: Cronbach a test results for energy topic T7 and skills T7S1-T7S8 of this topic 195
Table 26: World electricity production by primary supply in 2016 (source IEA 2019) 200
Table 27: A comparison between centralised and distributed generation (Stravopodis 2019) 206
Management of People and Organisations, Vol. 2 – Human Resource Management”/Athanasios Mihiotis
Η ενεργειακή μετάβαση προς αποκεντρωμένες και καθαρές πηγές είναι μια πολύπλοκη διαδικασία που περιλαμβάνει πολλούς εμπλεκόμενους φορείς, στρατηγικές, εφαρμογή μέτρων, πολιτικά και ρυθμιστικά πλαίσια. Η πολυπλοκότητα αυτής της μετάβασης προκαλεί μια σειρά προκλήσεων για τη διοίκηση ανθρωπίνου δυναμικού και απαιτεί πρόσληψεις στελεχών με γνώσεις και δεξιότητες από πολλά και διαφορετικά πεδία όπως η κλιματική αλλαγη, η καινοτομία, η βιωσιμότητα - για το λόγο αυτό η ΕΕ στο πλαίσιο της «Ατζέντας δράσης για τα ευρωπαϊκά πανεπιστήμια» ανέπτυξε ένα πλαίσιο δεξιοτήτων απασχόλησης που πρέπει να έχει ο επαγγελματίας στο χώρο της ενέργειας. Ο κύριος στόχος αυτής της έρευνας είναι να εξετάσει βασικές πτυχές της διαδικασίας πρόσληψης στον ενεργειακό τομέα, όπως εργασιακή εμπειρία, δεξαμενές ταλέντων και τα πιο σημαντικά προσόντα για να επικυρώσει τη σημασία της διεπιστημονικότητας στη διαδικασία πρόσληψης. Μια βιβλιογραφική ανασκόπηση πραγματοποιήθηκε αναφορικά με την γενικότερη λειτουργία της Διοίκησης Ανθρωπίνων Πόρων και ειδικά με την διαδικασία των προσλήψεων, την ενεργειακή μετάβαση και τον αντίκτυπό της. Επιπλέον, η έννοια της διεπιστημονικότητας εξετάζεται και συζητείται κριτικά. Χρησιμοποιήθηκε ποσοτική πιθανοθεωρητική έρευνα και συσχετική ανάλυση για την αξιολόγηση της σχέσης μεταξύ διεπιστημονικότητας και πρόσληψεων στον ενεργειακό τομέα.
Energy transition towards decentralized and clean sources is a complex process which involves many stakeholders, strategies, implementation of measures, political and regulatory frameworks. The complexity of this transition causes a number of workforce challenges and requires employees with skills from a variety of areas such as climate change, innovation, sustainability - for this reason the EU under the framework of the “Action Agenda for European Universities” developed a framework of employment skills which the energy professional should acquire and demonstrate. The main aim of this research is to examine key aspects of recruiting in the energy sector like the past and future experience, candidate pools and attributes and most significantly to validate the importance of interdisciplinarity in the recruitment process. A literature review is performed on recruitment in HRM, energy transition and its impact. Moreover, the concept of interdisciplinarity is reviewed and critically discussed. A quantitative approach by means of probability sampling and correlation analysis has been employed to assess the relationship between interdisciplinarity and recruitment for the energy sector.
Οι σύγχρονες προκλήσεις αναφορικά στην πρόσληψη κατάλληλου ανθρώπινου δυναμικού στον ενεργειακό τομέα